By calculation and comparison, the large absorption loss  contributes to reducing secondary radiation, which is quite beneficial for stealth uses. Thus, this work demonstrates a promising design method for the preparation of green high-performance composites for EMI shielding and stealth applications .Because of serious electromagnetic pollution caused by the widespread use of radio frequency equipment, the study of electromagnetic interference (EMI) shielding materials has been a long-standing topic. Carbon fiber and graphene composites have great potential as EMI shielding materials due to their unique microstructure and electrical conductivity. In this work, a novel kind of core-shell composite is fabricated based on the pore-rich pine needles-derived carbon fibers (coded as PNCFs) core and the graphene shell. The pore-rich PNCFs are created by KOH activation, and the integration between the pore-rich PNCFs and the graphene relies on a plasma-enhanced chemical vapor deposition (PECVD) method. The conductivity of the pore-rich PNCFs@graphene core-shell composite reaches 4.97 S cm With the rapid development of electronic information technology, radio frequency equipment has been widely used in various fields such as communications, biological medicine, and remote sensing. Radio frequency equipment brings us convenience, but it also brings serious electromagnetic pollution. Electromagnetic pollution will not only disturb the normal operation of precision instruments, but also cause harm to people’s health. Therefore, there is an urgent need to develop high-performance electromagnetic interference (EMI) shielding materials ,2,3,4.EMI shielding materials can be roughly divided into three categories: carbon materials, metals, and conductive polymers. Among them, carbon materials have suitable electrical conductivity, strong oxidation resistance, and superior thermal stability. They are quite suitable for electromagnetic shielding materials. As people’s awareness of environmental protection continues to increase, more and more attention has been paid to green chemistry and materials ,6. Graph2 V−1 S−1 [−1 and electromagnetic shielding effectiveness is 55.0 dB at 12.4–18 GHz. The porous structure will greatly increase the specific surface area of the materials, and the specific surface area of the shielding materials is also a key parameter that affects the EMI shielding performance. For instance, Chen et al. [Meanwhile, carbon nanomaterials  have attracted the interest of outstanding scholars in different fields in the past few years due to their superior various performance. For example, Zhang et al.  prepared V−1 S−1 ,21,22,23 V−1 S−1 . Silkworn et al.  preparedn et al. , we haveSEA) and reflection shielding effectiveness (SER) of the pore-rich PNCFs@graphene account for 90.8% and 9.2% of the total effectiveness, respectively, revealing an absorption-dominate EMI shielding mechanism. In addition, a possible mechanism analysis on the EMI shielding process is proposed.Herein, we use pine needles as raw materials and carbonize them into carbon fibers (coded as PNCFs) to integrate with graphene after an activation treatment. The activation treatment is conducted by a combined process of KOH immersion and calcination. The growth of graphene is carried out on the surface of pore-rich PNCFs by virtue of plasma-enhanced chemical vapor deposition (PECVD) ,28. FiguKOH (85% purity), ethanol (98% purity), distilled water, hydrochloric acid, pure paraffin, high-purity hydrogen (99.999% purity), argon (99.9% purity), and methane (99.999% purity) are provided by Hunan FaLanKeEr Chemical Co., Ltd., Changsha, China and directly used without further purification.2 atmosphere and heated to 500 °C at a heating rate of 5 °C min−1, and heated at this temperature for 1 h. Subsequently, the material was heated to 1000 °C min−1 at a heating rate of 5 °C min−1, and maintained at this temperature for 2 h. Finally, the temperature was reduced to 500 °C min−1 at a cooling rate of 5 °C min−1, and finally cooled to room temperature naturally.Green and fresh pine needle raw materials were washed with distilled water and then placed in an oven at 60 °C for 12 h. Then, the dried pine needles were placed in a tube furnace under N−1 under the protection of argon gas (flow rate 250 scc/min) and then kept at 800 °C for 5 h. After that, the sample was naturally cooled to room temperature. Then the sample was washed with 10% HCl until the pH value approached 7. Then the sample was dried at 60 °C.A combined method of KOH immersion and calcination was used to make pores for the pyrolyzed pine needles. First, 50 mL of KOH solution was used to soak the PNCFs for 24 h (the mass ratio of PNCFs to KOH is 1:4). After being dried at 105 °C to remove moisture, the PNCFs and powder-like KOH were transferred to a nickel ark for heat treatment. The temperature was first raised from room temperature to 800 °C at a heating rate of 10 °C min2CO3, K2O, etc. Subsequently, these products further react with PNCFs. These reactions not only produce abundant new micropores but also enlarge the specific