Graphene plasmon resonators with the ability to support plasmonic resonances in the infrared region make them a promising platform for plasmon-enhanced spectroscopy techniques. Here we propose a resonant graphene plasmonic system for infrared spectroscopy sensing that consists of continuous graphene and graphene ribbons separated by a nanometric gap. Such a bilayer graphene resonator can support acoustic graphene plasmons (AGPs) that provide ultraconfined electromagnetic fields and strong field enhancement inside the nano-gap. This allows us to selectively enhance the infrared absorption of protein molecules and precisely resolve the molecular structural information by sweeping graphene Fermi energy. Compared to the conventional graphene plasmonic sensors, the proposed bilayer AGP sensor provides better sensitivity and improvement of molecular vibrational fingerprints of nanoscale analyte samples. Our work provides a novel avenue for enhanced infrared spectroscopy sensing with ultrasmall volumes of molecules. With the ability to probe the vibrational characteristics of inorganic, organic, and polymer compounds samples, infrared spectroscopy possesses the advantages of rapid testing, convenient operation, good repeatability, high sensitivity, and less sample consumption ,2. The bTo address this problem, various types of strategies have been proposed to enhance the absorption signal of molecules. Among these strategies, plasmonic enhancement is one of the most effective approaches to improve light–molecule interaction in the mid-infrared range ,9,10. CoIn this work, we proposed a resonant graphene plasmonic system to far-field excite AGP mode between a bilayer graphene gap for ultra-sensitive molecular infrared spectroscopy sensing. Here the AGPs are excited in the graphene/nano-gap/graphene plasmon cavity instead of the graphene/nano-gap/metal plasmon cavity. The excitation efficiency of AGPs is improved by placing a reflection mirror below the graphene gratings to form an F–P resonant cavity in the vertical direction. In this bilayer graphene plasmonic biosensor, the tightly confined AGP mode inside the bilayer graphene gap provides strong coupling between ultra-compressed mode-volume plasmon cavity mode and molecular vibrational fingerprints mode, resulting in the significant enhancement of the molecular infrared spectroscopy. For illustration, the protein molecules are selected as the sensing analytes in order to evaluate and analyze the biosensing performance of this bilayer AGPs sensor platform. By adjusting the Fermi energy of graphene via external gate voltage, the AGPs’ resonant wavelength can be actively tuned over the infrared region, which enables us to sensitively detect the absorption band of ultra-thin target molecules.p = 82 nm, a width of graphene grating w = 42 nm, and a thickness of nanogap g = 3 nm as shown in e is the elementary charge, ℏ is the reduced Planck constant, c is the speed of light in vacuum, and x-direction and that in the y-direction is infinitely. The refraction index of silicon spacer layer is The ultra-compressed mode-volumes graphene plasmon cavity are formed by graphene/ nano-gap/graphene van der Waals heterostructure with a period of  formula ,33(1)σ(We first investigate the excitation of graphene acoustic plasmons in the proposed bilayer plasmonic system. Under the illumination of transverse magnetic (TM) waves, graphene plasmons can be excited with the assistance of the patterned graphene nanoribbons. According to the F–P resonant model, the resonance condition of such graphene plasmonic modes can be given as following equation:m represents the order of the resonance, Re means taking the real part of the complex quantity, and Here, r system .In order to improve the conversion efficiency of graphene acoustic plasmon, the dielectric spacer layer and gold mirror are further introduced to form an F–P resonance cavity ,34 to eng = 3 nm thickness protein coating over a lossless silicon dioxide substrate is plotted by red line in this picture, and the maximum value of absorption peak are 0.11% and 0.078%, indicating the positions (dash line) of the amide I band  feature of protein vibrational modes after plasmonic enhancement with varying graphene Fermi energy is further extracted and plotted as a function of wavelength in  sensors .To illustrate the advantage of tunable bilayer AGP biosensor, we finally compare the enhancement performance of the proposed bilayer AGP system to that of single-layer GP biosensor by removing top continuous graphene sheet, as shown in The extinction spectra of the GP sensor and AGP biosensor with varying widths of graphene ribbons are shown in  of 4 nm b. While  of 2 nm e. It is Our proposed structure can be fabricated with a similar process as previous literature ,30,34. TIn conclusion, we have suggested a method of probing vibrational fingerprints of extremely ultra-small volume molecules with a bilayer graphene acoustic plasmons biosensor, which is formed by continuous ultra-compressed mode-volume graphene-insulator-graphene plasmon cavity. Compared to graphene-gap-metal AGP structures, the proposed design is expected to solve the difficulty of metal unevenness or surface roughness which significantly attenuates far-field signals and requires a complex fabrication process to realize ultraflat metal surfaces. Furthermore, we have found that strong absorption and tight electromagnetic field confinement are realized in mid-infrared frequency for such a biosensor. The acoustic plasmons modes are squeezed into the nano-metric gap by far-field excitation with high efficiency at normal incidence. By placing protein molecules inside bilayer graphene nano-gap, the molecular fingerprints can be frequency-selectively enhanced by extreme plasmon overlap. The extinction spectra and the absorption band of thin protein molecules can be precisely resolved by sweeping the graphene Fermi energy and extracting plasmon-enhanced excitation peak signals. The maximum enhancement factor can be up to 510 for the amide I band and 560 for the amide II band of enhanced molecular absorption IR spectroscopy. We further demonstrate that the sensitivity of the AGP biosensor is much higher than that of the conventional GP biosensors due to highly confined plasmon mode overlap (<3 nm) and ultra-strong interaction of light and biomolecules. Our proposal provides a new platform to study nanoscale light-matter interactions and ultrasensitive infrared spectroscopy for single-molecule optics, extreme biosensing ,41, and


UV–Vis spectroscopy, transmission electron microscopy (TEM), zeta potential , and X-ray photoelectron spectroscopy (XPS) were used to characterize the AuNPs and nanocomposites. The addition of reduced graphene oxide (rGO) resulted in greater dispersion of AuNPs on the rGO surface compared with carbon-based nanomaterials used as a support. Differences in morphology due to the nature of the carbon support were observed and are discussed here. AuNPs/rGO seem to be the most promising candidates for the development of LSPR biosensors among the three composites we studied . Simulations based on the Mie scattering theory have been used to outline the effect of the phytochemicals on LSPR, showing that when the presence of the residuals is limited to the formation of a thin capping layer, the quality of the plasmonic resonance is not affected. A further discussion of the application framework is presented.Aiming to develop a nanoparticle-based optical biosensor using gold nanoparticles (AuNPs) synthesized using green methods and supported by carbon-based nanomaterials, we studied the role of carbon derivatives in promoting AuNPs localized surface plasmon resonance (LSPR), as well as their morphology, dispersion, and stability. Carbon derivatives are expected to work as immobilization platforms for AuNPs, improving their analytical performance. Gold nanoparticles (AuNPs) were prepared using an eco-friendly approach in a single step by reduction of HAuCl Plasmonic biosensors are widely explored as promising sensing tools due to their low cost, simplicity, and short response time. These devices can be useful in a variety of situations, such as medical emergencies in more isolated populations or/and with poor access to health services . For theGold nanoparticles (AuNPs) have been widely used in the development of optical biosensors . Their uWhen AuNPs are used as optical transducers, the region of confinement in which the evanescent wave is propagated is smaller than the wavelength of the incident light. In this case, the phenomenon is called localized surface plasmon resonance (LSPR), where the collective oscillation of the electrons originates a dipolar or multipolar moment in the nanoparticle. LSPR is excited at a specific light wavelength, which is determined by the morphology, size, and composition of the nanoparticle . Any modWhen the analyte of interest is recognized by the BRE, the surface refractive index of the AuNPs changes, promoting a shift in the LSPR wavelength .The interest in AuNPs for the development of biomedical diagnostic devices is related to their sensitivity and the possibility of controlling and optimizing the limit of detection (LOD) .2sp hybridization [Moreover, these interesting properties may act synergistically with graphene when this carbon nanomaterial is added to form a nanocomposite . For exadization , can be The oxidation of graphene, yielding graphene oxide (GO), allows the addition of oxygen functional groups to its surface, reducing electrical conductivity and malleability, and increasing solubility in polar solvents, as well as reducing the aggregation of carbon-based nanomaterials in aqueous solutions.Graphene oxide shows a greater functionalization capacity due to the presence of a larger number of oxygen groups, but for the same reason there is also restriction of the mobility of electrons on its surface. Reduction of the number of oxygen groups, yielding reduced graphene oxide, allows the restructuring of electrical and thermal conductivity towards pristine graphene, maintaining functionalization capacity and the distance between graphene sheets . The mai4) [In this work, the influence of a variety of carbon-based materials on gold nanoparticles’ (AuNPs) localized surface plasmon resonance (LSPR) has been studied and, considering environmental concerns, an ecofriendly approach in the synthesis of gold nanoparticles was chosen, using tea extract as a reducing/capping agent. As a matter of fact, the phytochemicals present in tea, such as polyphenols and tannins, have the interesting capacity of reducing metallic salts, such as chloroauric acid (HAuCl4) ,11,12.Although the use of composite materials based on metal nanoparticles and graphene