The FET was used to tune the work current in the micro-LED. Meanwhile, the transparent electrode of the micro-LED was also made of graphene. The operation of the device was demonstrated in room temperature conditions. This research opens the gateway to a new field where other two-dimensional (2D) materials can be used as FET channel materials to further improve transfer properties. The 2D materials can in principle be grown directly onto GaN, which is reproducible and scalable. Also, considering the outstanding properties and versatility of 2D materials, it is possible to envision fully transparent micro-LED displays with transfer-free active matrices (AM), alongside an efficient thermal management solution.Micro-light-emitting diodes (micro-LEDs) are the key to next-generation display technology. However, since the driving circuits are typically composed of Si devices, numerous micro-LED pixels must be transferred from their GaN substrate to bond with the Si field-effect transistors (FETs). This process is called massive transfer, which is arguably the largest obstacle preventing the commercialization of micro-LEDs. We combined GaN devices with emerging graphene transistors and for the first-time designed, fabricated, and measured a monolithic integrated device composed of a GaN micro-LED and a graphene FET connected in series. The  They represent the development trend of the next generation of displays by virtue of their low power, weather fastness, and super-high resolution. Currently, micro-LEDs constitute a new research focus in major university labs and in the semiconductor industry. Nevertheless, as it is still being developed, the technology has not yet been commercialized. One of the major bottlenecks is the “massive transfer” [With the rapid development of modern intelligent devices, the demand for power reduction and resolution enhancement of electronic displays has become higher and higher. Light-emitting diodes, or micro-LEDs, have a single-pixel area ≤ 2.5 × 10ransfer” ,5 and hiransfer” ,7. Howevp-GaN capping layer of LEDs [Graphene, a monolayer of graphite, is known as a new wonder material. It integrates many outstanding properties into one material  and hence, has a great potential in nanoelectronics . It can  of LEDs ,12.p-GaN, making the lighting more uniform, while the field effect in the graphene can be used to tune the current in the integrated device. This technique is intrinsically scalable and compatible with the semiconductor industry. Although the present study uses graphene grown ex situ on a Cu foil, the method can be extended to graphene grown in situ on GaN [2 with higher transistor on–off ratios [In this paper, we have designed and fabricated GaN micro-LEDs with graphene transparent electrodes and graphene field-effect driving transistors (GFET). To the best of our knowledge, this is the first proof-of-principle study on the monolithic integration of GaN micro-LEDs with their AM driving GFET. The role of graphene is two-fold: it acts as a transparent conducting film for the micro-LEDs and also as the channel material for the driving FETs to control the micro-LEDs. We demonstrate that graphene grown by chemical vapor deposition (CVD) can effectively spread the current in the u on GaN . Also, cf ratios , this ren-GaN, 74 nm n-AlGaN, 131.75 nm multiple quantum well , 40.62 nm electron-blocking layer (six periods of AlGaN/GaN = 3.61 nm/3.16 nm superlattice), and 92 nm p-GaN. The device fabrication process flow was as follows. First, mesa areas of the GaN micro-LEDs were defined by lift-off photolithography carried out in 2 nm Pt with 110 nm Ni sputtered onto the GaN LED wafer. The Pt/Ni layer was on the GaN mesa top, where Pt was used to improve the electrical contact between the transparent electrode and p-GaN, and Ni was used as the hard mask in the dry-etching. Inductively coupled plasma (ICP) etching was used to etch the semiconductor down to the n-GaN (1.25 μm deep) using a SiH4/Cl2 gas mixture. The Ni mask was then wet-etched away. At 300 °C, plasma-enhanced CVD (PECVD) was used to grow 300 nm SiO2 onto the sample as an insulation layer between the micro-LED and the graphene transistor. The patterning of this SiO2 layer was achieved by using buffered oxide etch (BOE). The buried back gate of the transistor was fabricated by another lift-off lithography with a sputtered 15 nm Ti/100 nm Au metal layer, as shown in 2, followed by BOE patterning to bury the gate. This layer of silicon dioxide served as the gate dielectric. Afterwards, a third lift-off lithography was used to pattern the sputtered 15 nm Ti/130 nm Au in order to form the p, n metal electrodes of the micro-LED, as well as the source and drain of the GFET. The p-pad of the micro-LED was connected to the source of the transistor. Finally, the graphene was grown by standard CVD on Cu foil using CH4 as the precursor . After the graphene CVD, the Cu foil was etched off, and the graphene transferred to the GaN wafer using standard graphene transfer technology [pn junction inserted with multiple quantum wells. When the injected current was large enough, spontaneous emission of photons was initiated by the recombination of electron–hole pairs. The GFET was connected with the micro-LED in series. Its gate capacitively coupled to the graphene channel. The Fermi energy in the channel was tuned by controlling the gate voltage, which was then translated into the modulation of the channel current Id. In this way, the current flowing into the micro LED and thus the electroluminescence could be adjusted.The inset of p electrode of the micro-LED was connected to the source pad of the transistor. The S, D metal pads of the GFET were sitting on top of the double-layer SiO2 film. It can be seen that the G pad was a little blurred, because the gate was buried in between the two SiO2 layers, as shown in −1 and ∼2690 cm−1 are signatures of sp2 hybridized graphitic carbon. The IG/I2D ratio was approximately 1/2, and the full width at half maximum (FWHM) of the 2D peak was ∼37.92 cm−1, indicating the graphene was a monolayer [onolayer . The D pVd) was swept between the source and drain, while the gate voltage Vg was altered in steps . In p-type. When Vd was fixed at 0.1 V and the gate voltage Vg was swept between −40 V and 40 V, the transfer curve shown in Vg > 40 V, which means the graphene was far from intrinsic (heavy p-doping). This can primarily be ascribed to the photolithography procedure, where the photoresist residue is known to dope the graphene severely [2O, which also reduces the carrier mobility. Using a formula based on a capacitor model:μ, Cg, L, W are the carrier mobility, gate capacitance, gate length, and gate width, respectively, the hole mobility in the GFET was calculated to be 696 cm2·V−1·s−1 . We note that this is the field-effect mobility, and the calculation depends on many device parameters, e.g., interface trap density. Furthermore, we used a simplified model without considering the quantum capacitance. The standard Hall measurement gave a more accurate estimation of graphene mobility on polyethylene terephthalate (PET) substrate, showing 1042 cm2·V−1·s−1. The transconductance gm of the GFET could be directly calculated from Vg = 25 V, the transconductance reached its maximum, which was about 0.067 mS/mm . The sheet resistance of the monolayer CVD graphene was approximately 4 kΩ/□.First, we measured discrete components’ performances. The static (DC) properties of the GFET were characterized at room temperature without special treatment, e.g., vacuum annealing. severely . Unfortu−5 or 10−6 Ωcm2 [−7 Ωcm2 or lower [c = 1 × 10−6 Ωcm2 [Graphene is a semimetal with no bandgap. Thus, graphene–metal contacts are generally very good. The specific contact resistivity for typical graphene–metal contacts is 100−6 Ωcm2 , with soor lower . For our0−6 Ωcm2 . The tot0−6 Ωcm2 .p-GaN. The forward differential resistance (series resistance) of the micro-LED was estimated to be as large as 190 Ω, which was also due to the nonoptimal graphene–GaN contact. The 2 nm Pt layer can bridge the Fermi levels of the graphene and p-GaN, improving the Ohmic contact to some extent. Currently, there is no stable and mature doping technology for graphene. In the future, if effective and stable doping can be realized, the graphene work function could be greatly improved, and the turn-on voltage would ultimately be reduced. At 8 V forward voltage VLED, the work current Id was about 10.5 mA, which is large enough for a micro-LED. The insert of p-GaN, the graphene transparent electrode helped spread the current effectively along the p-GaN mesa surface, solving the current crowding problem. The high transmittance of graphene ensured the light was effectively emitted to the external world. VDD of 0–9 V was swept on the drain electrode of the GFET, with the n electrode of the micro-LED grounded, as shown in the inset of Vg is shown at −40 V, 0 V, 20 V, and 40 V. In this figure, while tuning Vg, the current Id showed some changes when the VDD was relatively large. The current reached its maximum when Vg was −40 V and reached its minimum at Vg = 40 V. When VDD was 9 V, the difference between Imax and Imin was approximately 3.5 mA. This confirmed that the GFET can indeed be used to control the micro-LED in an AM fashion. VDD was set to be 8 V,. The device current Id (work current of both the micro-LED and the GFET) was plotted against the voltage on the transistor source, Vs, which is also the voltage on the p pad of the micro-LED. Clearly, when Vg was tuned from negative to positive, the voltage of the crossover point, Vs, was reduced, and the current Id was reduced correspondingly. This is because the graphene channel resistance increases when the gate voltage becomes more positive, due to the hole conduction mechanism. The voltage drop across the GFET (VDD–Vs) in the series circuit will therefore increase, leading to a reduction of voltage on the micro-LED. As the I–V relation of the micro-LED is exponential, a small decrease in the voltage on the p electrode, Vs, will result in a big decrease in the work current Id. This notwithstanding, compared to micro-LEDs driven by MOSFETs [Vs cannot be < 5.8 V). This can be attributed to the process-deteriorated graphene quality (mainly mobility). Furthermore, graphene is gapless, which means the GFET has a low on–off ratio. Also, the buried gate was very far from the channel, and the gate dielectric was not high-k material (300 nm SiO2). In the future, the channel material of the AM driving transistors can consist of other emerging 2D materials that have energy bandgaps, such as MoS2 and other transition-metal dichalcogenides (TMDs). With the technology of 2D materials becoming more and more developed, mobility, and consequently the transconductance of the FET channel, can be much improved. The size and aspect ratio of the gate can be optimized, and the gate dielectric can be replaced by high-k insulators [Finally, we examined the properties of the GaN micro-LED/GFET as an integrated device. sulators  to furthThe integrated devices reported in this paper and in reference  can bothIn this paper, we have designed, fabricated, and characterized a monolithic integrated device composed of a GaN micro-LED and a driving element, namely, a graphene transistor. Meanwhile, the graphene on the GaN mesa served as the transparent electrode for the micro-LED. This is the first demonstration of combining GaN micro-LEDs with AM GFETs to bypass the technical difficulty of massive transfer, which exists currently in the micro-LED community. By tuning the gate voltage of the GFET, the micro-LED current and the light intensity could be adjusted. The performance of the integrated device needs further improvement. With research booming on 2D materials other than graphene, we believe this research opens the gateway for combining traditional bulk semiconductors with ultrathin 2D materials in a bid to improve micro-LED applications. The 2D materials can be grown in situ onto GaN to achieve better process reproducibility. Furthermore, since 2D materials such as graphene often have very high thermal conductivity, the heat spreading effect should not be overlooked. This is especially important for micro-LED displays, where the pixel density is super-high, and an appropriate thermal management solution is a must. The thermal aspect of the monolithic integrated device is a subject for future work. Additionally, as 2D materials are atomically thin and transparent, as long as the metal electrodes are replaced by transparent conductors, e.g., ITO, the whole display can be made transparent, which is well in line with the future trend of development.