surface area [The activation mechanism using KOH as the activator can be described as follow. During the heating process, the KOH crystallizes into small particles and intercalates into PNCFs. When the temperature rises to 800 °C, the activator KOH reacts with PNCFs, leading to the production of Kace area . The relThe chemically activated samples were placed in a 250 mL WC lined vial of the planetary carrier and ground using a 10 mm diameter WC ball with a ball-to-powder mass ratio set at 4:1. Milling was performed at 300 rpm for 1.5 h. Then sieve the obtained powder to remove powder agglomerates larger than 325 microns in diameter (sieve size = 0.045 mesh) in order to obtain particles of micron size. Most of the resulting pore-rich PNCFs particle sizes are distributed between 50 and 325 µm.4/Ar flow rate ratio and the total gas pressure were set as 20 sccm/80 sccm and 400 Pa, respectively. The substrate bias voltage was 30 V and the RF power was 200 W. The graphene was deposited at 800 °C, and the deposition time was 60 min.The growth of graphene by PECVD follows our previously reported method . For theThe paraffin was first melted at 80 °C for 20 min. The liquid-stated paraffin was mixed with the pore-rich PNCFs@graphene at a weight ratio of 1:1. After the homogeneous mixing under a gentle magnetic stirring at 80 °C, the paraffin/pore-rich PNCFs@graphene composites were transferred to a mold for compression molding and naturally cooled to room temperature. The thickness of the sample was controlled at 2.000 ± 0.020 mm.−1 and 5–90°, respectively. Raman analysis was performed on a Raman spectrometer  employing a helium/neon laser (633 nm) as the excitation source. The instrument for tensile tests was an electronic universal material testing machine . Transmission electron microscope (TEM) images were acquired using an FEI, Tecnai G2 F20 TEM  with a field-mission gun operating at 200 kV. Atomic force microscopy (AFM) images were performed by Bruker Dimension Icon scanning probe microscope . The samples were ground by ball mill (PML 10). The size distribution of synthesized particles (100 mg) using a laser particle sizer .Micromorphology observations were made on a scanning electron microscope . Before the observations, the samples were adhered to the sample stage and sprayed with gold for 45 s by using an Oxford Quorum SC7620 sputter coater at 10 mA (the thickness of the sputtered gold coatings is ~5 nm). SEM digitized photographs were taken at an acceleration voltage of 15 KeV with a magnification range between 75 and 10,000 g. The chemical composition of carbon nanomaterials grown by PECVD was analyzed by energy dispersive spectroscopy (EDS). The crystal structure was studied by X-ray diffraction  with Cu Kα (target) radiation (λ = 1.5418 Å). The scan rate and scan range were set as 4° minSeaton et al.  first pr2 adsorption–desorption tests using an accelerated surface area and porosimeter system  at 77.4 K. High-purity nitrogen (99.99%) was used. Sample density was controlled at 1.000 g/cm3. All samples were degassed at 300 °C for 2 h prior to measurements.The pore structure and surface area were analyzed by NV, given in the form of the following adsorption integral equation [V(p) denotes the volume adsorbed as a function of pressure, and ρ is the model adsorption isotherm given in terms of the adsorbate density calculated for model pores as a function of pore width (w) and equilibrium pressure (p). The pore size distribution, f(w), is the distribution of pore volumes as a function of pore width. The lower and upper limit of the integration range are the widths of the smallest and largest pores  [ρ comes from the molecular model for nitrogen adsorption on the basis of nonlocal or smoothed density approximation, and it is also the kernel file in the DFT software we use.The International Organization for Standardization (ISO) recommends the use of DFT to calculate the pore size distribution . The theequation :(4)V(p)=e walls) . In this2@77K on carbon materials ” was selected.In this study, the DFT pore distribution model was calculated by using the ASIQwin software released by Quantachrome Instruments , and the model (kernel) “NS11 and transmission parameters S21 were measured. From the S-parameters, we can calculate the reflection coefficient (R), transmission coefficient (T), and absorption coefficient (A). The reflection loss (SER), absorption loss (SEA), and total shielding efficiency (SET) can be calculated by Formulas (5)–(10).A PNA-X network analyzer  was used for EMI shielding tests in the frequency range of 8.2~12.4 GHz (X-band) by coaxial method. The measuring device consists of two coaxial adapters (sample holders) and a network analyzer. The signal was coupled and transmitted through the coaxial cable and the attenuator. The paraffin/pore-rich PNCFs@graphene mixture was pressed into a ring mold  and tightly fixed in the coaxial sample holder. Based on the coaxial transmission theory, the electromagnetic wave was subjected to multiple reflections and transmissions in EMI shielding samples, hence leading to the electromagnetic