allotropes to explore plasmonic properties in sensing applications has already been reported in the literature ,14, a grSensors based on the local surface plasmon resonance of metal nanoparticles are characterized by simple structures and good sensitivities. Despite the good sensing properties of LSPR structures, full commercialization has been prevented by the production costs associated with the bio-functionalization and the high-precision systems necessary to extract the optoelectronic output. There is a great interest in new strategies for bringing the excellent detection properties of LSPR sensors into play in low-cost devices made with low-cost materials . The comThe main advantages of these nanomaterials are their biocompatibility, high chemical stability, and high surface areas. The reduced dimensions and weight, high resistance, simplicity of use, and low cost makes graphene and its derivatives excellent candidates for the development of biosensors.An optical sensor has, by definition, the ability to convert an external stimulus into an optical output signal.The π–π binding capacity of graphene with biomolecules allows the use of this carbon-derived nanomaterial as a substrate, and the SPR technique can be used to detect the interaction with the analyte of interest, in which the angle of an incident polarized light is adjusted as a result of the change in the refractive index caused by the interaction between them. rGO has advantages over graphene, since its oxygen functional groups improve the interaction of this nanomaterial with biomolecules .Optical sensors, such as those based on fluorescence, surface-enhanced Raman scattering (SERS), optical fiber biological sensors, among other kinds of optical sensors, are being developed due to the amazing properties of graphene and its derivatives. Various sensing applications, such as single-cell detection, cancer diagnosis, and protein and DNA sensing, have been reported in recent years .Due to its unique optical and electrical properties, graphene and its derivatives are widely used in photonic and optoelectronic devices as they have displayed several ideal properties, including broadband light absorption, the ability to quench fluorescence, excellent biocompatibility, and strong polarization-dependent effects, making them very popular platforms for optical sensors. Graphene and its derivatives-based optical sensors have numerous advantages, such as high sensitivity, low-cost, fast response time, and small dimensions. The use of metallic nanoparticles, namely, gold nanoparticles, may improve the response of biosensors, amplifying the signals obtained and increasing the sensitivity of these devices .Several applications of graphene and its derivatives-based optical sensors are summarized in the following It is undeniable that there are still many challenges in this area. The need to synthesize high-quality graphene, to achieve a low-cost, environmentally friendly method for synthesizing graphene and its derivatives are issues still to be addressed. With this study, the use of a simple green method to produce light-responsive material is our aim and contribution.Addressing the above challenges, we hope to show the potential of green methods as well as the importance of graphene and its derivatives in the development of optical sensing technologies, which will ultimately increase the quality of future life.4·3H2O) to a tea extract (5% w/w), a change of color from yellow to red was observed, which is consistent with the formation of AuNPs. The AuNPs’ characteristic LSPR was observed only after one week (t1w), the absence of LSPR at the initial time (t0) possibly being associated with the period of reduction required to originate AuNPs or due to the fact that in the case of hybrid nanostructures, carbon materials may be responsible for radiation absorption, inhibiting the detection of AuNPs, as described by Biris et al. [After the addition of chloroauric acid  and second week (t2w) after synthesis no significant differences in LSPR response was observed, confirming that polyphenols guarantee a certain stability to the synthesized AuNPs. As proven previously by the authors of [The stability of the synthesized nanoparticles was evaluated. We found that between the first  (LSPR = 541 nm) and reduced graphene oxide (rGO) (LSPR = 542 nm). The composite containing graphene oxide (AuNPs/GO) did not show any LSPR shift after the addition of carbon-based nanomaterials. For the AuNPs/G hybrid nanostructure, band amplitude decreased by ~10% when compared to the sample without any of the carbon-based nanomaterial, while for AuNPs to AuNPs/rGO, it increased by ~0.29, which corresponds to 46% of the naked/unsupported AuNPs’ absorbance. This result may be ascribed to a dispersion of the AuNPs [he AuNPs  in the phe AuNPs .LSPR properties depend not only on the AuNPs’ shape, size, and dispersion but also on the surrounding dielectric constant. TEM characterization showed the formation of spherical AuNPs . Althoug0, we assumed that the nucleation of metallic salts occurred near the rGO functional oxygen groups [The probable formation of π–π bonds between AuNPs and the functional oxygen groups of rGO  may be rn groups  which man groups .π–π interactions between AuNPs and functional oxygen groups of rGO; (ii) π–π interactions between benzene rings of the polyphenols and the surface of rGO; and (iii) electrostatic interactions between the OH on the surface of rGO and the polyphenols capping the AuNPs. After mixing, the shape of the AuNPs appeared to be the same when observed by TEM (The reason for using a pre-prepared solution of AuNPs stabilized with polyphenols then mixed with rGO is to help anchor the polyphenol-protected AuNPs to the rGO surface. This process could be assisted by: (i) d by TEM , but thed by TEM , the intIn the case where GO is added to the AuNPs, no significant deviation in the LSPR occurs relative to the value observed prior to the addition of this carbon-based material (λ = 556 nm). TEM images  show a gAfter the addition of both GO and rGO, sharper resonance bands as well as an increase in the resonance intensity or amplitude were observed . In factThe partial reduction of oxygen groups in GO driven by tea polyphenols  may contWe also measured the electrostatic potential at the electrical double layer surrounding a nanoparticle in solution, commonly referred to as the zeta potential. Nanoparticles with a zeta potential between −10 and +10 mV are considered approximately neutral, while nanoparticles with zeta potentials of greater than +30 mV or less than −30 mV are considered strongly cationic and strongly anionic, respectively . Since mpω) of the free electron gas depends linearly on the density of the electrons (Ne). Such a consideration leads, in the Drude model, to a linear dependence of the metal dielectric function on the value of Ne. Within the dipole approximation, i.e., when the nanoparticle size is much smaller than the light wavelength, and under the Fröhlich condition, it can be shown that the LSPR frequency (LSPRω) can be directly related to pω by the following equation:mε is the dielectric constant of the surrounding medium [According to the classical physical theory for the electromagnetics of metals, the plasma frequency  and 2 weeks (t2w) after synthesis. 1w vs. t0). These data show that the synthesis of the nanoparticles did not cease after 20 min of vigorous stirring of chloroauric acid with the tea extract. This shift could mean that the diameters of the AuNPs increased between t0 and t1w. As mentioned above, the increased band amplitude after a week suggests that there was an increase in the concentration of nanoparticles, indicating that the formation of AuNPs was still happening [The stability of the synthetized nanoparticles was also evaluated by the sample’s characterization, 1 week (tappening .2w) showed that LSPR did not shift in comparison to that observed after 1 week (t1w)  . The pol1w and t2w, we can observe a slight increase in the absorbance value without a change in the behavior of the plasmonic response. Tea contains a multitude of different chemicals. Some of these, e.g., tannins, are fairly dark to begin with, but, if they are allowed to react with the oxygen [1w and t2w is more obvious in the 5%_AuNPs/GO in comparison with the 5%_AuNPs/rGO sample, since GO has more oxygen groups on its surface.Between te oxygen  in the ae oxygen , creatin4 with the phytochemicals present in tea extract as a reducing agent, we have considered the gold nanosphere capped by a thin uniform layer of tea polyphenols, immersed in pure water. This approach agrees with the morphology observed in The variation in the transmission spectra caused by the plasmonic resonance of the nanoparticles can be calculated by recourse to the Mie Theory , and theThe effect of increasing the thickness of the cover layer results in the LSPR shifting towards the red region. Simultaneously, the intensity of the LSPR shows a marked reduction as the capping thickness increases.We can conclude from this simulation analysis that the residual polyphenols which remain after AuNP fabrication have a negligible effect on the quality of the plasmonic response. An excessive accumulation of the residual polyphenols on the AuNP surface would reduce the LSPR intensity, but the simulation shows that for the range of the capping thickness observed in the TEM images, such a level of EGCD accumulation is not reached. The red shift foreseen by the simulation has no impact on the operation mechanism of a sensor device built with these materials. At the same time, the presence of the capping layer can be expected to physically separate the nanoparticles, preventing their aggregation. Thus, AuNPs synthesized by this green method combine the advantage of a simplified fabrication method that avoids aggregation with a reliable plasmonic resonance that can be successfully exploited in a sensing device.The wavelength and intensity of LSPR are strongly dependent on the refractive index of the surrounding medium. An alteration of this parameter provokes a shift of the LSPR peak which can be used as the output value of a sensing system. To evaluate the sensing efficiency of the AuNPs, the spectral shift (Δλ) of the resonance wavelength, as a function of the variation in the refractive index (Δn) of the surrounding medium, can be translated into a sensitivity parameter (S):4·3H2O) was purchased from Sigma-Aldrich ; the black tea leaves (Thea sinensis) for the tea extract preparation used as a reducing agent for chloroauric acid was purchased from Pingo Doce ; graphene [Chloroauric acid  with a 6 mL total volume. This mixture was stirred for 20 min at 400 rpm at room temperature. After that, 1 mg