A universal increase in energy consumption and the dependency on fossil fuels have resulted in increasing severity of global warming, thus necessitating the search of new and environment-friendly energy sources. Hydrogen is as one of the energy sources that can resolve the abovementioned problems. Water splitting promotes ecofriendly hydrogen production without the formation of any greenhouse gas. The most common process for hydrogen production is electrolysis, wherein water molecules are separated into hydrogen and oxygen through electrochemical reactions. Solar-energy-induced chemical reactions, including photocatalysis and photoelectrochemistry, have gained considerable attention because of the simplicity of their procedures and use of solar radiation as the energy source. To improve performance of water splitting reactions, the use of catalysts has been widely investigated. For example, the novel-metal catalysts possessing extremely high catalytic properties for various reactions have been considered. However, due to the rarity and high costs of the novel-metal materials, the catalysts were considered unsuitable for universal use. Although other transition-metal-based materials have also been investigated, carbon-based materials, which are obtained from one of the most common elements on Earth, have potential as low-cost, nontoxic, high-performance catalysts for both photo and electrochemical reactions. Because abundancy, simplicity of synthesis routes, and excellent performance are the important factors for catalysts, easy optimization and many variations are possible in carbon-materials, making them more attractive. In particular, low-dimensional carbon materials, such as graphene and graphitic carbon nitride, exhibit excellent performance because of their unique electrical, mechanical, and catalytic properties. In this mini-review, we will discuss the performance of low-dimensional carbon-based materials for water splitting reactions. Hence, the use of hydrogen as an energy source has the potential to significantly lower current global pollution rates. In addition, water splitting systems that involve splitting of water into hydrogen and oxygen molecules can be considered as sustainable and reliable because they can produce high-purity hydrogen and oxygen without any destructive by-products. Moreover, these sustainable water-splitting systems can be powered by existing renewable energy sources such as solar, wind, hydro, and geothermal powers. Since electrochemical water splitting was first reported, it has been widely applied. Water electrolysis, an efficient and clean technology used for the generation of high-purity hydrogen, also shows excellent adaptability. Solar-powered water splitting systems are developed through the engagement of photocatalytic and photoelectrochemical systems that effectively convert incident solar energy into chemical bonds by using light-absorbing materials [Increasing energy consumption rates and dependency on fossil fuels have resulted in wide-spread ecological contamination. The dependency on fossil fuels can be reduced by encouraging the use of hydrogen as an energy resource. Hydrogen has the potential to be an ideal energy carrier because it theoretically possesses high gravimetric energy density, can be produced easily through various methods, and, most significantly, produces zero COaterials ,2,3,4.2) [Hydrogen is regarded as a clean solar fuel because of its high free energy content, accessibility as a water resource, and production of water and oxygen as sole byproducts . Additio2) . The pro2) ,4,7, desThe methods of water splitting systems can be separated to electrochemistry, photoelectrochemisty, and photocatalysis, like shown in Numerous semiconductor materials have exhibited the capability to split water under solar irradiation ,9. The sThe PEC cell, the center portion of 3N4) [Carbon-based materials have attracted considerable attention for use in a variety of chemical reactions, such as HER, OER, ORR owing to their low cost, high abundancy and highly stable catalytic properties ,9,17,18.3N4) ,24, grap3N4) ,26, and 3N4) ,28, has 3N4) . Two-dim3N4) . These m3N4) ,30. GQDs3N4) . The ban3N4) .2·g−1 [2·V−1·s−1 [Graphene is a monoatomic layer of an indefinite number of carbon atoms arranged in a 2D hexagonal lattice . Graphen2·g−1 ,35 coupl·V−1·s−1 , enables·V−1·s−1 ,35,36,37Graphene has been applied to photocatalytic water splitting reactions as well as PEC systems due to its excellent conductive properties and large surface area. As a co-catalyst, N-doped monolayered graphene has been employed on Si photocathodes so as to investigate the catalytic activity of graphene and the effect of nitrogen doping in the PEC system. Sim et al.  have tra2 and Cu for improved photocatalytic reaction. While the TiO2 generates electron-hole pairs under light irradiation, charge carriers are efficiently separated due to the high charge carrier transfer and mobility of graphene. PEC measurement also demonstrates an increment of photocurrent density with graphene as a co-catalyst, compared to ones without graphene. When coupled with graphene, enhancement of PEC performance of the photoelectrode materials with low diffusion lengths and low absorption rates, such as Fe2O3, was witnessed. The high conductive properties of graphene compensate for the loss of efficiency caused by low diffusion lengths. Yoon et al. [2O3. The composition of Fe2O3 and inverse opal graphene reached a photocurrent density of 1.62 mA·cm−2 at 1.5 V vs. RHE, under AM 1.5 G, which is 1.38-fold higher than that of pristine Fe2O3 with 1.17 mA·cm−2. The inverse opal structure of the graphene can also play an important role in the charge transferring mechanism where it facilitates the fast transfer of electrons, generated by Fe2O3 loading.Owing to its outstanding electrical properties, graphene can effectively improve the charge separation and suppress the recombination of the excited carriers, generated by the photocatalysts or photoelectrodes. Lv et al.  investign et al.  reported2 nanoparticles through a solvothermal treatment approach. Photocatalytic reaction and PEC performance were measured to investigate the role of the NGR. NGR/TiO2 composite showed a photocurrent density of 7.0 μA·cm−2, whereas the TiO2 was about 1.5 μA·cm−2. The photocurrent density of TiO2 composite with reduced graphene oxide (rGO), which was also investigated as a comparison, was approximately 4.5 μA·cm−2, which demonstrates that NGR has a higher charge transfer rate than rGO. This high charge transfer rate facilitates the efficient generated-charge separation and disables charge recombination for enhanced hydrogen evolution.Chemical doping of heteroatoms such as boron and nitrogen is one of the most practical approaches to modify the diverse properties of graphene. Doping can cause semiconducting behaviors in graphene with different valence electrons of the heteroatoms. Additionally, the catalytic activity of graphene towards photocatalytic reactions is enhanced due to the formation of active regions caused by asymmetric spin and charge distributions during doping. Doping with nitrogen has been considered as a means of producing an ideal photocatalyst. Nitrogen-doped graphene (NGR) has shown great potential in metal-free photocatalysts. Along with their unique semiconducting and catalytic properties, it can also be used as a supporting matrix of semiconductor-based photocatalysts in enhancing photocatalytic activity. Mou et al.  have syn−1 and a highest current density of 120 mA·cm−2 at 1.75 V vs. RHE. Zhou et al. [−2 which is approximately ~200 mV with a Tafel value of 79.3 mV·dec−1.The electrochemical study of modified NGR with a transition metal was conducted for the overall water splitting reactions. Joy et al.  incorporu et al.  wrapped 2 production, CO2 reduction, and pollutant degradation [Zero bandgaps of graphene have proved challenging for their use as a photocatalyst. Efforts have been made to overcome this challenge by modifying and manipulating graphene so as to activate its surface for reactions. Graphene oxide is a derivative of graphene, tuned according to the degree of oxidation. With an extended wavelength of light absorption, graphene oxide can be used in photocatalysis for Hradation ,29. Due radation . Oxidizeradation . Oxygenaradation .2 on graphene oxide as a hierarchical framework electrocatalyst for HER. The MoS2/GO framework hydrogels were prepared with the use of a simple hydrothermal process for a binder-free synthesis. This 3D hierarchical framework of MoS2/GO showed exhibited a specific surface area and electrical conductivity due to the π–π interaction between 2D layer properties of both MoS2 and GO. With a ratio of 2:5, MoS2 and GO respectively, showed superior catalytic activity towards HER with an overpotential of 107 mV, which is comparatively higher than that of pure GO, as shown in −1 was achieved with a 2:5 ratio. Moreover, the reaction exhibited a high level of stability with little or no change in the cathodic current during 8 h of continuous operation. The overall EC performance of the composite was superior to previous MoS2 catalyst’s performance, suggesting that GO is a well-suited framework for electrocatalysts. Hu et al. [x on GO with different degrees of oxidation through the use of facile wet chemical methods as an electrocatalyst for HER. Appling different weight ratios of graphite and KMnO4, GO, with various degrees of oxidation, was measured with Raman spectroscopy, as shown in −1 indicate the D, G, and 2D bands of graphene, respectively. The relative intensity ratio of ID/IG shows the degree of oxidation. MoSx/GO composite with 1:4 ratio of graphite and KMnO4, respectively, denoted as MoSx/GO2 with ID/IG ratio of 0.88, showed improved HER performance, as shown in 2 exhibited the best performance improvement with an overpotential of 180 mV and a Tafel value of 60.5 mV·dec−1. The MoSx/GO2 composite has the smallest charge transfer resistance value among all of the prepared samples, owing to the rapid electron transport between the active site and the electrode. It should be noted that by modifying the degree of oxidation in GO, catalytic performance can be enhanced.With its large specific surface area and excellent electrical properties, graphene oxide is regarded as being an ideal support for catalysts. Zhou et al.  have grou et al.  have alsReduced-Graphene Oxide (rGO) occurs when graphene oxide is reduced for the purposes of increasing the quantity of active sites available for photoelectrochemical reactions . rGO is −2 and was calculated to 3.16% of half solar-to-hydrogen conversion efficiency whereas the bare SiNW was measured at 0.09 V overpotential and 0.91% half STH. The onset potential at −1 mA·cm−2 exhibited a positive shift from 0.179 V of SiNW to 0.326 V of rGO-SiNW, thereby proving an enhancement of PEC HER.Sim et al.  investig2 and rGO heterojunctions for photo-enhanced HER, as shown in 2 as the electron transfer bridge and the electrocatalyst for PEC hydrogen production, respectively. High electric conductivity and high photoresponsivity