energy attenuation . After mThe microstructure of the pore-rich PNCFs@graphene composite and its precursors is observed by SEM. As shown in SE). It also involves the skin depth δ where electromagnetic wave energy decreases to 1/e of the incident wave, described as δ = (πfμσ)−1/2 . In X-band (8.2–12.4 GHz), the calculated δ is in the range of 45.0–55.4 nm, which is much lower than the thickness of the graphene shell. Therefore, the graphene shell can effectively shield electromagnetic waves.The graphene shell plays a key role in EMI shielding. As a result, we study its microstructure through SEM and AFM observations. θ = ~23.2° and 43.4°, which correspond to the (002) and (100) planes of carbon structure, respectively. By comparing the XRD patterns of pore-rich PNCFs and pore-rich PNCFs@graphene, both XRD patterns show a distinct amorphous carbon structure, and the relatively intact crystal structure of graphene after growing graphene by PECVD causes the carbon (002) and (100) peaks in pore-rich PNCFs@graphene to become narrower and sharper [To further study the microstructure of composite, the crystal structure of the pore-rich PNCFs and pore-rich PNCFs@graphene are monitored by XRD a. The XR sharper ,39.−1 and 1600 cm−1 are assigned to the characteristic D-band (defects and disorder) and G-band (graphitic) bands  between the D and G bands is often used as a measure of the defect density in carbon materials. The larger the ratio of ID/IG, the higher the degree of defects in the carbon materials. After growing the graphene film, ID/IG increased significantly, and the ID/IG value increased from 1.15 (pore-rich PNCFs) to 1.21 (pore-rich PNCFs@graphene), which is caused by the graphitization edge faces (dangling bonds) exposed on the surface during the PECVD process and the additional defects generated by the active plasma [ID/IG value of the pore-rich PNCFs@graphene composite reveals a large disorder/defect density. The residual defects/groups of graphene may be beneficial for the electromagnetic wave absorption of the pore-rich PNCFs@graphene core-shell composite.Raman spectra are employed to confirm the graphitized structure and the defect density of carbon materials. The Raman spectrum of composite shows the typical signal characteristics of graphene. The peaks located at around 1350 cmc) bands b. The G-symmetry . In addie plasma . The hig2 g−1 (pine needles) to 333.2 m2 g−1 (PNCFs). A combined method of KOH immersion and calcination was used to make pores for PNCFs, and the introduction of graphene by PECVD is to make the surface of PNCFs rougher, thus further increasing the specific surface area of the material. The specific surface area of the material increases from 333.2 m2 g−1 (PNCFs) to 498.1 m2 g−1 (pore-rich PNCFs) and eventually to 699.1 m2 g−1 (pore-rich PNCFs@graphene)  . Larger 2 adsorption–desorption isotherms  to 0.22 cm3 g−1 (pore-rich PNCFs) after pore formation (3 g−1 (pore-rich PNCFs) by 1.8 times to 0.40 cm3 g−1 (pore-rich PNCFs@graphene). This is related to the growth of graphene films and the formation of core-shell structures. Thus, the final pore-rich PNCFs@graphene has a suitable porous structure. The enhancement of the surface area and pore volume of pore-rich PNCFs@graphene and its close impedance matching with free space may accelerate the incident electromagnetic waves entering the composite material. The existence of pores in the pore-rich PNCFs@graphene can also provide channels for the penetration and transmission of electromagnetic waves to promote multiple scattering, thus further increasing the attenuation of electromagnetic waves in the composite [The calculated average pore size decreases from 6.55 nm  to 2.20 nm (pore-rich PNCFs) when the calculated total pore volume increases from 0.17 cmormation . The pheomposite . The porSE, which is expressed using decibels (dB) as a unit. As electromagnetic waves propagate to the surface of the material, part of the incident power (PI) will be reflected, part of the energy will be absorbed and dissipated, and the rest (PT) will pass through the shielding material [SET is equal to the sum of electromagnetic wave reflection SER, electromagnetic wave absorption SEA, and electromagnetic wave internal multiple reflection loss (SEM), expressed as:σ means the conductivity of the shielding material, μ is the relative permeability of the shielding material, f is the frequency of a certain frequency point, d represents the thickness of the shielding material, and σ represents the skin depth of the material. Reflection loss and absorption loss are the two main mechanisms of electromagnetic shielding. The internal multiple reflection loss is the electromagnetic radiation trapped between the two boundaries of the material; that is, the electromagnetic wave reflected from the second boundary returns to the first boundary, and continues to reflect from the first boundary to the second boundary, and so on. The larger surface area of the porous structure and the larger surface-interface space contribute to providing more sites for electromagnetic wave scattering and multiple