of graphene or derivative was added to every 2 mL of the samples previously obtained (Sequence 1 (SQ1): 1 mL of chloroauric acid (0.1 M) was added to each tea extract obtained a.Sequence 2 (SQ2): The same weight of graphene or derivatives was added before the addition of chloroauric acid b. The re0, t1w and t2w, to determine the stability of the AuNPs and the nanocomposites over time, t0 being the starting moment, immediately after stirring the samples, t1w one week after, and t2w two weeks after. All samples were preserved in cold conditions and protected from light. TEM images were obtained between t0 and t1w, as was the zeta potential of the samples.Both nanoparticles and nanocomposites were characterized by UV–Vis spectroscopy  and transmission electron microscopy  with a ThermoNoran EDS light and zeta potential . The characterization by UV–Vis was undertaken at 3 distinct moments, t2 C-C and C-H peak set at 284.4 eV [Modified and unmodified graphene were analyzed by X-ray photoelectron spectroscopy with a XSAM800 spectrometer from KRATOS. Non-monochromatic radiation from a Mg Kα source was used (hν = 1253.6 eV). Powdered samples were fixed on the XPS holder with a double face tape and analyzed at UHV, at TOA = 45°. The BE was corrected considering the charge shift observed for the sp284.4 eV . Other o284.4 eV .Thea sinensis) showed reducing capacity of chloroauric acid, allowing the synthesis of spherical gold nanoparticles. The production of AuNPs through this green synthetic approach proved to be sustainable, not only due to its low cost but also because of the reducing capacity of the tea and its coating agent function, conferring stability to the synthesized AuNPs. We found that 2 weeks after stirring of the reagents, polyphenols prevailed as coating agents, sustaining the stability of the AuNPs. Simulation results based on the Mie theory for the LSPR effects support the conclusion that, even with a thin capping layer of residuals, no significant reduction of plasmonic resonance should be expected.The black tea extract (The addition of carbon-based nanomaterials before stirring of the tea extract with chloroauric acid proved not to be efficient with respect to the stability of synthesized AuNPs, and, in some cases, LSPR did not take place at any of the moments of characterization. The reverse order of material addition, i.e., the addition of carbon-based nanomaterials after the stirring of the tea extract with metallic salt, proved to be more efficient, both in terms of the synthesis and stability of the AuNPs.Of the three materials studied, rGO proved to be the most efficient carbon-based nanomaterial used as a support for AuNPs to be applied in biosensors. The stability revealed by the zeta potential, the greater dispersion of AuNPs, and the conductivity of this nanomaterial reported by several authors support this statement. Khalil et al.  corrobor0 and t1w so that we can determine when this reaction ceases. In addition, trying other brands of black tea could supply additional valuable information, since different conditions, both soil and climatic, may be associated with different antioxidant properties.The topic of this paper has not been widely explored in the literature and it will be necessary to carry out more exhaustive research in order to reach conclusions about certain questions raised in our study. Nevertheless, we believe that this work can be a good starting point for this investigation. Regarding future work, it would be interesting to monitor the synthesis of AuNPs between t


Four N-doped graphene materials with a nitrogen content ranging from 8.34 to 13.1 wt.% are prepared by the ball milling method. This method represents an eco-friendly mechanochemical process that can be easily adapted for industrial-scale productivity and allows both the exfoliation of graphite and the synthesis of large quantities of functionalized graphene. These materials are characterized by transmission and scanning electron microscopy, thermogravimetry measurements, X-ray powder diffraction, X-ray photoelectron and Raman spectroscopy, and then, are tested towards the oxygen reduction reaction by cyclic voltammetry and rotating disk electrode methods. Their responses towards ORR are analysed in correlation with their properties and use for the best ORR catalyst identification. However, even though the mechanochemical procedure and the characterization techniques are clean and green methods , they are time consuming and, generally, a low number of materials can be prepared, characterized and tested. In order to eliminate some of these limitations, the use of regression learner and reverse engineering methods are proposed for facilitating the optimization of the synthesis conditions and the materials’ design. Thus, the machine learning algorithms are applied to data containing the synthesis parameters, the results obtained from different characterization techniques and the materials response towards ORR to quickly provide predictions that allow the best synthesis conditions or the best electrocatalysts’ identification. Nitrogen-doped graphene , obtained by the substitution of carbon atoms from the graphene layers with nitrogen, possesses interesting structure and electronic, magnetic and optical properties ,2,3. DueIn recent years, research aimed at the development of efficient and inexpensive oxygen reduction reaction (ORR) catalysts has involved the use of the N-doped graphene materials, coupled or not with some transitional metals, such as iron or cobalt ,11,12,13− reaction pathway is preferred to the 2e− one yielding to water molecules, and it9 is considered a plausible pathway for ORR on doped graphene in both acid and alkaline media [The oxygen reduction reaction (ORR) is a kinetically slow process, requiring a transfer up to four electrons and four protons, and at least three intermediates are involved during the catalytic procedure . The 4e−ne media . Howeverne media . Moreovene media .In our research, we have performed a ball milling treatment in order to prepare nitrogen-doped graphene from graphite and melamine as nitrogen source. In addition to the advantage of obtaining large quantities of functionalized graphene-based materials, this protocol allows the development of materials by a green approach (a clean and safe chemistry), without the employment of polluting solvents. Four types of N-doped graphene with N wt.% between 8.34 and 13.1 have been obtained and their ORR performance has been evaluated using cyclic voltammetry and rotating disk electrode protocols. To determine the correlation of the ORR catalytic performance with the materials’ structure, we analysed the hybrid materials by different methods, including transmission and scanning electron microscopy , thermogravimetric measurements (TGA), elemental analysis, X-ray photoelectron spectroscopy (XPS), X-ray powder diffraction (XRD) and Raman spectroscopy. Moreover, to facilitate the materials’ design and to establish the best synthesis conditions, the machine learning algorithms have been trained on variables containing as input the data that combine both the experimental conditions and different characterization parameters, and as output the number of electrons transferred during the ORR process. The predicted results were then used to identify the optimization of material properties and the best synthesis conditions by applying the reverse engineering process.These images indicate the nitrogen doping of the graphene and the presence of oxygen species on the graphene surface. Moreover, the graphene obtained by ball milling procedure appears to have a fluffy structure, very different from that synthetised by CVD or chemical methods ,19,20 orA first idea about the functionalities found on the graphenes’ surface is given by the thermogravimetric analysis of nanocomposites under the argon atmosphere . The phy2, can be also seen in this region [For the hybrid materials, the TG and DTG curves, obtained under the argon atmosphere, show nearly no weight loss for graphite —region I of the TGA—and from the O-functional groups that are formed during the grinding process, considering that the materials are obtained in air and not under argon or nitrogen atmosphere. According to the data from Further analysis of the N-doped graphene surfaces has been performed by X-ray photoelectron spectroscopy (XPS) and the results are presented in Three major nitrogen types—pyridinic-N 398.4 eV, pyrrolic-N 399.5 eV, and pyridine-N-oxide ~404.9 eV—have been identified from the XPS measurements . The pre2/Csp3 ratio and the content of oxygen functionalities from the graphene surface .The quality of the four materials has been investigated by considering the XPS percentages obtained for the C1s, in terms of the Csp surface . The res2 hybridized carbons [The X-ray diffraction (XRD) patterns of the four N-doped graphene are presented in  carbons . The maiAccording to these results, the number of graphene sheets depends on the quantity of melamine and the reaction time; when a large quantity of melamine is involved, a reaction time of 24 h is not enough to lead to low graphene layers (larger quantities of billing agent/reactant need more energy to accomplish the graphite exfoliation). When the reaction time is 48 h, the differences between the general parameters of the two composites are small . The cor−1. Thus, the 1355 cm−1 peak, which is characteristic of polycrystalline graphite, can be attributed to a particle size effect and its intensity is inversely proportional to the crystallite size [−1) and D + G (about 2950 cm−1) [−1 to 1596 cm−1) as the N concentrations increase, and the 2D′ peaks show a down-shifting (from 3241 to 3215 cm−1) associated with the N content rising. The 2D peaks are located between 2707 and 2704 cm−1; however, the slight redshift of the 2D bands could not be directly associated with the increasing of the N amounts in the composite  . In Figuomposite .D/IG ratio and the nitrogen content of the four hybrid materials. However, the ratio between ID’ and IG (ID′/IG)—calculated for the four N-doped graphene materials—increases from 0.41 to 0.56, showing a better correlation with the N materials’ content than that of the ID/IG. Nevertheless, when attention is paid to the differences between all ID’/IG values, we observe that these differences are not entirely connected with the concentrations’ differences , their investigation requires that this wavelength be used. The obtained results are presented in The electrocatalytic activity for ORR of the four N-doped graphene samples, prepared by the green procedure , was tested in alkaline solutions of 1M NaOH. 2- or O2-saturated 1M NaOH solutions at a scan rate of 50 mV/s using the N-doped graphene/glassy carbon (GC) modified electrodes; for comparison, the 10 wt.