of rGO showed a synergetic effect with MoS2, the HER electrocatalyst. Also, utilizing the tunablity and gaps of rGO, is advantageous in generating a built-in electric field so as to suppress electron-hole recombination and to promote the catalytic activity of MoS2. Compared to obvious photo-enhancement with rGO, MoS2 with graphene showed a minor light response, evidence that rGO plays a crucial role in the PEC system of HER catalysis. With graphene, overpotentials at a current density of −10 mA·cm−2 were 197 mV under dark and 193 mV under one-sun illumination resulting in 4 mV of photo-enhancement. MoS2/rGO resulted in 32 mV photo-enhancement with an overpotential of 173 mV under dark, and 141 mV under illumination at −10 mA·cm−2 current density, which is eight times the enhancement of MoS2/graphene, as shown in Zhang et al.  have wor2O3 [2O3/rGO/BiV1−xMoxO4 Core-Shell heterojunction arrays were synthesized with and without rGO. A BiV1−xMoxO4 photocatalyst absorbed the visible light thereby generating the electron-hole pair. Assisted by rGO, these electrons are then transferred through ferrite to the cathode. Due to the conductive properties of rGO, the electrons are efficiently transferred so as to inhibit the recombination of photogenerated electron-hole pairs. This mechanism was also shown with other PEC systems.Inorganic semiconductor photoelectrodes composed of transition metal dichalcogenides  such as ZnS , ZnO 5151, Fe2O32O3 ,53, have2O3  have inv2/NiFe-layered double hydroxide (NiFe-LDH) composite for enhanced PEC performance. To begin with, TiO2, under illumination, generates the electron-hole pairs. Following this, owing to the band energy difference and chemical binding interactions between the TiO2/rGO/NiFe-LDH components, the rapid migration of electrons to the current collector occurred due to the superior electron mobility exhibited by rGO. Simultaneously, the holes are collected to NiFe-LDH along with the oxidation of Fe, which acts as the active site for water oxidation. With the support of rGO, the PEC was greatly enhanced as compared to TiO2/NiFe-LDH composite and TiO2 with rGO only. These works have shown the role of conductive rGO in PEC water splitting reactions. The pristine TiO2 nanoarrays displayed 0.92 mA·cm−2 photocurrent density at 0.6 V (vs. SCE) which is relatively low compared to 1.18 mA·cm−2 of TiO2/NiFe-LDH, 1.5 mA·cm−2 of TiO2/rGO, and 1.74 mA·cm−2 of the ternary TiO2/rGO/NiFe-LDH nanoarrays. An overall 1.89-fold enhancement of photocurrent density was observed. The increment of photocurrent density between TiO2/LDH and TiO2/rGO/LDH can be indicated to highlight the role of rGO in this ternary nanoarray structure. 1.74 mA·cm−2 photocurrent density of TiO2/rGO/LDH is 1.47-fold higher than 1.18 mA·cm−2 of TiO2/LDH. This can also be noticed through the comparison between the TiO2/rGO and bare TiO2. TiO2/rGO has exhibited 1.63 times greater photocurrent density than bare TiO2 nanoarrays. Similar results have been shown using other metal oxide semiconductors such as α-Fe2O3 and WO3 with rGO/NiFe-LDH decorations. The improved charge transport, arising from rGO, allows for better separation of the electron-hole pair to suppress surface recombination thereby enhancing PEC systems.Similarly, Ning et al.  have inv3N4) is regarded as a good photocatalyst for solar fuel conversion and pollutant degradation due to its moderate bandgap of 2.7 eV, good chemical stability under light irradiation, low cost and nontoxicity [3N4 a favorable catalyst in water splitting reactions [3N4 powder can be easily synthesized via inexpensive raw materials, such as urea, melamine, dicyandiamide, under high temperatures, further adding to its advantages as a catalyst in water splitting reactions [3N4 powder can be deposited onto solid substrates in PEC water splitting systems [Graphitic carbon nitride  then decorating with g-C3N4. For comparison, amorphous TiO2, Ti foil without anodic oxidation treatment was also investigated. The photocurrent density of g-C3N4 decorated TNTAs (g-C3N4/TNTA) was about 0.86 mA·cm−2 at 1.23 V vs. RHE while pristine TNTAs only measured approximately 0.40 mA·cm−2 at 1.23 V. The photocurrent densities of g-C3N4decorated TiO2 and pristine TiO2 were determined to be 0.19 and 0.07 mA·cm−2, respectively. The lower current density of TiO2, compared to TNTAs, refers to the distinct photo-response of TNTAs. However, in both cases of g-C3N4/TNTAs and g-C3N4/TiO2, an increase in photocurrent density occurred with g-C3N4 decoration. The photocurrent density of g-C3N4/TNTAs was twice the density of pristine TNTA, while amorphous TiO2 increased to a 2.7-fold higher photocurrent density with the g-C3N4 decorations.A similar concept of heterojunctions of TiOu et al.  through 3N4 has also shown excellent performance in EC water splitting. Ma et al. [3N4/Carbon Nanotube (CNT) three-dimensional (3D) composite for OER electrocatalysts. The 2D layer of g-C3N4 showed outstanding activity for OER originating from its high nitrogen contents, along with its unique porous structure with CNT. The composite showed an operating potential of 1.60 V vs. RHE to achieve the current density of 10 mA·cm−2 with a Tafel value of 83 mV·dec−1, which is a similar value to that of 1.59 V vs. RHE and 90 mV·dec−1 for IrO2-CNT, one of the noble-metal catalysts. The composite with 2D g-C3N4 showed improved OER performance compared to that with bulk g-C3N4, owing to an increase in surface area in the 2D structure of g-C3N4. Additionally, it can be noted that comparatively poor electrical conductivity is compensated through the smooth current transfer from the π–π interaction of the two carbon-based materials.The high content of nitrogen in carbon material has facilitated high reactivity during the chemical reactions. g-Ca et al.  have con3N4, reported by Kale et al. [3N4 nanostructure from MNG powder, which was obtained through a simple hydrothermal process with melamine. Geodes are hollow, circular volcanic rocks formed in nature. The multiwalled closed cage structure of nano-sized geodes of melamine is mixed with a sulfur precursor which then undergoes a two-step heating process in order to obtain S-modified g-C3N4. The overpotential of 290 mV achieved with this structure is lower than that of the novel-metal as well as that of non-metal, metal/metal oxide catalysts.Sulfur-modified g-CGraphene quantum dots (GQDs) are zero-dimensional (0D) derivatives of graphene with a diameter of up to ~20 nm . Due to −1. However, as shown in The electrocatalytic performance of nitrogen functionalized inter-connected GQDs (c-GQDs) was reported by Kundu et al.  GQDs wer−2 similar values of 0.3 V and 0.11 V, planar Si and porous Si, respectively, were observed. The positive shift of the overall J–E curves induced by the N-GQSs indicates an enhancement of HER performance. The group further experimented with N-GQSs on Si nanowire (SiNW). Similarly, an enhanced HER performance can be observed with a positive shift of 0.09 V for the onset potential, in addition to a high ABPE of 2.29%, which is 2.5-fold higher than that of bare SiNW.Atom-thick Graphene Quantum Sheets (GQSs) were introduced into the PEC system as a co-catalyst for the water splitting system . Moon et2 production rate and the light source are the important factor. Similar with photocatalysis, the intensity of the light source is a crucial factor in photoelectrochemical system. The onset potential measured when the current density reaches −1 mA·cm−2, and the overpotential, which is measured when the current density reaches −10 mA·cm−2, are the important factors in PEC hydrogen evolution along with the light intensity. However, in some cases where the materials show relatively low current density, the photocurrent density is measured to evaluate the performances. Likewise, the current density of the oxygen evolution performances is relatively low compared to HER, therefore the photocurrent density is measured along with the light intensity. The HER performances are shown in We went through the low-dimensional carbon-based materials for the water splitting reactions. The materials are summarized below for electrochemical, photocatalysis, and photoelectrochemical system. The few key parameters measured for electrochemical systems are the Tafel slope and the overpotential to investigate the performance of the materials. 3N4 also act as reaction sites, similar to oxygenated functional groups in GO and rGO. The simple synthesis methods and high abundancy of these 2D materials are advantageous aspects of the multi-purpose catalyst for use in water splitting reactions. Furthermore, 0D GQDs and GQSs have also shown outstanding performance as catalysts. Due to the quantum confinement effects of these 0D materials, GQDs and GQSs exhibit increased surface area and distinct bandgaps, making them favorable catalysts.As the search for environment-friendly energy source progresses, hydrogen produced from water splitting reactions is proving to be a possible candidate. However, efficient energy conversion in water splitting reactions requires further optimization through the development of suitable catalysts. Novel-metal and numerous semiconductor-based materials are available for the enhancement of catalytic performance. However, low cost, non-toxic, abundant, and highly efficient catalysts are vital for their commercial use. Due to their unique electrical and catalytic properties, carbon materials exhibit outstanding performance as both catalyst and cocatalyst and are promising candidates for worldwide commercial use. Because of its layered 2D structure and π–conjugated planes, graphene exhibits excellent electrical properties, enhancing the separation of photogenerated EHPs and electron transfer for catalytic reactions. Furthermore, the transparent characteristic of graphene, makes it suitable as a cocatalyst also. The oxygenated functional groups of the GO and rGO act as active sites for the water splitting reaction with defects of the functional groups opening the bandgap thereby enabling the absorption of light energy. With the created bandgap, GO and rGO is also suitable for creating heterojunctions with other carbon materials as well as the semiconductor-based catalyst thereby improving charge separation and reducing recombination probability. The pyridinic and quaternary nitrogen atoms in g-CIn summary, low-dimensional carbon-based materials are being widely studied for the enhancement of photo and electrochemical water splitting reactions, as well as photocatalysis. These materials have been found to exhibit suitable characteristics for use as catalysts due to their exceptional performance and ecofriendly properties. Many derivatives of the carbon materials are available, and they have been co-opted for multiple uses. Numerous combinations of other catalysts, as well as other derivatives, are available due to the flexible optimization of the carbon materials. Low-dimensional carbon materials exhibit enormous potential as highly efficient, low cost and ecofriendly catalysts in water splitting reactions for the production of hydrogen as an energy source.