% Pt/C modified GC has been prepared and tested towards ORR in alkaline medium  measurements have been performed in Ne medium .According to these results, neither the use of large amounts of melamine nor the increase in the reaction time is justified for ball milling preparation of efficient catalysts for oxygen reduction in alkaline media.As can be seen in −1 vs. ω−1/2) at different potentials and show clear linearity. From the Koutecky–Levich (K–L) equation, presented below, the number of electrons (n) transferred in the ORR process is evaluated and presented in j—measured current density; lj—diffusion-limiting current density; kj—kinetic-limiting current; ω—electrode rotation rate (rpm); F—Faradaic constant ; O2C—O2 concentration in 1M NaOH solution (0.843 × 10−6 mol/cm3); O2D—O2 diffusion in 1M NaOH solution (1.43 × 10−5 cm2/s); n—number of electrons transferred in the ORR process; ν—kinematic viscosity of the electrolyte (1.13 × 10−2 cm2/s) [A comparative study was also conducted using the rotating disc electrode (RDE) at different rotating rates from 400 to 2500 rpm on the appropriate N-doped graphene/glassy carbon (GC) . These e2 cm2/s) ,31,32.Based on the K–L equation, there is a variation of the numbers of electrons transferred with the potentials, indicating a complex mechanism. The best results are observed for N-Gr-3-24 and N-Gr-10-24, of about 3.96 and 4.1 at −0.45 V, which are close to the theoretical value of Pt/C (4 electrons). These hybrid materials appear to be more efficient for ORR compared to N-Gr-3-48 and N-Gr-10-48 (numbers of electrons transferred: 3.41 and 3.08 at −0.45 V).2 for 10 wt.% Pt/C and 7.84 mA/cm2 for N-Gr-3-24, suggesting a better response towards ORR of the N-doped graphene material.It is clear that the changes occurring in the materials during the additional 24 h of the synthesis process are determinant for their ORR behaviours . For theThe long-term stability of the four nitrogen doped materials and Pt/C was investigated using cycling voltammetry . The cat2 and CO saturated 1M NaOH electrolyte, respectively   and a high current density (4.7 mA/cm2) .To summarize, the hybrid materials’ response for the ORR seems to be strongly influenced by the quality of graphene structure .Looking to the electrochemical response towards ORR of the investigated materials and analysing these results by comparison with the results obtained from different characterization techniques, some questions need to be addressed: have we tested the best materials for ORR or is it possible to find an N-doped material, prepared by ball milling procedure, with improved electrochemical properties for our study?2 (0.67) values were found for the Stepwise Linear Regression model. 2 value obtained for the model.K. Min et al.  have alrinvpred) has been applied to the predicted values, and to the most significant variables, when the number of electrons transferred during ORR is four.The predicted values of the numbers of electrons have been further employed in the reverse engineering process to establish the best synthesis conditions or the best material characteristics that should be obtained if we wish to improve the materials’ performances towards ORR. Thus, the inverse prediction function , the FWHM of the XRD peak, and the ID′/IG, I2D/IG and G peak positions from Raman measurements. The results are presented in Firstly, we applied the reverse engineering process for the synthesis parameters; our data make it possible to obtain perspective for the reaction time, but not for the melamine amounts. We continued with the most significant variables, the material characteristics that could provide information regarding the materials’ properties: the nitrogen doping percentage, the pyridinic and pyrrolic N content, the quality of graphene  and 2,4,6-triamino-1,3,5-triazine , further denoted as melamine, in a planetary ball milling machine .During the grinding process, a high amount of energy was released and activated the reactants, thus making the doping process possible. Therefore, the nitrogen atoms from the melamine could be introduced in the graphite network, yielding to the nitrogen functionalized material. Thus, the synthesis was conducted at a temperature much lower than the temperature commonly used for chemical synthesis.Our process was performed at room temperature. The weighted graphite and melamine were transferred to a stainless-steel grinding jar with grinding balls  made of stainless steel. Two ratios between graphite and melamine were chosen: 1:3 and 1:10; in addition, two reaction times of 24 and 48 h were investigated.Briefly, 1 g of graphite and the appropriate amounts of melamine were loaded in the milling bowl, sealed by fixing in the planetary ball-milling machine and grounded at 400 rpm for 24 h or 48 h, respectively. The obtained materials are denoted as N-Gr-3-24, N-Gr-3-48, N-Gr-10-24, N-Gr-10-48, in agreement with the ratios and reaction times employed in the synthesis procedure. Afterward, the materials were washed thoroughly with hot water to remove the un-reacted melamine.Transmission and scanning electron microscopy (TEM and SEM) were realized on a Hitachi HD 2700  equipped with energy dispersive X-ray analysis (EDX).−1.Thermogravimetry measurements were carried out in argon using TA Instruments SDT Q 600 equipment  in the temperature range from 25 to 800 °C, with a heating rate of 10 °C minA Flash 2000 CHNS/O analyzer  analyzer was used to perform elemental analysis of the N doped material.For X-ray Photoelectron Spectroscopy (XPS), a SPECS XPS  equipped with an Al/Mg dual-anode X-ray source, PHOIBOS hemispherical energy analyzer, 150 2D CCD, and a multichanneltron detector, were employed. The spectra were recorded for nitrogen, carbon, and oxygen elements by performing multiple scans with high resolution. Data analysis and curve-fitting were performed by employing a Gaussian–Lorentzian product function and a nonlinear Shirley background subtraction. The high-resolution spectra were deconvoluted into the components corresponding to different types of bonds.θ< 85° with a step size of 0.01°/s.The X-ray powder diffraction (XRD) analyses were recorded by using a Bruker D8 advanced diffractometer  with Cu Kα1 radiation, operating voltage 40 kV, and current 40 mA. The diffraction patterns were measured in the range 15° < 2−1 were employed to record the spectra in the range of 73–4200 cm−1. Each spectrum was collected with an exposure of 100 s and a minimum of three scans; the data were analyzed using OriginPro 2017 software.The Raman spectra were obtained using a JASCO NRS-3300 Raman Spectrometer  with a CCD detector and excitation at 514.5 nm from an argon-ion laser. An Olympus objective UMPLFL 100X, 600 L/mm grating, 0.1 × 6mm slit, and the wavenumber centered at 2300 cm2) as the working electrode, Ag/AgCl , and platinum plate electrodes as the reference and auxiliary electrodes; all the measurements were realized in N2- or O2-saturated 1M NaOH solutions. For the RDE measurements, the spectra were recorded in O2-saturated 1M NaOH solution at various electrode rotating rates (400 to 2500 rpm) and a potential scan rate of 10 mV/s. The procedure for CO stripping experiments employed the following steps: (i) N2 was bubbled on the electrolyte (1 M NaOH) at a constant potential of −0.9 V vs. Ag/AgCl for 30 min, (ii) the gas was switched to CO and purged at the same potential for 30 min, (iii) the gas was switched back to N2 for 30 min, keeping the potential at −0.9 V vs. Ag/AgCl to remove the excess of CO gas from the electrolyte. The CO stripping voltammograms were recorded by cycling the potential between −1.0 and 0.2 V vs. Ag/AgCl with 20 mV/s scan rate.Electrochemical investigations, cyclic voltammetry (CV), rotating disk electrode voltammetry (RDE) and CO stripping voltammetry were performed on Autolab Potentiostat/Galvanostat 302N , controlled by the Nova 1.11 software. The three-electrode system employed a graphene-based electrode  were suspended in 1 mL dimethylformamide and sonicated for 3 min using a finger sonicator . Afterwards, 20 µl from these suspensions were drop-casted on the clean GC electrode and dried at room temperature.The glassy carbon electrode (GC) having a diameter of 3 mm was used for modified electrode preparation. Before any treatment, the glassy carbon electrode was mechanically polished with chromium (III) oxide powder. An electrochemical treatment was also applied in 0.5 M Hinvpred) was applied to the predicted results, and the most significant variables, when the number of electrons transferred during ORR was 4.Machine learning studies were performed using the Regression learner app implemented in MATLAB R2017b . The best predicted model was exported in the Matlab workspace and the predicted results were then used in the reverse engineering process; thus, the inverse prediction function (We have prepared, by a green and easily scalable method, four types of N-doped graphene materials with a nitrogen content ranging from 8.34 to 13.1 wt.%. These materials have been tested towards ORR by cyclic voltammetry and rotating disk electrode protocols and their electrochemical responses have been compared with the results obtained from the surfaces and chemical investigation techniques.D′/IG and I2D/IG ratios exhibit a good correlation with the nitrogen content and the crystallite size/number of graphene sheets, while the ID/ID′ values are similar with the current density responses obtained for the investigated materials.Thus, morphological, thermal, elemental analyses and XPS investigations have been used to obtain data about the materials’ properties. Moreover, as a rapid screening for the performance of N-doped graphene in the ORR process, we showed that the correlation between the XRD and Raman spectroscopy methods can be effective. These methods give valuable information about the graphene crystallite size and number of graphene sheets, while some parameters attained from Raman data can be correlated with the nitrogen contents and crystallite size/number of graphene layers. Thus, the G peaks shift to higher frequencies when the N concentrations increase in the materials, the IBy combining the synthesis parameters, the data obtained from different characterization techniques and the materials response towards ORR, we tested the possibility of using the machine learning algorithms for the optimization of synthesis conditions and prediction of the best structure parameters. The regression learner and reverse engineering methods have been employed to obtain these predictions. Our results highlight the advantage of using machine learning for the optimization of synthesis conditions and the design of materials.


The shallow neural network (SNN) is a popular algorithm in atmospheric parameters retrieval from microwave remote sensing. However, the deep neural network (DNN) has a stronger nonlinear mapping capability compared to SNN and has great potential for applications in microwave remote sensing. The Microwave Humidity and Temperature Sounder  onboard the Fengyun-3 (FY-3) satellite has the ability to independently retrieve atmospheric temperature and humidity profiles. A study on the application of DNN in retrieving atmospheric temperature and humidity profiles from MWHTS was carried out. Three retrieval schemes of atmospheric parameters in microwave remote sensing based on DNN were performed in the study of bias correction of MWHTS observation and the retrieval of the atmospheric temperature and humidity profiles using MWHTS observations. The experimental results show that, compared with SNN, DNN can obtain better bias-correction results when applied to MWHTS observation, and can obtain higher retrieval accuracy of temperature and humidity profiles in all three retrieval schemes. Meanwhile, DNN shows higher stability than SNN when applied to the retrieval of temperature and humidity profiles. The comparative study of DNN and SNN applied in different atmospheric parameter retrieval schemes shows that DNN has a more superior performance. As the basic parameters of the atmosphere, temperature and humidity profiles play an important role in the research and applications of atmospheric science, such as numerical weather forecast, climate change research, and strong convective weather forecast and analysis ,2,3,4. GThe atmospheric temperature and humidity profile retrieval algorithms based on passive microwave observation have been developed for more than 60 years, and the retrieval algorithms can be summarized into two categories: physical retrieval algorithms and statistical retrieval algorithms ,8. The eAmong the many retrieval schemes of atmospheric temperature and humidity profiles using the passive microwave observations, three neural-network-based schemes have been successfully and widely applied for most of the atmospheric scenes, especially the clear sky scene ,18. The At present, although NNs are widely used to retrieve atmospheric temperature and humidity profiles using passive microwave observations, it is almost the SNNs that are extensively used, and they usually contain a hidden layer with a small number of neurons that are based on the error backward propagation algorithm. Each neuron in the hidden layer performs a nonlinear computation, and the small number of neurons in the hidden layer limits the nonlinear mapping capability of SNN. When there are thick clouds or/and rain in the sight of the microwave radiometer, the nonlinear relationship between the measurements of the microwave radiometer and the atmospheric temperature and humidity profiles is complicated, and SNN may not be able to accurately model the nonlinearity between the input and output samples, which leads to the poor accuracy or even failure of the retrieval of the temperature and humidity profiles based on SNN. However, compared with SNN, the nonlinear mapping capability of DNN has been greatly improved ,26. TherMWHTS is a key payload onboard the new generation of polar-orbiting meteorological satellites Fengyun-3C  and FY-3D. It is a microwave radiometer that integrates humidity and temperature sounding to detect atmospheric humidity and temperature profiles simultaneously. In this paper, a study on the applications of DNN in three retrieval schemes for temperature and humidity profiles using MWHTS measurements is recounted. The retrieval results are analyzed and compared with those of SNN, with the aim of exploring the potential of DNN in retrieving atmospheric parameters by using passive microwave observations. This paper is organized as follows: As a total power microwave radiometer, MWHTS is an important payload onboard FY-3C and FY-3D satellites, which were launched in September 2013 and November 2017, respectively, and both payloads are operating normally in orbit and have accumulated rich atmospheric sounding data. MWHTS performs the cross-track scanning along the orbit with the angle of ±53.35° from the nadir to inspect 98 nominal fields of view (FOVs) in each scan line, which is corresponding to the scanning of the swath of 2645 km in 2.667 s. MWHTS has eight temperature sounding channels with frequencies near the 118.75 GHz oxygen absorption line for sounding temperature from the surface to the upper atmosphere and five humidity sounding channels with frequencies around the 183.31 GHz water vapor absorption line for sounding water vapor and precipitation from the surface to about 300 hPa, and two window channels with frequencies at 89.0 and 150.0 GHz, respectively, for providing information about the surface parameters . Table 1http://satellite.nsmc.org.cn accessed on 15 November 2019)—the quality of MWHTS observations at ECMWF has been evaluated, and the detailed description of the evaluated results can see Lawrence et al. [http://apps.ecmwf.int/datasets accessed on 15 November 2019). Many global atmospheric sounding measurements are assimilated with the 12-h analysis window by using four-dimensional variational analysis to produce ERA-Interim [The datasets used in this study included the following: (1) Level 1b brightness temperatures of MWHTS onboard FY-3D from the National Satellite Meteorological Center (NSMC) -Interim ,30. In tIn the three retrieval schemes used in this study, it was necessary to calculate the simulations of MWHTS, especially for the retrieval scheme based on the physical retrieval algorithm, where the radiative transfer model is an essential part. In this study, the fast radiative transfer model RTTOV , Version 11.3, developed by ECMWF, was used to calculate MWHTS simulated brightness temperatures. RTTOV can calculate simulated brightness temperatures for a wide range of satellite-based microwave radiometers, with an accuracy that can meet the application requirements . The emiAccording to the purpose of this paper, for applying the data used in this study in the three retrieval schemes, the data-preprocessing process was as follows. First, building the collocated dataset. As for the criteria of the collocation between the MWHTS observed brightness temperatures and the atmospheric parameter dataset, the time and absolute distance in latitude and longitude are less than 0.5 h and 0.1°, respectively. Second, calculating the simulated brightness temperatures of MWHTS in the collocated dataset in which the profile parameters and the surface parameters are input to RTTOV to calculate the simulations. Then, after filtering out the scattering-affected data in the collocated dataset, we took the vertical integral cloud liquid water of 0.05 mm as the threshold of precipitation. If the integral cloud liquid water in one group of collocated dataset exceeds 0.05 mm, then that group’s data are considered as scattering-affected data and are removed. Finally, 80% of the collocated dataset after filtering out the scattering-affected data containing the observed brightness temperatures, the simulated brightness temperatures, and the atmospheric parameters is randomly selected as the analysis dataset with 848129 collocated samples, and the remaining 20% are set as the testing dataset with 212033 collocated samples. The overall data preprocessing procedures are summarized in In 2006, deep learning was introduced in the research of Hinton and his students, which initiated the research wave of multilayer neural networks, and many scholars began to study the applications of DNN in various fields . In shorThe purpose of DNN, as a type of neural network, is to build a statistical model between input and output samples through supervised learning in a training dataset, and to make predictions on new input samples from the validation dataset when they are fed into the established statistical model. In this study, the application of DNN to the retrieval of atmospheric temperature and humidity profiles using MWHTS observations involved two main aspects: the building of the DNN-based observation-bias-correction model and the building of the DNN-based retrieval model for temperature and humidity profile.For MWHTS-observation-bias correction, DNN was used to build a statistical model between MWHTS-observed brightness temperatures and MWHTS observation biases for predicting the observation biases, and the observation biases are defined as follows:For the retrieval of atmospheric temperature and humidity profiles, both MWHTS observed brightness temperature and MWHTS simulated brightness temperature can be used to build the retrieval model. The DNN-based retrieval model using the observed brightness temperature is built as follows. The training dataset of DNN is established, i.e., MWHTS observed brightness temperatures in the analysis dataset are set as the input samples, and the atmospheric temperature and humidity profiles in the analysis dataset are set as the output samples. Then, the four-layer network structure  of DNN is built, and DNN is trained by using the training dataset. Thus, the DNN-based retrieval model, using the observations, is built. If DNN is trained with the simulated brightness temperature instead of the observed brightness temperature in the training dataset, the DNN-based retrieval model using the simulations is obtained. Finally, the observed brightness temperatures from the testing dataset are fed into the DNN-based retrieval model using the observation or the corrected brightness temperature from the testing dataset are fed into the DNN-based retrieval model, using the simulations to obtain the retrievals of the temperature and humidity profiles, and the temperature and humidity profiles in the testing dataset are used to verify the retrieval of the temperature and humidity profiles. Further details of the training and testing of the DNN-based retrieval models are also contained in S; and T represents the matrix transpose. By minimizing the cost function, ξ, the optimal solution is obtained as follows:K is the tangent linear function of f at point S, n is the iteration index, and S1 is the initial state variable.The 1DVAR algorithm is generally labeled under the general term of physical retrieval that inputs the initial values of atmospheric parameters to the radiative transfer model and adjusts the initial values through