The level of PD-L1 expression in immunohistochemistry (IHC) assays is a key biomarker for the identification of Non-Small-Cell-Lung-Cancer (NSCLC) patients that may respond to anti PD-1/PD-L1 treatments. The quantification of PD-L1 expression currently includes the visual estimation by a pathologist of the percentage  of tumor cells showing PD-L1 staining. Known challenges like differences in positivity estimation around clinically relevant cut-offs and sub-optimal quality of samples makes visual scoring tedious and subjective, yielding a scoring variability between pathologists. In this work, we propose a novel deep learning solution that enables the first automated and objective scoring of PD-L1 expression in late stage NSCLC needle biopsies. To account for the low amount of tissue available in biopsy images and to restrict the amount of manual annotations necessary for training, we explore the use of semi-supervised approaches against standard fully supervised methods. We consolidate the manual annotations used for training as well the visual TPS scores used for quantitative evaluation with multiple pathologists. Concordance measures computed on a set of slides unseen during training provide evidence that our automatic scoring method matches visual scoring on the considered dataset while ensuring repeatability and objectivity. More precisely, the proliferation and activation of T-cells as well as the production of the cytokine signaling proteins are inhibited by the binding of PD-L1 proteins to (i) the PD-1 receptors of activated T-cells and (ii) to the CD80/B7-1 receptors on T-cells and antigen presenting cell. Immunotherapeutic drugs aim at restoring the ability of immune cells to kill tumor cells by blocking this escape pathway. The role of complementary or companion diagnostics assays is, in this context, to help the identification of patients which are likely to benefit from a checkpoint inhibitor therapy, i.e. patients with high tumor PD-L1 levels3. The tumor PD-L1 level is estimated by a trained pathologist on small biopsy specimen stained with a PD-L1 antibody and most usually obtained in clinical practice with small needles. More precisely, its quantification is based on the tumor proportional score or TPS, which is defined as the percentage of tumor cells with PD-L1 staining localized in the membrane. For three of the four assays used for PD-L1 therapy of non-small cell lung cancer (NSCLC) and as detailed below, the negative or positive PD-L1 status of the patient is set by comparing this percentage to an assay specific cut-off value4.The programmed death receptor 1 (PD-1) checkpoint protein with its ligand - programmed death ligand 1 (PD-L1) plays a major role in the immune escape of the cancerous tumor cells, i.e. in the inhibition of the human immune system responses7. For the 28-8 and 22C3 assays, PD-L1 status is defined based on the 50% standard cut-off on the TPS, the cut-off for the 22C3 assay being additionally extended to the 1% cut-off9. For the Ventana SP142 assay, PD-L1 status is defined based on the 50% cut-off on the TPS and on the 10% cut-off on the tumor area occupied by PD-L1 expressing infiltrating immune cells. Finally, for the Ventana SP263 assay, PD-L1 status is defined based on the 25% cut-off on the TPS. The reference scoring guidelines and more specifically the standard cut-off values for each assay system have been individually set and validated for their respective treatments in clinical trials to maximize correlation with patient outcome data10. It is interesting that some studies have shown the relative similarity of the two Dako and the Ventana SP263 assays12, providing some evidence that the three assays could be interchangeable. However, interchanging the reference standard cut-off values or defining a unique cut-off between assays could so far not be clinically demonstrated. Also, marked differences are reported while classifying patients as positive or negative if the same cut-off is enforced between the assays7. This seems to have been recently confirmed by Munari et al.13 which report that the proportion of positive cases at the cut-off values associated with the Dako 22C3 clone significantly differs if the slides are stained with the Ventana SP263 clone and not with the Dako 22C3 clone as intended. In this work, we solely consider the Ventana SP263 PD-L1 system which was developed with durvalumab. As detailed in the corresponding development study10, the cut-off value at 25% was set to maximize the predictive value of the test on durvalumab treated patients while considering other parameters such as the prevalence of the population, the ease of visual scoring, and optimizing for the negative predictive value. Because the focus of this work is on the SP263 assay system, that other cut-point values have not been shown to be clinically relevant for this assay, and that the interchangeability between different assays is still an open topic of research, we consider only the standard reference 25% cut-off in the remaining of this study.There exists multiple assay systems to inform on PD-L1 treatment decision, each system consisting of an therapeutic agent , a primary antibody clone (28-8 and 22C3 clones by Dako and SP142 and SP263 clones by Ventana respectively) and a reference standard cut-off value for setting the PD-L1 patient status14. First, PD-L1 staining is not restricted to the membrane of tumor cells: tumor cells with strong cytoplasmic but no membrane staining, immune cells (e.g. macrophage and lymphocyte) as well as necrotic and stromal regions are not included in the score calculation despite possibly showing PD-L1 staining. A challenge specific to visual scoring is moreover the difficulty for any human observer to estimate heterogeneous distribution of cell populations, with positive and negative tumor regions being often spatially inter-mixed. These challenges make TPS estimation a subject to some variability among pathologists12. In this work, we propose an automatic scoring solution based on image analysis which achieves an accuracy similar to visual scoring while ensuring objective and reproducible scores and could potentially be used as a computer aided system to help pathologists make a better therapeutic decision. The complexity of the scene and the difficulty of the task naturally lead us to opt for a deep learning-based solution.There are known challenges to an accurate estimation of TPS20 as well as in the more specific field of digital pathology image analysis24. As a first example, Litjens et al. showed in their pioneering study21 the potential of deep learning for the detection of prostate cancer regions and of breast cancer metastasis in lymph nodes on digital images of H&E stained slides. More specially, two fully supervised convolutional neural networks (CNN)26 were independently trained on the complete manual annotations of 200 and 170 tissue slides respectively. Cruz-Roa et al. proposed a similar fully supervised CNN-based approach for the detection of invasive breast cancer region in H&E stained slides22, relying on the annotations of nearly 400 slides from multiple different sites for training. Most previous works in the field of digital pathology image analysis build on fully supervised networks, which are trained only on labeled information obtained through very extensive manual annotations. Collecting the necessary amount of annotations is however a well known problem in this field. This is because images with the level of complexity observed in digital pathology can and should only be interpreted by experts with several years of training and experience. This is a key difference to other fields of application of deep learning methods, for instance in robotics, for which the complexity arises more from the number and diversity of the classes than on the ability of untrained humans to recognize these classes. Because pathologists can disagree on the interpretation of a given slide, it is often necessary to collect manual annotations of the same slide from different pathologists. While being often necessary in order to reduce ambiguity in the training set, annotating the same slide multiple times significantly increases the burden of manual annotation.Previous works have shown the ability of deep learning-based methods to solve complex tasks in the field of image analysis and understandinget al.27 introduced a long short term memory (LSTM) network to directly predict patient outcome on tissue microarrays. Campanella et al.28 developed a multiple instance learning (MIL) solution to predict prostate cancer diagnosis on needle biopsies, the training of the system requiring a huge dataset of more than 12000 slides. The use of needle biopsies and the small size of datasets available in clinical trial studies make however the use of the aforementioned weakly supervised learning approaches challenging in our scenario.To bypass the need of manual annotation for region segmentation or object detection, some recent works proposed to directly infer the patient label. Bychkov et al.29 for the Her2 scoring of breast cancer slides, scores do not have to be learned and can be accurately replicated using the heuristic definition given in the scoring guidelines and an intermediate detection step. To keep the intermediate detection step while reducing the amount of manual annotation, we propose a semi-supervised learning solution. These approaches still employ manual annotations but make use of raw unlabeled data to lower the necessary amount of labeled data30. Only a few previous works33 have used semi-supervised learning for digital pathology image analysis. Peikari et al. recently introduced a cluster-then-label method based on support vector machine classifier that is shown to outperform classical supervised classifiers31. Sparks et al. proposed an image query approach based on semi-supervised manifold learning34. Other works focus on transfer learning where the model weights are initialized on other classification task33 or learned on raw unlabeled data using representation learning, the labeled data being then used for model refinement only. Given the recent advances in the field of generative adversarial networks (GAN), our work build on class auxiliary generative adversarial networks (AC-GANs). To the best of our knowledge, this study introduces the first application of AC-GAN networks for digital pathology image analysis as well as the first computer-aided diagnostic tool for PD-L1 scoring on needle biopsies.As previously shown by Vandenberghe 35 is more precisely chosen to this end. Second, TPS is computed as the ratio between the pixel count (i.e. the area) of the detected positive tumor cell regions to the pixel count of all detected tumor cell regions. Since approaches based on pixel counts often show higher performance than cell-count based quantification36 and enable an easier annotation workflow, we estimate TPS based on the pixel counts of PD-L1 positive and negative tumor regions.The proposed algorithm for the automated TPS estimation consists of two main steps. First, positive tumor cell regions TC(+) and negative tumor cell regions TC(−) are detected using a deep semi-supervised architecture trained on both labeled and unlabeled data. An Auxiliary Classifier Generative Adversarial Network (AC-GAN)37 and morphological filtering is performed, leading to the introduction of another class corresponding to non-tissue regions. Labeled image patches are generated on a regular grid defined on the annotated regions that are concordant between the two pathologists. This leads regions with different classes to be discarded from the analysis. On the remaining set of non-annotated slides, unlabeled patches are generated on a regular grid defined on the detected tissue.A small subset of slides is selected and partially annotated by two pathologists for positive tumor cells TC(+), negative tumor cells TC(−), positive lymphocytes, negative lymphocytes, macrophages, necrosis and stromal regions. A simple detection of the tissue and background regions based on Otsu thresholdinget al.35. GANs consist of two neural networks, a generator network (G) and a discriminator network (D). The network G transforms a noise vector z, which is sampled from a simple distribution such as an uniform or normal distribution, into an fake image Xfake = G(z) using a series of deconvolution and activation layers. The network D classifies an input image as real or fake. More formally, the discriminator outputs the probability distribution over the sources P(S|X), S ∈ {real, fake}, through a series of convolutional and activation layers by maximizing the log-likelihood of the correct source:Let’s first recall the principle and architecture