an iterative process with the aim of fitting the simulations from the radiative transfer model to the observations from the satellite. The 1DVAR algorithm mainly includes two parts: one is the radiative transfer model for the simulations of brightness temperature; the other is the minimization of the cost function. Assuming that the errors in the observations are neither biased nor correlated, Gaussian distribution, the optimal estimate of the atmospheric state variable, function :(3)ξ=12SaS, and the initial state variable, In this study, the parameters of the 1DVAR for retrieving atmospheric temperature and humidity profiles using MWHTS observations were specifically set as follows. The averages of the temperature and humidity profiles in the analysis dataset were taken as both the background state variable, ferences ,37,38. TIn this study, three commonly used retrieval schemes were designed for retrieving atmospheric temperature and humidity profiles by using MWHTS brightness temperatures to study the effect of DNN on the retrieval accuracy of temperature and humidity profiles in different retrieval schemes. In order to compare the performance of DNN in retrieving temperature and humidity profiles, studies of SNN in the three retrieval schemes were carried out. The three retrieval schemes for retrieving atmospheric temperature and humidity profiles using MWHTS brightness temperatures were specifically designed as follows.The first retrieval scheme: The 1DVAR retrieval retrieved the atmospheric temperature and humidity profiles based on the MWHTS 1DVAR retrieval system. First, according to the description in The second retrieval scheme: The NN-based retrieval using the observations retrieved the atmospheric temperature and humidity profiles by the NN-based retrieval model using the observations. The DNN-based retrieval model using the observations and the SNN-based retrieval model using the observations were established, respectively, as described in The third retrieval scheme: The NN-based retrieval using the simulations retrieved the atmospheric temperature and humidity profiles by the NN-based retrieval model using the simulation. The DNN-based retrieval model using the simulations and the SNN-based retrieval model using the simulations were established, respectively, as described in In this study, the DNN and the SNN design used in the three retrieval schemes both produced the best results in terms of reproducing the observations bias and the atmospheric temperature and humidity profiles. The input layers of DNN and SNN used in the observation-bias correction have 15 neurons, which receive the observations of 15 channels of MWHTS, and the output layers have 15 neurons, which output the observation biases corresponding to the observations in the input layers. In the retrievals of the temperature and humidity profiles, the input layers of DNN and SNN also have 15 neurons, which receive the simulations or the observations of MWHTS; the output layers have 74 neurons, which output the temperature and humidity profiles.For the hidden layers of DNN and SNN applied in the three retrieval schemes, the configurations of neurons, layers, and activation function were determined by extensive testing . In the extensive testing, the trained DNN and SNN for the observation-bias correction were evaluated by the Root Mean Square Error (RMSE) between the predictions of the observation bias and the observation biases in the testing dataset, and the trained DNN and SNN for the retrieval of the temperature and humidity profiles were evaluated by the RMSE between the retrievals and the temperature and humidity profiles in the testing dataset.In the three retrieval systems, one hidden layer for SNN and two hidden layers for DNN can ensure adequate training of the model in the extensive testing. Then the Rectified Linear Unit (ReLU) was selected as the activation function in this study because it can overcome the problems of saturation and vanishing gradients . MoreoveThis section presents the experimental results of DNN and SNN models used to correct the MWHTS observation bias, as well as the retrieval results of atmospheric temperature and humidity profiles when DNN and SNN models are applied to the three retrieval schemes described in According to the experimental design in As can be seen in As can be seen from This section presents the retrieval results of the temperature and humidity profiles of the three retrieval schemes as designed in According to the experimental design of the first retrieval scheme, the MWHTS observed brightness temperature in the testing dataset, the DNN-based corrected brightness temperature, and the SNN-based corrected brightness temperature obtained in As can be seen from According to the experimental design of the second retrieval scheme, the observed brightness temperature in the testing dataset is input to the DNN-based and SNN-based retrieval models using the observations, respectively. The output results are the retrieval results of temperature and humidity profiles, and the retrieval accuracies are verified as shown in From According to the experimental design of the third retrieval scheme, the DNN-based corrected brightness temperature and the SNN-based corrected brightness temperature obtained in the first retrieval scheme are input to the DNN-based and SNN-based retrieval models using the simulations, respectively, to obtain the retrieval results of the atmospheric temperature and humidity profiles, and the comparison of the retrieval accuracies is shown in From From The stability test of the algorithm is required when the neural network is applied to retrieve the atmospheric parameters using passive microwave measurements. The weights and biases are set randomly at the start of the training of the neural network. The performance of a well-constructed neural network for atmospheric parameter retrieval is not affected by the different weights and biases used at the start of training. Therefore, the stability testing experiments of DNN and SNN in the three retrieval schemes are carried out, simultaneously. Taking the 1DVAR retrieval and the NN-based retrieval using the observations as examples, one must retrain the DNN and SNN models with randomly initialized weights and biased for three separate times, respectively. Then they are applied to the same MWHTS observations as those of the 1DVAR retrieval and the NN-based retrieval using the observations. The differences in the RMSE of bias correction between the three bias corrections and the bias correction in For the bias-correction results of SNN and DNN for MWHTS observation bias, it can be seen from In conclusion, both SNN and DNN can be successfully applied to the 1DVAR retrieval and the NN-based retrieval using the observations, and DNN shows better performance than SNN in both MWHTS-observation-bias correction and the retrieval of temperature and humidity profiles using MWHTS observations. However, in the NN-based retrieval using the observations, the application of SNN fails, while DNN achieves a successful retrieval based on MWHTS simulations due to its stronger generalization ability. For the three retrieval schemes, although the 1DVAR retrieval and the NN-based retrieval using the observations obtain comparable retrieval accuracies of atmospheric temperature and humidity profiles, they have their advantages and disadvantages. Although the 1DVAR algorithm does not require a large amount of historical data for learning the sample features, the parameters of the physical retrieval system need to be set before retrieving to ensure a high retrieval accuracy, which is computationally intensive and expensive. In the NN-based retrieval using the observations, although the retrieval operation is simple, it is necessary to establish representative sample data for the training of NNs to ensure high retrieval accuracy when applied to new observed brightness temperature. For the NN-based retrieval using the simulations, although the retrieval accuracy is slightly poorer compared with the former two retrieval schemes, it has the advantage that the data-matching errors between satellite observations and atmospheric parameters can be disregarded because the atmospheric parameters can be directly used to generate simulated brightness temperatures, which can satisfy the demand of the neural network for a large number of data samples, and it is easy to establish a representative sample dataset.In this paper, the application of DNN in the retrieval of atmospheric temperature and humidity profiles from FY-3D/MWHTS was investigated, mainly involving the application of DNN in MWHTS-observation-bias correction and in retrieving atmospheric temperature and humidity profiles using MWHTS observed brightness temperature or MWHTS simulated brightness temperature. In order to verify the performance of DNN applied in retrieving, a study on the application of SNN in the same situation was also carried out. It is found that, in the three retrieval schemes of atmospheric temperature and humidity profiles that use MWHTS measurements, DNN shows superior performance and better stability than SNN in both MWHTS-observation-bias correction and retrieval of atmospheric temperature and humidity profiles. Therefore, the results indicate that DNN and other deep learning algorithms have great potential in the applications of microwave remote sensing.It is noted that the retrieval experiments of temperature and humidity profiles in this study were conducted under all-weather conditions and do not classify the MWHTS observations for different atmospheric states, such as clear sky, cloudy, and rainy. In practical applications, higher retrieval accuracy can be obtained if the brightness temperature classification according to different atmospheric states is realized based on the brightness temperature characteristics, and then the atmospheric temperature and humidity profile is retrieved under different atmospheric states. However, it is difficult to classify satellite observed brightness temperature under different atmospheric states based on the characteristics of satellite observations at present. The study of data classification of satellite microwave observation under different atmospheric states by deep learning algorithms to further improve the retrieval accuracy of atmospheric temperature and humidity profiles using satellite passive microwave observations is the focus of future work.


The rGO is flat and smooth with a sheet-like morphology while the N-rGO exhibits folded morphology. This type of folding of the surface morphology can increase the gas sensitivity. The N-rGO and the rGO sensors showed n-type and p-type semiconducting behaviors in ambient conditions, respectively, and were responsive to low concentrations of NO gases (< 1000 ppb) at room temperature. The gas-sensing results showed that the N-rGO sensors could detect NO gas at concentrations as low as 400 ppb. The sensitivity of the N-rGO sensor to 1000 ppb NO (1.7) is much better than that of the rGO sensor (0.012). Compared with pure rGO, N-rGO exhibited a higher sensitivity and excellent reproducibility.In this study, we use nitrogen-doped to improving the gas-sensing properties of reduced graphene oxide. Graphene oxide was prepared according to a modified Hummers’ method and then nitrogen-doped reduced graphene oxide (N-rGO) was synthesized by a hydrothermal method using graphene oxide and NH The development of gas sensors is a highly critical research area that involves issues related to safety, health, and environmental risks4. Typical gas sensors use metal oxide materials, such as zinc oxide and tin oxide6. Metal oxide-based gas sensors operate at high temperatures (200–400 °C), which results in excessive consumption of energy and limits their long-term stability. Thus, it is important to develop new gas sensor concepts that can overcome these drawbacks.Gas sensors are very important for a wide range of applications, including detection of harmful chemical vapors, industrial process control, and chemical processing plants9. However, these sensors show low sensitivity and long response time, or poor reproducibility, depending on the assembly process and the purity. Graphene-based materials are useful for the fabrication of high-performance sensors, especially for detecting gases at low operating temperatures. Graphene exhibits two excellent properties for gas detection: (1) High carrier mobility  and high surface area (2630 m2/g), facilitating detection of resistance change during the process10 and (2) Low electrical resistivity (1.0 × 10–6 Ω cm) for detecting resistance change after adsorption or desorption and increase or decrease in target gas concentration11. However, pure graphene is intrinsically inert, while graphene oxide (GO) has multitudinous oxygen functional groups and is too insulating for fabrication of resistance-based sensors. Recently, several graphene-based composites have been reported for gas sensors. For example, Ganhua et al.12 prepared graphene directly from GO through one-step heating , which was responsive to low-concentration NO2 and NH3 gases in air at room temperature with excellent sensitivity. Jianwei et al.13 synthesized graphene/Pt with sensitivities of 14% (7%), 8% (5%), and 10% (8%), for 1000 ppm H2, NH3, and NO gases, respectively with (without) Pt nanoparticles at room temperature. Vardan et al.14 prepared a hybrid structure of reduced graphene oxide (rGO) and ZnO nanostructures exhibiting 40–50% better response to NO2 and H2 compared to pure ZnO sensors at an operating temperature of 200 °C. Hao et al.15 synthesized rGO/SnO2/Au hybrid nanomaterials through a one-step hydrothermal process, exhibiting a much better response/recovery time to 5 ppm NO2 compared with pure rGO and rGO/SnO2 at an optimal operating temperature of 50 °C. Sen et al.16 synthesized rGO/ZnO/Au hybrids through the wet chemical method with high sensitivity and fast response for detection of NO2 at an operating temperature of 80 °C. The improved sensor performance reported in the above studies is attributed to the incorporation of graphene/RGO-based nanocrystals/nanoparticles. However, their synthesis methods are complicated, requiring a high operating temperature and incorporation of precious metal or metal-oxide, and the sensor has a low response to low concentrations of gases. Therefore, reducing operating temperature and cost, and developing an environment-friendly strategy is very important to extend the applications of graphene-based gas sensors.In recent years, carbon-based nanomaterials, such as carbon nanotubes and graphene, have been used for gas detection at room temperature. Various gases have been tested using carbon nanotubes gas sensorsWe prepared nitrogen doped rGO gas sensors with fast response and high sensitivity. Nitrogen-doped rGO (N-rGO) nanosheets were prepared directly from GO through the hydrothermal treatment in an ammonia solution. In the gas sensing process, N-rGO and rGO exhibited n-type and p-type semiconductor behaviors, respectively. We assessed the sensing characteristics of N-rGO at various concentrations of NO gas at room temperature.All chemicals were of analytic grade and used without further purification. Graphite powder  was used to prepare GO. Sodium hydroxide  and an ammonia solution  were used to prepare rGO and N-rGO, respectively. The NO calibration gas cylinders (1000 ppb) used for the sensing studies were obtained from Air Products San Fu Co. Ltd., Taiwan.17 is used to prepared graphene oxide powders. The mixture of 2 g of graphite powder and 0.6 g of NaNO3 is mixed in 20 mL of concentrated H2SO4 solution under 30-min stirring in an ice-bath surrounding. Then, 10 g of KMnO4 was added slowly in the mixture solution which was stirred at 35 °C for 3 h. Follow, the mixture solution with 180 mL DI water was heated at 98 °C for 2 h. In order to eliminate the excess of KMnO4, 20 mL of 30% H2O2 was dropped slowly to the solution. Finally, the mixture solution was centrifuged and washed with 5% HCl and DI water to remove the metal ions and control the pH value to 7. The resulting solid was dried in a vacuum oven under a pressure of 101.32 Pa at 70 °C for 12 h.The modified Hummers’ methodHydrothermal method is used to synthesize N-doped graphene from the raw material of graphene oxide. Dispersion of GO (0.05 g) in 50 mL of DI water is obtained by ultra-sonication for 2 h. Then, 20 mL of ammonia solution (25 wt.% in water) was added in dispersed solution. The Teflon-lined autoclave which contain the mixture solution is placed in drying oven at 200 °C for 24 h. The reacted solution from the autoclave which is cooled naturally to room temperature are centrifuged and washed several times with distilled water and ethanol after. The remaining mixtures are drying in a vacuum oven at 80 °C for 24 h. For fabrication of rGO powders, the NaOH solution is conducted in the same process instead of ammonia solution.The optical properties were measured using a Shimadzu UV 3600 spectrophotometer at wavelengths in the range of 200–800 nm. The morphology and the dispersion level of rGO and N-rGO were analyzed by transmission electron microscopy , field-emission scanning electron microscopy , and scanning electron microscopy . Raman spectra were recorded on an Andor DU401-BV multichannel confocal microspectrometer with 632-nm laser excitation. X-ray photoelectron spectroscopy  was performed using monochromated Al Kα radiation (1486.8 eV). All these measurements were carried out at room temperature.2O3 substrate. Figure The sensor was fabricated using a three-layer configuration: sensing materials, Ag–Pd electrodes, and Al19.Figure 2 (C=C), sp3 (C–H), C–O , and C=O (carboxylic) bonds21. The intensity of the sp2 peak increases and that of the oxygen-related peaks decreases, further confirming the reduction of GO. Figure 2 (C=C), Csp2-N, and Csp3-N, originating from the doping of N22. As a result of the relatively low electronegativity of N (than oxygen), this peak appears at a lower binding energy relative to the C–O and C=O peaks. The N 1s spectra in Fig. 24. The presence of these peaks show that the doping of N atoms into an sp2-bonded network of carbon results in three different N functionalities  gra2 C=C bonds and n-π* transition of the C=O bonds, respectively27. When GO is reduced to graphene, a typical absorption peak is observed at 234 nm, which can be ascribed to the excitation of the π-plasmon of the graphitic structure28. The N doping introduces a new UV absorption peak at 268 nm, corresponding to the electron transitions from C=N29. Meanwhile, when GO is reduced to rGO and N-rGO, the shoulder peak at approximately 305 nm disappears as a result of the decrease in the concentration of carboxyl groups30.Figure −1 and the G-band (in-plane vibrations of sp2 carbon network) is observed at approximately 1613 cm−131. The intensity ratio of the D-band to the G-band (ID/IG) is used to determine defect lattices and degree of disorder in graphene32. The ID/IG ratio increased from 1.15 for GO to 1.24 for rGO. This is a clear indication of the effective reduction of GO. A higher ratio (ID/IG = 1.28) for N-rGO indicates the presence of more defects caused by N doping.Figure 3-hybridized carbon atoms33. After the NaOH treatment, the resistance of rGO slightly decreases, further confirming the reduction of GO. The resistance of N-rGO significantly decreases, indicating improved sensitivity.Figure 0) in 1000-ppb NO at room temperature. Initially, the sensor was exposed to clean air to determine the base resistance; then, NO gas was injected into the sensing chamber for 2.5 min to register the sensing signal; finally, the NO gas supply was closed and air flow was simultaneously allowed for 2.5 min to repeat the cycling of the sensor. In these experiments, there was no evidence of any deterioration in the sensitivity. Figure 34. This is because N-rGO is very sensitive to this gas. As the N-rGO nanostructures are exposed to air ambient, the electrons introduced by the oxygen vacancies in the N-rGO nanostructures are adsorbed by the oxygen molecules from the air ambient which compensate the surface oxygen vacancy sites. These surface adsorbed oxygen molecules are transformed into oxygen ions such as O2−, O2− and O− by capturing the electrons from the conduction band of N-rGO. Hence, the electron depletion layer is extended on the surface of the N-rGO nanostructures that increases the electrical resistance of the nanostructures35.Figure −, O2− ions by capturing electrons from the conduction band which results in increase in resistance (Eq. 1). When the N-rGO is exposed to the atmosphere of NO, it captures the electrons due to its higher electrophonic property leading to the formation of adsorbed NO− (ads) which results in increase in resistance (Eq. 2). However, desorption of NO− can take place and the adsorbed NO− (ads) reacts with adsorbed oxygen  which results in further increase in resistance (Eq. 3). Then, when the air was let in, NO can readily dissociate into O− and some bound electrons were released36. Figure 37 and chemically38 reduced GO when exposed to the ambient environment. The absorbed oxygen on the rGO surface attracts electrons from graphene, generating additional holes as carriers. This process increases the hole concentration and contributes to the p-type semiconducting behavior. When the rGO (p-type) sample is exposed to the NO gas, a chemical reaction takes place. This chemical reaction will then cause a change in the material properties, for example, electrical conductivity or resistance. The NO gas is adsorbed on the surface of the N-rGO material according to Eqs. , 2), an, an2), a2) gas excess NO reacts with oxygen to form NO239. Therefore, used the N2 represent the state the ambient. Figure 2. This means that the surface topography is related to the response time. We calculated the sensitivity (S), which is one of the most important characteristics of a gas sensor41. Figure Various concentrations of NO gas, ranging from 400 to 1000 ppb, were fed into the chamber to determine the sensing performance of the optimal sensor. In ambient environments, the oxygen , ethanol (EtOH), methanol (MeOH), as summarized in Fig. Selective detection of target gases remains a challenge in the applications of gas sensors. To identify their selectivity by exposing them to saturation organic vapors like NHThe long-term cyclic stability of the rGO and N-rGO sensor was measured in Fig. 2 hybridized carbon atoms and sp3 hybridized carbon atoms. These constitute the graphite structure and the structural defects, and form chemical bonds with oxygen-containing groups, respectively. The absorption energy of sp3 is larger , resulting in slower adsorption and desorption43. Figure 2 carbon network, in which N has a higher electronegativity (x = 3.04) than C (x = 2.55), thus further affecting the physical and the chemical performance; (iii) The doped N atoms exist in three different states, namely pyridinic-N, graphitic-N, and pyrrolic-N. The enhanced catalytic activity is usually attributed to the increased active sites of pyridinic-N and/or pyrrolic-N. Pyridinic N refers to N atoms at the edges of graphene planes, where each N atom is bonded to two carbon atoms and donates one p-electron to the aromatic π system45. Pyrrolic N atoms are incorporated into five-membered heterocyclic rings, which are bonded to two carbon atoms and contribute two p-electrons to the π system48. Pyridinic-N modifies the band structure of carbon, raising the density of π states near the Fermi level and lowering the work function.the relative electronegativity of graphitic N atoms reduces the electron density on the adjacent C nuclei, which helps electrons transfer from the adjacent C to N atoms, and N backdonates electrons to adjacent C pz orbitals. The donation and backdonation processes not only facilitate O2 dissociation on the adjacent C atoms, but also help forming a strong chemical bond between O and C49.The gas sensing mechanism of N-rGO can be explained using a surface-controlled model, which includes absorption, electronic desorption, and transfer. The unsatisfactory performance of the rGO sensor results from its constituent carbon atoms. Graphene has two types of carbon atoms, namely spReduced graphene oxide and N-rGO nanosheets were evaluated for their NO gas sensing properties at room temperature. The hydrothermal treatment converted rGO to n-type N-rGO, as confirmed by UV and XPS analyses. The gas-sensing results showed that the N-rGO sensors could detect NO gas at concentrations as low as 400 ppb. The sensitivity of the N-rGO sensor to 1000 ppb NO (1.7) is much better than that of the rGO sensor (0.012). The results of the present study indicate that N-doping can significantly enhance the NO sensing properties of graphene-based sensing materials at room temperature, suggesting their excellent potential for use as gas sensors.