of Generative Adversarial Networks introduced by Goodfellow 38 formally defined as follows:The two networks are trained in opposition following a minimax gameMore intuitively, the discriminator is trained to differentiate whether the image is coming from real image distribution or fake image distribution from G. In the opposition, G is trained to produce images which are more and more difficult to be identified by the discriminator as real or fake images. When the optimum of this minimax game is reached, the generator creates images so close to the real images that they cannot be differentiated by the discriminator.40. Some recent works42 in the computer vision community proposed its extension to the AC-GAN variant. The AC-GAN leverages the side information on the class labels by two means. First, the generator is conditioned with class labels to perform conditional image generation: the generator network takes as input a noise vector concatenated with the one-hot embedded class labels. The concatenated vector goes through a series of transformations by a CNN to create fake images Xfake = G. Second, the discriminator, in addition to predicting the correct source probability P(S|X), also performs class label recovery. The discriminator contains an auxiliary classifier network which outputs the probability distribution of the class P(C|X). The network parameters between the auxiliary classifier and the discriminator are shared, which enables joint learning of P(C|X) and P(S|X). This requires the introduction of a second cost function as the log-likelihood of the correct class:The GAN architecture is, however, strictly unsupervised and generative in nature i.e. it can only be used to generate realistic new samples, leading its current main application in digital pathology to be stain normalizationLC + LS and the generator G to minimize LS − LC. Since the maximization and minimization problems are interlaced, the AC-GAN model is trained by alternatively updating the two models G and D. The exact AC-GAN architecture used in this work is displayed in Fig. Similar to the vanilla GAN formulation, the two networks D and G are trained by a minimax game with slight modification. The discriminator D is trained to maximize In our application, we are only interested in the classification performance of the network. While we investigate the discriminator performance in more detail, further quantitative assessment of generator has not been performed. Some qualitative examples of images produced by the generator are however displayed in Fig. 43 and a shallow VGG25 network modified to be fully-convolutional and to include batch-normalization44 are commonly employed for the analysis of digital pathology images. The two non-generative semi-supervised networks are vanilla auto-encoder networks45 built on the two aforementioned classification networksWe test the semi-supervised generative AC-GAN architecture against two baseline classification networks for fully-supervised learning and two baseline non-generative networks for semi-supervised learning. The two chosen fully-supervised architectures, the inception network v210 and scanned on an Aperio scanner. Scene resolution is 0.49 μm per pixel. On a subset of Nscore = 60 slides, visual TPSs are estimated on the scanned digital slides by two in-house pathologists. This completes the visual TPS estimated on glass-slides and obtained from an external source . A smaller subset Ncnm = 20 is selected across the range of TPS values for training and testing the supervised part of the region detection model and have as such been partially manually annotated by the two in-house pathologists using an in-house annotation software. More specifically, 15 slides are used for training and 5 slides for testing and optimizing the model parameters. The accuracy of the resulting computer-based TPS is estimated against the three visual TPS values on the remaining Ntest = 40 unseen slides, i.e. unused for training or testing the supervised nor the unsupervised part of the region detection model.Our dataset consists of 270 NSCLC needle biopsy slides from a subset of the clinical trials (NCT01693562) and (NCT02000947). The slides are stained with the Ventana PD-L1 (SP263) assayk and 200 k iterations respectively using the Adam optimizer46 with the following learning parameters: lr = 0.0001, beta1 = 0.5, beta2 = 0.999. For each network, we select the model weights that maximize the accuracy on the test dataset in order to avoid overfitting on the training set. The developed framework is based on the open-source Keras API47 and Tensorflow framework48.Patches of size 128 × 128 pixels are extracted on a regular grid of 20 pixel stride defined on the concordant annotated regions and are augmented using 90 degree rotation. We sample unlabeled patches on a regular grid of 60 pixel stride defined on the tissue area of the 210 slides which have not been scored by all pathologists as well as on the 15 annotated slides which are used for generating the labeled training database. This yields a total of around 180k labeled and 400k unlabeled patches for training as well as 40 k labeled patches for testing. All models are trained using the same patches. Batches with 64 labeled patches are used for training the fully-supervised networks. Batches with 32 labeled patches and 32 unlabeled patches are used for training the three semi-supervised networks. For the two non-generative semi-supervised networks, the reconstruction loss is computed on the complete batch and the classification loss on the labeled patches only. For the AC-GAN, the generative loss is computed on the complete batch and the classification loss on the labeled patches only. Training of the four baseline networks and of the generator and discriminator of the AC-GAN network is performed for 100 Ntest unseen slides. An example of region detection results is provided in Fig. Nscore = 60 slides for which three visual TPS values are available, we predict the different class probabilities and assign each pixel to the class label of the maximum probability. Given the resulting TC(+) and TC(−) pixels in a given slide, we approximate the corresponding TPS as the ratio of the number of tumor positive cell pixels to the total number of tumor cell pixels:The prediction is restricted to the detected tissue regions and is performed in a sliding window manner with a stride of 32 pixels. The ability of the system to differentiate between the classes has been qualitatively checked on the TPSref. as the median of the three visual scores. The following concordance measures between the automated computer-based TPS and the consolidated visual score is calculated on the set of 40 unseen slides only to ensure an independent estimation of the performance: Lin’s concordance coefficient (Lcc), Pearson correlation coefficient (Pcc) and Mean Absolute Error (MAE). As reported in Fig. Lcc = 0.94, Pcc = 0.95, MSE = 8.03) than any other fully-supervised and semi-supervised network.We then compute for each slide the consolidated visual score path. as the mean absolute pairwise difference between the associated visual scores:To quantify the variability between the visual scores cf. Fig. , we addiet al. recently confirmed the high concordance of PD-L1 scoring on glass slides and digital slides12, leading us to pool of all data to estimate inter-rater variability independently of a glass/digital scoring. Given increasing maximum levels of inter-rater variability, we restrict the computation of the aforementioned concordance measures to the slides whose associated value stays below the given maximum. As displayed in Fig. Please note that Tsao 10.We compare hereby in more detail the performance of the automated score based on the AC-GAN detection to the three visual scores by pathologists. Table TPScnm = 89 is very close to the highest value TPS1 = 0.90. Computing the concordance to the visual scores TPS3 estimated on the microscope, we note that the automated TPS scores leads to a higher agreement  than the two pathologist scores estimated on digital slides  − . These observations are confirmed on the reported OPA values of the low/high PD-L1 status at 25% cut-off. The automated scoring systematically yields the highest agreement with the visual TPSs. In particular, considering the third visual TPS, automated scoring achieves an OPA of 0.88 to be compared with OPAs of 0.88 and 0.80 observed for the first and second pathologists visually scoring on digital slides.The automated TPS computed from the AC-GAN predicted regions yields the highest correlation and the lowest absolute error for all but one case. Note that in the only case where this does not hold, the Pearson correlation of the automated score To further analyze the concordance of automated scoring versus visual scoring, we consider the AC-GAN score and the three visual scores independently of their source and compute for each of the four resulting scores all concordance measures between each score and the median of the three remaining scores. Results displayed in Table 10, we present what is to our knowledge the first proof of concept study showing that deep learning enables an accurate and automated estimation of the PD-L1 expression level and PD-L1 status on small biopsies samples.The aim of anti-PDL1 therapies is to revive the immune response to cancer cells: inhibiting the PD-L1 pathway reverses T-cell exhaustion and restores T cell’s cytotoxic activity. Patients with high expression generally showing higher response rates and longer progression free survival than patients with low expression, an accurate testing of PD-L1 expression may inform on the best treatment decision on whether or not the patient should follow such therapy. As we recalled in the introduction, there is a significant heterogeneity between the existing PD-L1 assay system, different antibodies being employed as companion and complementary diagnostics for different immunotherapeutic drugs, different patterns of staining (tumor cells only or tumor cells and tumor infiltrating immune cells) being considered for different indications  and finally different clinically relevant cut-off values being used for different antibodies for a given indication. While the focus of this work is to replicate the test associated to the antibody clone SP263 on NSCLC patientsThe performed analysis of inter-rater variability suggests that the accuracy achieved by the proposed automated scoring method is concordant with visual scoring by pathologists on our dataset. This work focuses on the automated estimation of the PD-L1 tumor proportion score yet, it more generally introduces the first application of deep semi-supervised and generative learning networks (AC-GAN) in the field of digital pathology. It also provides first evidence of the good performance of this model against standard fully supervised learning networks.2 to detect the regions and to compute the score using a single NVidia K80 GPU chip. Given the slide resolution of 0.5 μm, this corresponds to predicting on an image of around 20 K × 20 K pixels. This translates into an average computation time of 78 s per biopsy. Because the AC-GAN architecture can be further optimized for speed upon transformation into a fully convolutional network49, that the prediction can be parallelized on multiple GPU chips and that the time to estimate the score from the detected regions is neglectable, the scoring system is foreseen to take only a few seconds in a potential diagnostic setup.The proposed system takes 245 s per cmGoing beyond the presented proof of concept, we believe that further evidence could be provided by increasing the size of the unseen dataset on which the agreement between the automated and visual TPSs has been computed as well as ensuring that the comparison between the visual scores is not biased by external parameters such as (i) the heterogeneous experience of the pathologists and (ii) if the scoring is performed on digital or glass slides. Also, the interoperability of the system should be further analyzed, for instance by applying the trained model on an independent patient cohort in particular on data generated by a different diagnostic laboratory. While we focus in this study on the technical aspects of the image analysis and automated scoring algorithm to include its technical performance compared to manual scoring by a pathologist, the use of the proposed algorithm as a predictive signature of response to durvalumab represents a logical extension of this work and is currently under study. Getting confirmation results will be key before potentially applying the proposed system into clinical routine.50, we do foresee a computer-based quantification of tumor-associated immune cells on the SP263 assay as a logical next step. In this case, an additional challenge to be solved is the low agreement among pathologists on immune scoring12. More generally, we envision that the success of checkpoint inhibitor related immune therapies can be increased by automated profiling of tumor and immune cells with respect to their cluster of differentiation (CD) protein expression levels. This study on CD274 (PD-L1) in NSCLC is the first conclusive step in this journey.Even though we currently do not have data to support this claim, we would expect that the proposed method could be applied to the 22C3 and 28-8 Dako PD-L1 assays provided that enough manual annotations are available for every class of interest in each assay. This is first because the estimation of the PD-L1 status solely depends on the TPS and second because these two assays appear relatively similar in overall pattern with the SP263 assay. However, because each PD-L1 clone is associated with a different clinically relevant cut-off value, the determination of the patient status would have be adapted according to their respective guidelines. Since staining of tumor cells with the Ventana SP142 assay has been shown to be less concordant (e.g. to stain fewer tumor cells) and that the decision on the PD-L1 status is not only based on TPS but also on the percentage of tumor area occupied by PD-L1 expressing tumor-infiltrating immune cells, we do not foresee that the proposed algorithm could be used at this point to determine patient status on this assay system. However, because the determination of the PD-L1 status in urothelial carcinoma (UC) tissue involves the scoring of both tumor cells and immune cell regions and that durvalumab has demonstrated favorable clinical activity in locally advanced/metastatic UC