Smoothened from the mesoderm impaired the development of the venous pole of the heart and resulted in hypoplasia of the atrium/inflow tract (IFT) and a low heart rate. The blockage of Smoothened led to reduced expression of genes critical for sinoatrial node (SAN) development in the IFT. In a cardiac cell culture model, we identified a Gli2–Tbx5–Hcn4 pathway that controls SAN development. In the mutant embryos, the endocardial-to-mesenchymal transition (EndMT) in the atrioventricular cushion failed, and Bmp signalling was downregulated. The addition of Bmp2 rescued the EndMT in mutant explant cultures. Furthermore, we analysed +Gli2 scRNAseq and −/−Tbx5 RNAseq data and explored the potential genes downstream of hedgehog signalling in posterior second heart field derivatives. In conclusion, our study reveals that Smoothened-mediated hedgehog signalling controls posterior cardiac progenitor commitment, which suggests that the mutation of Smoothened might be involved in the aetiology of congenital heart diseases related to the cardiac conduction system and heart valves.Smoothened is a key receptor of the hedgehog pathway, but the roles of Smoothened in cardiac development remain incompletely understood. In this study, we found that the conditional knockout of  Cardiac progenitor formation and differentiation are essential for heart development. During early gastrulation, a subset of mesodermal cells leaves the primitive streak and is destined to a cardiac fate . Later d−/−Smo mutant embryos fail to turn and are arrested at approximately E9.0 with a linear heart tube . M. MGli2 ilaxation . A scRNAt hearts g. qRT-PCt hearts h. Type 2illation ,42. RyR2 mutants h.Gli2+ cardiac progenitors.Thus, we identified the Gli2–Tbx5–Hcn4 and Gli2–Bmp2 axes, which control SAN development and AV cushion formation, respectively, and we also analysed and validated the genes related to the cardiac contraction in . 4+ cells within the AV canal myocardium.We have demonstrated that Hh signalling is required for the developmental potential of the cardiac progenitors and their differentiation towards pacemaker cells within the SAN and Bmp2. 4.1Smo in the mesoderm reduces the de novo expression of Tbx5 in the pSHF. Wnt2, a downstream target of Tbx5 [Smo mKO mutants. However, the loss of Smo did not affect the expression of Hcn4 and Isl1 at the approximately E7.5–E8.0 stage. These results demonstrated that Hh signalling is required for the developmental potentials but not the formation of the cardiac progenitors in the pSHF. It is reported that Tbx5 acts upstream and parallel to Hh signalling in the SHF [Tbx5+/− mutants. Our data along with that obtained in the previous study suggests that the regulation of the Hh pathway by Tbx5 might constitute a feedback pathway.In this study, we found that the inactivation of  of Tbx5 , was als the SHF . The pre the SHF  showed t. 4.2Hcn4, Tbx3, Shox2, Isl1 and Tbx18 are enriched in the SAN [A differential expression analysis of RNAseq data revealed that  the SAN ,44. The  the SAN ,45. The  the SAN .Hcn4, Tbx3, Shox2 and Isl1. Furthermore, functional assay revealed that the heart rates are significantly decreased in Smo mutant heart. By contrast, the expression of Tbx18 was not affected in the IFT of Smo mKO mutant heart. These results suggest that Hh signalling controls pacemaker progenitor cell commitment.Tbx5 and Hcn4 are required for the specification and maturation of pacemaker progenitor cells, respectively . Tbx3, SSmo mKO mutant hearts at E8.25–E8.5 . By E9.5, a marked reduction of proliferation was found in the mutant hearts  . It has been reported that Tbx5 controls the expression of cell-cycle progression genes [In addition, we measured cell proliferation at different embryonic stages. The proliferation of cardiac progenitor cells displayed a decreasing trend in the . 4.3Bmp2 expression, which is required for activation of the Bmp pathway and initiation of the EndMT. Moreover, the administration of Bmp2 to the Smo mKO mutant AV cushion rescued the transition defect, which suggested that Bmp2 is both necessary and sufficient for induction of the EndMT by Hh signalling.Multiple signalling pathways are involved in the EndMT of the endocardium, and these pathways include Bmp/Tgfβ, Notch, Vegf and calcineurin/NFAT . In this−/−Shh hearts [Shh and Ihh compound mutants arrest shortly after gastrulation and phenocopy Smo mutants. Shh and Ihh compound mutants or Smo mutants exhibit a more severe phenotype than −/−Shh mutants [Shh and Ihh are both required for Smo-mediated EndMT during heart development.It has been reported that the specification of the AV cushion and the initiation of the EndMT proceed normally in − hearts . Shh is − hearts . Shh and mutants . We spec. 4.4Tbx5 in the pSHF was reduced in the Smo mKO mutants, its expression in the anterior heart field was less affected. These results suggest that the regulation of Tbx5 by Hh signalling is context-dependent. We noted that the expression levels of Hcn4 in the heart field were comparable between the control and Smo mKO mutant embryos at E8.0. As the embryo develops, the expression of Hcn4 was decreased in the IFT and common atrium of the mutant hearts, which suggested Tbx5 is required for the maintenance of Hcn4 expression.We identified the Gli2Tbx5–Hcn4 axis which is essential for pacemaker progenitor cell differentiation and cardiac conduction. Although the expression of Bmp2 expression. The results indicate that Gli2 is required in cardiac progenitors for Bmp2+ cell lineage determination.The cardiac progenitor cells in the pSHF contribute to the AV canal myocardium, atrium and IFT. The specific expression of Bmp2 in the AV canal myocardium induces the EndMT by lateral induction. We demonstrated that the overexpression of Gli2 enhanced Gli1-lacZ, Gli2-lacZ and Gli3 mRNA are dominantly expressed in the lateral plate mesoderm (pSHF) and contribute to atrium/IFT development [−/−Gli2;−/−Gli3 double knockout embryos, the expression of Tbx5 is reduced in the lateral plate mesoderm [Smo mKO mutants. These results indicate that Gli2 and Gli3 redundantly regulate the expression of Tbx5 (d). In future study it would be interesting to explore whether Bmp2 expression is downregulated in the AV canal myocardium of the Gli2 and Gli3 compound mutant embryos. Gli1 and Gli2 exhibit similar expression patterns during cardiogenesis. zfd/zfdGli1 and lz/lzGli1 are viable with no obvious defects [Smo mKO mutants (a–d), and the overexpression of Gli2 in P19CL6 cells increased the expression of Gli1 (c), which is consistent with the in vivo results [Tbx5 and Bmp2 in the cell model (c). Thus, the in vivo and in vitro results indicate that Gli1 might not be essential for the formation of the SAN and AV cushion. In this study, we also found that Hh signalling controls other core transcriptional factors required for SAN node development. Single-cell ChIP-seq would be a powerful tool for dissecting the regulatory mechanism in the future.At E8.5 and E9.5. elopment . In Gli2mesoderm , which i of Tbx5 d. In fut defects ,50. Both mutants a–d, and  of Gli1 c, which  results . Unlike ll model c. Thus, Isl1, Tbx5, Hcn4, Mef2c, Fgf8, Wnt2, Osr1 and Hoxb1 were expressed in +Gli2 cells, which suggested that Hh responding cells contribute to both the aSHF and pSHF. Moreover, we predicted the potential downstream genes in the pSHF regulated by Gli2. Among these target candidates, Hcn4 was validated by a gain-of-function assay. The qRT-PCR results demonstrated that the expression of Mybpc3, a potential downstream target gene, was reduced in the IFT of the Smo mkO heart. +Gli2, +Tbx5, +Shox2 triple-positive cells were identified in E8.5 cardiomyocytes. We found that the expression of Shox2 was reduced in −/−Tbx5 and Smo mKO pSHF cardiomyocytes. The data suggest that Gli2 might regulate the expression of Shox2 via Tbx5.We analysed scRNAseq data from the E8.5 mouse embryonic heart . Isl1, TIn summary, our data demonstrate that Hh signalling in the pSHF controls the activity of Gli2 to regulate the development of the SAN and the formation of the AV cushion.