Cerebrospinal fluid (CSF) biomarkers reflect ongoing processes in the brain. Growth-associated protein 43 (GAP-43) is highly upregulated in brain tissue shortly after experimental ischemia suggesting the CSF GAP-43 concentration may be altered in ischemic brain disorders. CSF GAP-43 concentration is elevated in Alzheimer’s disease patients; however, patients suffering from stroke have not been studied previously.The concentration of GAP-43 was measured in longitudinal CSF samples from 28 stroke patients prospectively collected on days 0–1, 2–4, 7–9, 3 weeks, and 3–5 months after ischemia and cross-sectionally in 19 controls. The stroke patients were clinically evaluated using a stroke severity score system. The extent of the brain lesion, including injury size and degrees of white matter lesions and atrophy were evaluated by CT and magnetic resonance imaging.P = 0.02 and P = 0.007). At 3–5 months after stroke GAP-43 returned to admission levels. The initial increase in GAP-43 during the nine first days was associated to stroke severity, the degree of white matter lesions and atrophy and correlated positively with infarct size .Increased GAP-43 concentration was detected from day 7–9 to 3 weeks after stroke, compared to day 1–4 and to levels in the control group  contains supplementary material, which is available to authorized users. The presynaptic protein Growth associated protein 43 (GAP-43) is highly expressed during neuronal development and synaptogenesis and thereafter sustained in presynaptic terminals in the hippocampus and associate cortex in the adult human brain –3. GAP-4The study was approved by the ethics committee for medical research at the University of Gothenburg, Sweden. Longitudinal CSF samples were collected prospectively from 28 patients with an acute ischemic stroke that occurred within the first 3 days of admission at Sahlgrenska University Hospital, Department of Neurology, between September 1992 and January 1994. Exclusion criteria were patients with a history of previous stroke, malignant or autoimmune diseases, severe infections, or taking immunosuppressive drugs. When possible, CSF samples were collected on up to five occasions; at admission to the emergency ward at day 0–1, and then day 2–4, day 7–9, 3 weeks and 3–5 months. CSF samples were taken by lumbar puncture in the L3/L4 or L4/L5 interspace, the first 12 ml of CSF was collected in polypropylene tubes, centrifuged at 2000 g for 10 min, and stored at 80 °C pending biochemical analyses.n = 9), mild (n = 8), moderate (n = 4), and pronounced (n = 3), while white matter lesion status was scored as normal (n = 15), general-light (n = 1), general-moderate (n = 3), and cortical-light (n = 5) on T2-weighted MRI scans. Two patients lacked information about atrophy and white matter lesion, and two additional patients did not have enough samples for longitudinal analysis. Therefore, there were 24 patients for analysis of associations between these clinical measures and GAP-43 concentration. The control group consisted of healthy volunteers, without history, symptoms or signs of cognitive disturbances, neurological or psychiatric symptoms.All stroke patients were clinically evaluated in a standardized way, as described previously . Briefly2SO4 and the absorbance was read in a Sunrise microplate absorbance reader  at 450 nm . Cerebrospinal fluid sample concentration was calculated via interpolation from the calibrator curve (4PL weighted 1/Y2).CSF GAP-43 concentration was measured by an in house enzyme-linked immunosorbent assay (ELISA) described in detail previously , with miFor CSF GAP-43 assay characterization, intra- and inter-assay precision, measurement range, recovery, parallelism, selectivity and sample storage stability was evaluated. For intra- and inter-assay precision, five duplicates of two samples with high and low concentrations, respectively, were analyzed on 5 different days. Two technicians were involved in the acquisition of data. For determination of measurement range (lower limit of quantification (LLOQ) and upper limit of quantification (ULOQ)), calibration curve data from 5 runs were used. The relative error of the back-calculated concentrations for the calibrators was plotted as a function of concentration, and LLOQ and ULOQ determined as calibrator points with < 20% relative errors. Parallelism was determined in duplicates of one sample analyzed as neat and diluted × 2, × 4, × 8, × 16 and × 32 times in assay diluent. Recovery was determined in two samples spiked with the GAP-43 calibrator. Neurogranin interference was determined in duplicates of one neat sample and the same with 10 ng/ml recombinant neurogranin protein spike was measured. Protein stability was evaluated by dividing 3 samples into nine aliquots where one was directly placed at − 80 °C, and the others stored at − 20 °C or at 4 °C overnight or for 1 week, at room temperature for 24 h, or freeze-thawed 1–4 times.t tests on the estimated marginal means. The Pearson correlation coefficient was calculated between subject-specific slopes for the first model of early measurements and the subject-specific slopes of the second model of late measurements to understand the relationship between initial change and late change in GAP-43 concentrations.Because the longitudinal trajectory of GAP-43 values appeared to be nonlinear, the data was initially analyzed in two parts. First, a linear mixed effects model was fit on measurements taken between before 9 days to test the hypothesis that GAP-43 increases directly after stroke. The model included random intercepts and slopes, and time was discretized into Days 0–1, Days 2–3, and Days 7–9. Age and sex were included as covariates and GAP-43 concentrations were normalized for each individual relative to their baseline measurement. Next, the same form of mixed model was fit on measurements taken from Days 7–9, 2–4 weeks, and 3–5 months in order to test the hypothesis that GAP-43 returns to normal levels over time. Differences in GAP-43 concentration across groups were analyzed using approximate A linear mixed model was then fit on longitudinal stroke severity information, again with random intercepts and slopes. The relationship between GAP-43 change and stroke severity change was subsequently assessed by calculating the Pearson correlation coefficient between the subject-specific slopes of stroke severity and subject-specific slopes of GAP-43 as estimated from the respective mixed effects models. The association between GAP-43 change and infarct size was also computed using the Pearson correlation coefficient.F tests and between each pair of groups using approximate t tests on the estimated marginal means. Finally, concentrations of GAP-43 in healthy controls were compared to those of stroke patients at each time group using Mann-Whitney U tests, with correction for multiple comparisons using Holm’s procedure.Additionally, group differences in GAP-43 change across discrete levels of white matter lesions and across discrete levels of brain atrophy were analyzed by fitting the same linear mixed effects model as above but with white matter lesion (or brain atrophy) status included as an interaction with time. Overall group difference was assessed using approximate p = 0.05. All statistical analysis was performed using the R programming language (version 3.4.3) with the nlme (version 3.1) and emmeans (version 1.2) packages being used for mixed effects modelling specifically.All tests were two-sided with a significance level set to rs (day 0–4) = 0.43, p = 0.13; control: rs = − 0.23, p = 0.34) and no differences between gender (stroke (day 0–4): p = 0.78; control: p = 0.45). For white matter lesion status in the stroke patients, there were nine individuals who scored as normal, eight as sporadic, four as moderate, and three as pronounced. For brain atrophy, 15 individuals were scored as normal, one as general-light, three as general-moderate, and five as cortical-light.Demographics of study participants are provided in Table The average intra- and inter-assay variation was assessed by determining the coefficient of variation (CV) of repeated analysis. The intra- and inter-assay CVs were 3.4 and 7.4% respectively, for a sample with a mean GAP-43 concentration of 1184 pg/ml, and 3.2 and 5.8% respectively, for a sample with a mean GAP-43 concentration of 3117 pg/ml. The LLOQ was 315 pg/ml and ULOQ was 10,000 pg/ml. The parallelism was 85–100% for samples diluted 2–8 times. Recovery was 99–109%. No neurogranin interference was detected (spike sample (1751 pg/ml); GAP-43 recovery was 101% of neat sample (1734 pg/ml)). All stability aliquots were analyzed simultaneously and their concentrations differed between 81 and 116% for samples with different storage conditions, and 75–115% for freeze-thaw cycled aliquots, compared to the original sample stored at − 80 °C.1,32 = 24.8, P < 0.0001, Fig. 1,32 = 11.7, P = 0.002, Fig. 31 = 2.4, P = 0.02, Fig. 31 = 2.6, P = 0.02, Fig. CSF GAP-43 concentrations in controls and change over time in stroke patients are depicted in Fig. 1,38 = 4.5, P = 0.04) and absolute GAP-43 concentrations also decreased significantly over the same period . Additionally, GAP-43 concentrations between 3 and 5 months were significantly lower compared to 3 weeks  and compared to Days 7–9 .Additionally, GAP-43 concentration decreased significantly starting 9 days after stroke relative to baseline  in Days 2–3 (mean: 118.1%) and increased greatly in Days 7–9 (mean: 187.1%), then began to decrease relative to Days 7–9 after 3 weeks (mean: 166.6%) and returned to initial levels after 3–5 months (mean: 89.8%). Continuous individual longitudinal trajectories of absolute GAP-43 concentration after stroke are shown in Fig. P = 0.007, Fig. P = 0.02, Fig. P = 0.58, Fig. P = 0.44, Fig. P = 0.73, Fig. Compared to cross-sectional measurements of healthy controls, absolute GAP-43 concentration in stroke patients was significantly elevated in Days 7–9 . However, there was no correlation between the change in GAP-43 concentration over time and the change of stroke severity , but not for change in GAP-43 concentration later on . However, there was no significant difference between GAP-43 changes for any two specific white matter lesion groups likely due to lack of statistical power .The change in GAP-43 concentration until 9 days after stroke significantly differed overall across white matter lesion status , but not for GAP-43 concentrations after 9 days . Again, there was no significant difference between GAP-43 changes for any two specific brain atrophy groups likely due to lack of statistical power , indicating that those with higher infarct size also had larger initial increase in GAP-43 levels. Meanwhile, the later decrease in GAP-43 concentration after 9 days correlated negatively with infarct size .The initial increase in GAP-43 concentration over the first 9 days correlated positively with infarct size  and the following increase during the first 9 days after stroke indicate that increased CSF GAP-43 results from a delayed or progressing process. Different mechanisms of ischemia-induced cell death are acutely activated in the ischemic core, appearing within a time-window of 0–2 days . In addiMoreover, our results suggest an association of CSF GAP-43 concentration and clinical characteristics during the first 2 weeks after ischemia. Larger increase in CSF GAP-43 was related not necessarily to a worsening in clinical characteristics but rather a lack of improvement, given that all but one individual either improved or stayed the same over the course of the study. This connection was even stronger when analyzing only those patients whose clinical characteristics actually improved. The connection to white matter lesions indicates that GAP-43 levels were likely to increase more in individuals with pronounced white matter lesions. Similarly, a high increase in CSF GAP-43 concentration was related to more severe atrophy and larger infarct size. Together with weaker, or the lack of, association with clinical measures at later time points, this suggests that CSF GAP-43 concentration primarily reflect the degree of, or response to, neuronal injury but not recovery. Limitations of this study include the relatively low sample number and the rather long sample storage time. The reason for the long sample storage time is that a stable CSF GAP-43 quantification method was just recently developed and this evaluation of CSF GAP-43 in longitudinal stroke samples was possible through a previous established collaboration. However, longitudinal studies have the advantage of letting each patient be its own control at baseline and individual changes over time should not be considerably affected assuming equal effects of storage on all samples. An additional limitation is the use of the modified version of the SSI, which is not standardized internationally. Nonetheless, we show for the first time that changes in CSF GAP-43 after stroke are connected to clinical measures. Additional studies are required to further evaluate the value of GAP-43 as a stroke biomarker and elucidate its connections to injury progression and clinical outcome. CSF GAP-43 could become useful as a quantitative measure of the regenerative response during recovery, which should simplify comparison and evaluation of both stroke treatments and rehabilitation strategies.Importantly, the results reported here, demonstrating a clear effect of ischemia on CSF GAP-43 concentrations, need to be considered when using CSF GAP-43 as a biomarker in other settings. In a previous study of several dementia disorders, we have shown a specific increase in CSF GAP-43 in Alzheimer’s disease compared to several other neurodegenerative diseases . As incrThe transient increase of CSF GAP-43 appearing most intensely at 1–3 weeks after stroke is important to take into account when used as a biomarker for other neurodegenerative diseases. Our findings also suggest that CSF GAP-43 may be a marker of neuronal injury responses in stroke and urge for additional studies confirming the potential of CSF GAP-43 to reflect severity and outcome of stroke in larger cohorts.Additional file 1:p values across the different white matter lesion status and brain atrophy status groups, up to 9 days and, after 9 days past the stroke. (XLSX 9 kb)Table of t and


A graphene-loaded metamaterial absorber is investigated in the mid-infrared region. The light-graphene interaction is greatly enhanced by virtue of the coupled resonance through a cross-shaped slot. The absorption peaks show a significant blueshift with increasing Fermi level, enabling a wide range of tunability for the absorber. A simple circuit model well explains and predicts this modulation behavior. Our proposal may find applications in a variety of areas such as switching, sensing, modulating, and biochemical detecting.The online version of this article (10.1186/s11671-019-2852-y) contains supplementary material, which is available to authorized users. Plasmonic metamaterial (PM) absorbers work with metallic nanostructures at deep subwavelength scale. Perfect absorptions can be achieved and tailored at particular wavelengths, leading to a variety of applications including light emitter/detector, sensor, photothermal therapy, optical-mechanical interaction, and hyperspectral imaging –7. PM abEF) which can be continuously tuned through bias voltage within several nanoseconds, enabling a high modulation rate in the near infrared and mid-infrared regions [Most recently, graphene has received considerable attention because of its high-speed modulation capability and tunability as a plasmonic material –20. Spec regions , 19–24.  regions . As a re regions , 25–28,  regions , 28, theIn this work, we have proposed a graphene-loaded absorber with modulation range from 9 to 14 μm, which is of great interest for applications such as biochemical sensing and thermal imaging , 29–31. td = 520 nm and that of the metallic patches is tm = 100 nm. Figure S1 and S2 are l1 = 1.5 μm and l2 = 1.7 μm, respectively. The slot width for both subunits is a = 20 nm. In our study, the metallic material is chosen as gold (Au), whose optical property is described by the Drude model of ωp = 1.369 × 1016 Hz and τ = 1.224 × 1014 Hz [n = 2.2 with negligible loss in the mid-infrared region [As shown in Fig.  1014 Hz . The died region .Fig. 1a x and y directions and perfect matched layer conditions in the z directions. The single graphene layer is modeled as a two-dimensional structure by the surface conductivity approach [σg, including the interband term σinter and the intraband term σintra, can be calculated by the Kubo formula [e and ξ are the charge and energy of the electron, ℏ is the reduced plank’s constant, ω is the angular frequency, T is the absolute temperature, Γ is the scattering rate, kB is the Boltzmann constant, and EF is the Fermi level. In our calculation, T = 300 K, and Γ = 10 meV [ε0 is the permittivity of vacuum, and tg is the thickness of graphene layer. Equations  method is employed to calculate reflectance spectra and electromagnetic field distribution. The simulations are carried out with periodic boundary conditions in the approach . The sur formula .1\docume= 10 meV . The mesentclass1pt{minimax-polarized wave (φ = 0) at the normal incidence. When the Fermi level is EF = 0eV, two absorption peaks are observed at the wavelength λ = 12.4 μm and 13.3 μm, respectively. The incident light ranging from 12.1 to 13.5 μm is almost absorbed by the nanostructure. As EF increases, the resonance moves toward shorter wavelength. At EF = 0.2 eV, the absorption peaks shift to 11.8 μm and 12.46 μm, indicating respectively a relative shift of 4.8% and 6%. Meanwhile, the absorbance of peak 2 declines, which is attributed to the impedance mismatch between the metamaterial and air at a higher EF [Figure igher EF . Here, iM = Δλ/λ0, where λ0 is the resonance wavelength at EF = 0 eV and Δλ is the wavelength shift due to the change of EF. Figure M1 = 20.1% and M2 = 25.5% for peak 1 and peak 2, respectively, when EF reaches 0.6 eV. The modulation range of resonances is much broader compared with previous works [EF is highly desirable for many applications. Separate calculations show that the absorption peaks blueshift with decreasing thickness of the spacer  structure , 36–38 sω =  − iσg∫S|Es|2dS/W0 [s|2is the intensity of the electric field in the graphene layer, W0 is the stored energy, and S denotes the area covered by the graphene. The spectral shift of the resonance (Re(Δω)) is decided by the imaginary part of σg, which is much greater than its real part in the mid-infrared region [E field in the patch slots, leading to a much bigger shift of the peaks than in previous cases which only possess the enhanced E fields at the metallic edges [Such field distributions well explain the reason why the modulation is so great in our proposal. Based on a perturbation theory, the graphene-induced shifting of resonance can be evaluated as Δs|2dS/W0 . Here, |d region , 28. As ic edges , 27, 28.Li and Ci  are, respectively, the inductance and capacitance for the patch Si in Fig. a is very big and there is no graphene layer, we can ignore the effects induced by the slots and graphene. Then, Li and Ci can be decided by separate calculations through fitting with the resonant wavelength obtained in absorption spectra [L1 = 0.07 pH and C1 = 350 aF for subunit S1, while L2 = 0.075 pH and C2 = 380 aF for subunit S2. The slot-induced coupling effect inside each subunit can be described by a shunt capacitance Cc, which is found to decrease with the increasing slot width a. In our cases, Cc is 290 aF for a = 20 nm, and becomes 200 aF, 180 aF, and 135 aF with every increasing 10 nm of a. The resonance wavelength is obtained by letting the impedance of the circuit to be zero, i.e., c0 is the light speed in vacuum, “i” refers to subunit Si, and According to the field distributions and above discussions, an LC circuit model is proposed to study the tuning behavior. As shown in Fig. d Ci i = , 2 are,  spectra , 39, 40.V and the current I across the slot can be evaluated by V = aE andI = 2litg(σg − iωε0)E, where E is the electric field in the graphene layer. So, we can introduce an inductance Lg =  − 1/ω Im(V/I) [The two-dimensional graphene layer basically acts as an inductor. As shown in Fig.  Im(V/I) , which dThis inductor serves as a parallel element shown in Fig. EF. Deduced from Eqs. (εg)| for the graphene at a higher EF, which gives a smaller Lg in Eq. ,,EF. Dedug in Eq. . Becausee in Eq. . The cale in Eq. c–f. The e in Eq. , we haveg to Eq. . Figure EF increases from 0 to 0.6 eV. Such a large modulation comes from the graphene-light interaction tremendously enhanced by the coupled-resonances inside the cross-shaped slot of each metallic patch. This effect is well described by a graphene-introduced inductor in the LC model. Such a simple model predicts the modulation behavior under different geometric parameters, and the results agree well with the FDTD simulations. Our proposal is beneficial to potential applications such as optical communication, sensing, and thermal imaging.In conclusion, we have designed a polarization-independent, broadband metamaterial absorber with a large range of modulation. For both resonances, the tuning range reach up to 20.1% and 25.5% of the central wavelength when Additional file 1:Supplementary Material. (PDF 308 kb).


The SEM analysis indicates that the texture of RhNPs@f-graphene resemble the dispersion of water droplets on lotus leaf. TEM analysis indicates that RhNPs of <10 nm diameter are dispersed on the surface of f-graphene. The air-stable NPs and nanocomposites were used as electrocatalyts for conversion of acidic water to hydrogen gas. The composite RhNPs@f-graphene catalyses hydrogen gas evolution from water containing p-toluene sulphonic acid (p-TsOH) at an onset reduction potential, Ep, −0.117 V which is less than that of PtNPs@f-graphene  under identical experimental conditions whereas the onset potential of CoNPs@f-graphene was at Ep, −0.97 V and the FeNPs@f-graphene displayed onset potential at Ep, −1.58 V. The pure rhodium nanoparticles, RhNPs also electrocatalyse at Ep, −0.186 V compared with that of PtNPs at Ep, −0.36 V and that of CoNPs at Ep, −0.98 V. The electrocatalytic experiments also indicate that the RhNPs and RhNPs@f-graphene are stable, durable and they can be recycled in several catalytic experiments after washing with water and drying. The results indicate that RhNPs and RhNPs@f-graphene are better nanoelectrocatalysts than PtNPs and the reduction potentials were much higher in other transition metal nanoparticles. The mechanism could involve a hydridic species, Rh-H− followed by interaction with protons to form hydrogen gas.Current research on catalysts for proton exchange membrane fuel cells (PEMFC) is based on obtaining higher catalytic activity than platinum particle catalysts on porous carbon. In search of a more sustainable catalyst other than platinum for the catalytic conversion of water to hydrogen gas, a series of nanoparticles of transition metals  Hydrogenases are enzymes that catalyze the interconversion of H2 and its constituents, two protons and two electrons as shown in Eq. ] cubane type sub-cluster obtains electrons from pyruvate oxidation and transfer those electrons to the [FeFe] site which utilizes them and form hydrogen gas in an unique reaction mechanism. The mechanism involves the replacement of the labile water ligand shown in Fig. 299) at the vicinity of the [FeFe] cluster1.The three known classes of hydrogenases, [NiFe]-, [FeFe]- and FeS-cluster free hydrogenases contain iron at their active sites which are coordinated by thiolates, CO, CN2 gas generation is the conversion of electricity to H2 gas in the presence of a catalyst that can performance-wise replace the platinum electrode which functions at Ep, −0.413 V at a pH value of 7.08. Iron-containing electrochemical catalysts that are known to date include mononuclear iron(II) complexes, [FeII(S2C6H4)(CO)2(PMe3)2], [FeII(S2C6H2Cl2)(CO)2(PMe3)2], an FeS cluster with Fecubane(μ-SR)Fesubsite and [Fe2(CN)(CO)4H(PMe3)(S2C3H6)] which function at high reduction potentials, Ep ≈ −1.0 V16. However, several electrochemical catalysts that contain other transition metals are known, e.g. carbon nanotube supported [Ni(PPh2NPh2)2(CH3CN)][BF4]2, (CpMoμ-S)2S2CH2, cobalt macrocyclic glyoxime/tetraimine complexes and a nickel(II) dithiolene complex which function at considerably lower reduction potentials. Several other electrochemical hydrogen evolution catalysts have also been reported including metalloporphyrins, low-valent transition metal complexes forming hydrides upon reaction with acids, mononuclear iron(II) complexes, cobalt-dithiolene complexes and a nickel complex [Ni(PPh2NPh)2][BF4]2  which electrocatalyze H2 production with high turnover frequencies but at significantly high reduction potentials Ep > −1.1 V24. Various kinds of heterogeneous non-precious metal based electrocatalysts for hydrogen evolution reaction (HER) and oxygen reduction reaction (ORR) including metal sulfides, selenides, carbides, nitrides, phosphides, three dimensional porous carbon nanostructures and heteroatom doped nanocarbons have been reviewed27. Multilayer thin films of metal nanocrystals and graphene quantum dots have been prepared by layer-by-layer assembly approach and these composites demonstrate efficient and versatile electrocatalytic performances such as reduction of aromatic nitro compounds, methanol oxidation and water splitting28. Plasmonic TiO2 NRs@Ag@GQDs ternary heterostructures which have been prepared by a layer-by-layer assembly strategy combined with an in situ light irradiation display an enhanced photoelectrochemical water splitting performance29. Pd-CdS nanowire heterostructures display remarkable photocatalytic reduction of nitroarenes and photocatalytic hydrogen production under visible light irradiation30. Our research focused towards the electrochemical hydrogen evolution resulted in a mononuclear iron(III) dithiolene of severely distorted square pyramidal geometry and a nickel(II)-sulfur based radical ligand complex that catalyze electrochemical hydrogen gas evolution at lower potentials in CH3CN. In these cases, along with metals, the dithiolene ligands are also potentially redox active forming sulfur based radicals36.Although nature uses the potential redox active element “iron” in this active site, the hydrogen gas electrode is based on platinum metal. Electrocatalytic H3Ni(111) surface is 90-fold more active for ORR than the current platinum catalyst on porous carbon used in PEMFC37. Core-shelled TiC@TiO2 has been shown to be a promising catalyst support for proton exchange membrane fuel cells (PEMFCs). TiC is thermally stable with low solubility in sulfuric acid and high electronic conductivity. Both these materials are used as supports for platinum and platinum–palladium alloy catalysts  and their catalytic activity toward ORR are much higher than those for Pt/TiC38. Earth abundant transition metal nanocatalysts that include Mn, Fe, Co, Ni and Cu and early transition metals such as Ti, V, Cr, Zr, Nb and their nanocomposites for reduction of various aromatic compounds have been reported39. Synthesis of different types of graphene-based composite photocatalysts and their applications in reduction of CO2, nitroarenes, methanol oxidation, elimination of pollutants and photochemical water splitting have been reviewed40. Au-Pd nanoalloys supported on graphene (Au-Pd/GR) have been reported which display higher photocatalytic performance than the monometallic, GR supported nanoparticles towards degradation of dye, rhodamine B (RhB)41. Basic principles of photocatalytic water splitting, engineering strategies for photocatalysts optimization, and promising photocatalytic materials for water splitting have been recently reviewed42. Various metal oxides, metal non-oxides and non-metal catalysts for oxygen evolution reaction (OER) have also been recently reviewed43. There are several reports on the catalytic activities of rhodium nanoparticles (RhNPs) and rhodium nanoparticles on polymer, graphene and carbon nanotube matrices for the reduction of aromatic compounds, amino boranes as a means of H2 storage. But there are no reports on the application of RhNPs and graphene supported RhNPs (RhNP@f-graphene) as electrocatalysts for the generation of hydrogen gas from water. Here in we report preparation, characterisation and electrocatalytic properties of rhodium nanoparticles (RhNPs) and graphene supported rhodium nanoparticles (RhNPs@f-graphene) which display better electrocatalytic performance than the platinum nanoparticles (PtNPs) and graphene supported platinum nanoparticles (PtNPs@f-graphene) under similar experimental conditions.It has been demonstrated that Pt1–3) are given in methods. The metal halides were reduced by NaBH4 in water at laboratory temperature (30 °C) under high dilution conditions. The black colored particles which precipitated out immediately were sonicated, centrifuged and isolated after 12 h. The transition metal nanoparticles  were insoluble in water, they were sonicated in water and deposited on to polymer coated carbon grids and aluminum stubs for transmission electron microscopic (TEM) and scanning electron microscopic (SEM) analysis respectively. The TEM analytical results of the rhodium nanoparticles are shown in Fig. The detailed synthetic procedures for the preparation of the three nanoparticles RhNPs, PtNPs and CoNPs, (31. Graphitic powder was sonicated in tetrahydrofuran (thf) for 1 h followed by centrifugation. The supernatant thf layer was decanted and the residue was washed with acetone and dried thoroughly which was heated under reflux in a mixture of H2SO4 and HNO3. The composites of transition metal nanoparticles and functionalized graphene were obtained by sonication of both in acetonitrile. The solvent was evaporated and the residue was washed with acetone and then finally dried. The composites were characterized by TEM and SEM methods. The TEM images of the RhNP@f-graphene are given in Fig. 44. The TEM images of the platinum nanoparticles (PtNPs) and cobalt nanoparticles (CoNPs) are displayed in Supplementary Information whereas the TEM images of the f-graphene composites of PtNP and CoNP are displayed in Fig. 45.The functionalized graphene was obtained from graphitic powder by following a previously reported procedurep-TsOH. Glassy carbon working, platinum wire auxillary and Ag/AgCl reference electrodes were used. In a typical experiment, nanoparticles (0.02 g) were taken in distilled water (8 ml) in an electrochemical cell, with potassium nitrate (0.1 mM) as a supporting electrolyte. Aqueous solutions of p-TsOH were added to the electrochemical cell and the cyclic voltammograms were recorded as a function of increasing concentrations of p-TsOH. The results of electrocatalytic experiments using RhNPs and RhNP@f-graphene as the catalysts are displayed in Figs p-TsOH (0.2 ml of 50 mM) a reduction signal was observed at Ep, −0.67 V  and p-TsOH  in water with KNO3 (0.2 M) as supporting electrolyte was purged with nitrogen gas for 15 min. A 2 ml syringe was inserted into the electrochemical cell and the argon gas supply inlet was closed. The reaction mixture was subjected to controlled potential electrolysis at −0.6 V. The gas bubbles formed at the glassy carbon working electrode surface were tapped off to the surface and the gas over the solution was taken into a syringe which was analysed by gas chromatography and confirmed to be H2 gas. Cyclic voltammograms of functionalized graphene without electrocatalysts were recorded as a function of addition of aqueous solutions of p-TsOH and the results are displayed in Fig. p, −1.7 V but the onset potentials were observed at Ep, −1.1 V. The reduction of pure p-TsOH in water in the absence of RhNPs or RhNPs@f-graphene was observed at a higher negative potential, Ep, −1.4 V vs. Ag/Ag+.The transition metal nanoparticles and their f-graphene composites were tested for electrocatalytic efficiency and hydrogen gas generation by cyclic voltammetry. The electrocatalysis was conducted in acidic water containing 2-type) of boding and protons could also bind to rhodium leading to the formation of Rh-H+ species. This could be reduced upon application of electrode potential to a hydridic species, Rh-H− as shown in Fig. 60) in an η2-mode viz., (η2-C60)Rh(CO)H(PPh3)2 have been reviewed46. This Rh-H− species could interact with further protons and could form hydrogen gas. In the process, rhodium nanoparticles remain unaffected and provide site for fixing protons which in other words, an adsorption medium. The rhodium metal nanoparticles could play an important role in bringing down the reduction potential, Ep, because the reduction of protons and evolution of hydrogen gas takes place at higher reduction potentials when CoNPs  and FeNPs  were used as electrocatalysts. This might be due to the ease of formation and optimum stability of Rh-H− species.The SEM analytical results of the composite, RhNPs@f-graphene Fig.  indicate3 supporting electrolyte and p-toluene sulfonate used in the cyclic voltammetric experiment. The electrocatalysts, RhNPs and RhNPs@f-graphene were recycled as follows: After the cyclic voltammetric experiment, the RhNPs and RhNPs@f-graphene were filtered and were sonicated in water (50 mL) for 30 min. Then they were centrifuged and the water layer was decanted. The process was repeated for 6 times in order to get rid of KNO3 and p-toluene sulfonate and the recycled and purified RhNPs and RhNPs@f-graphene were reused as the electrocatalysts. The recycled RhNPs were found to electrocatalyze further generation of hydrogen gas by addition of p-TsOH (0.8 g) and the current again exceeded 10 mA. This proves that the RhNPs and RhNPs@f-graphene have excellent durability and electrocatalytic efficiency.After the cyclic voltammetric experiments, the RhNPs and the RhNPs@f-graphene were filtered from the reaction mixture and were analysed by SEM and TEM techniques. The results obtained in the case of RhNPs are shown in Supplementary Information which indicate the presence of mixtures of large crystals along with the RhNPs. The large crystals could be KNOp, −0.367 V  and their composites with f-graphene  have been prepared and characterized by SEM, TEM and p-XRD analytical methods. The nanoparticles were mostly aggregated and segregate upon composite formation with f-graphene. The RhNPs immobilized on f-graphene resemble the dispersion of water droplets on a lotus leaf. The RhNPs and the composite RhNPs@f-graphene electrocatalyze hydrogen evolution from acidic water at much lower potentials than that of PtNPs and PtNPs@f-graphene under identical experimental conditions. The reduction onset potentials for RhNPs and RhNPs@f-graphene were found to be E3.3H3O or anhydrous PtCl2 or CoCl2.6H2O or anhydrous FeCl3 (0.05 g) was stirred in distilled deionized water (30 mL) at 30 °C for 5 min. NaBH4 (0.02 g) was added to the reaction mixture in portions carefully. A bright red colored solution was obtained in the case of RhCl3.6H2O which became colorless after the precipitation of black particles. After stirring for 8 h, the black precipitate was collected by filtration. The precipitate was washed with water (10 mL), sonicated with acetone (25 mL), centrifuged and the acetone layer was decanted. The black precipitate thus obtained was dried and weighed .The metal halides, RhCl2SO4 (30 mL) and fuming nitric acid (10 mL) at 0 °C. The reaction mixture was heated under reflux for 12 h and allowed to stand at 38 °C for 10 h. The supernatant acid layer was decanted and the residue was washed thoroughly with water by centrifugation and dried. FT-IR : 3400 (br), 1714 (br.), 1600 (w).Graphite powder (0.5 g) was taken in THF (80 mL) and water (20 mL), stirred at 38 °C for 2 h and ultra-sonicated for 10 h. The solvents were decanted after centrifugation and the residue was dried thoroughly. This residue was treated carefully and dropwise with concentrated H3CN (12 mL) and ultra-sonicated in closed sample vials under argon atmosphere for 3 h. The reaction mixtures were then evaporated to dryness and the residues were used in catalytic experiments.RhNPs/PtNPs/CoNPs (0.02 g) was mixed with functionalized graphene (0.02 g) in degassed LC-MS grade CHESI