reflections. To acquire more reflected waves, shielding materials must possess abundant load carriers (electrons and holes) to interact with each other.Theoretically, for plane waves propagating to the material surface, they will be reflected and absorbed. The remaining electromagnetic wave will penetrate the material and cause multiple reflections inside the material, thereby inducing further dissipation of electromagnetic wave energy. The EMI shielding property is measured by the EMI material . The SETSEA and SER are the main ways of electromagnetic shielding. Both SEA and SER are proportional to the conductivity of the material. So the electrical conductivity is tightly responsible for the EMI shielding property. As the conductivity increases, the ability of the material to lose the incident radiation increases. According to the free electric theory, the electrical conductivity σr in an alternating electric field could be evaluated by the following Equation (15):f is frequency, ε0 is the permittivity of free space (ε0 = 8.854 × 10−12 F/m), and ε″ is the imaginary permittivity [−1, and the conductivity of the pore-rich PNCFs rises to 1.57 S cm−1. The difference between them is mainly attributed to the construction of conductive networks. The conductive networks promote the movement of charge carriers and provide more pathways for the transport of electrons in the pore-rich PNCFs, thereby improving the electrical conductivity of pore-rich PNCFs. The unique graphene “shell” in the composite is very important in improving conductivity. Due to the high conductivity of the graphene shell in the pore-rich PNCFs@graphene, the pore-rich PNCFs@graphene reaches 4.97 S cm−1 at 8.2 GHz, 216.6% higher than that of the pore-rich PNCFs (1.57 S cm−1). High electrical conductivity facilitates the efficient mobilization of charge carriers in the pore-rich PNCFs@graphene, which leads to the promotion dissipation of incident electromagnetic waves on the surface of the core-shell composite and improves the EMI SE of the composite.According to electromagnetic shielding theory, ittivity ,50. The SET) is the summation of all attenuating functions. The pine needles present an extremely low SET value, indicating that pine needles almost have no EMI shielding ability. By contrast, the PNCFs deliver a much higher SET maximum value of ~36 dB. The maximum value of SET rises to ~52 dB for the pore-rich PNCFs. As the graphene is introduced, the maximum value of SET increases to ~77 dB for the pore-rich PNCFs@graphene  [3O4@reduced graphene oxide (rGO)/natural rubber (37 dB) [To investigate the EMI shielding property of the pore-rich PNCFs@graphene, the composite is tested in the frequency range of 8.2–12.4 GHz . Generally, the total EMI shielding effectiveness (graphene a. Such a (28 dB) , PEI/PA@ (28 dB) , honeyco (28 dB) , loofah  (28 dB) , Fe3O4@r (37 dB) , rGO/pol (37 dB) , multi-w (37 dB) , ALC bas (37 dB) , SC-Co-G (37 dB) , and cot (37 dB) , as summX% is the percentage of the difference between the energy in the shielding field and the energy outside the field relative to the energy in the field. For example, 99%, that is, the radiation intensity of the radiation source is shielded by 99%. EMI shielding effectiveness (dB) is converted into EMI shielding effectiveness (%) using Equation (16) as follows:EMI shielding efficiency is usually expressed as a percentage (%) to indicate the ability of a material to block waves. The percentage shielding effect After substituting the data into the calculation, the pore-rich PNCFs@graphene shows a shielding efficiency of 99.9999%. This shows that the composite can shield most electromagnetic waves.A (A = SEA/SET) of PNCFs, pore-rich PNCFs, and pore-rich PNCFs@graphene are further analyzed. A can be approximately expressed as [To estimate the mechanism of material dissipating electromagnetic waves more accurately, the EMI absorptivity essed as ,59:(17)ASEA and SER can be expressed by Equations (18) and (19):d is the thickness of the materials, μr is the magnetic permeability, ω is the angular frequency, σAC is the frequency-dependent conductivity. The SET, SER, and SEA values of the PNCFs, pore-rich PNCFs, and pore-rich PNCFs@graphene are presented in A increases from 83.6% for PNCFs to 85.8% for the pore-rich PNCFs and eventually to 90.8% for the pore-rich PNCFs@graphene .graphene d, shows e X-band ,61. Cleaε = ε′ − iε″) and permeability (μ = μ′ − iμ″). The real part of the electromagnetic parameters  is a measure of the polarization amount and means the capability for electric or magnetic energy storage, while the imaginary part  represents the energy loss ability. As seen in ε′ decreases with the increase in frequency. This behavior is raised by the lag of the induced electric field reversed external electric field at high frequency [ε′ increases obviously. The result indicates the remarkably improved electric energy storage capacity of the composite.The electromagnetic property of the pore-rich PNCFs@graphene is determined by complex permittivity  to 498.1 m2 g−1 (pore-rich PNCFs) and eventually to 699.1 m2 g−1 (pore-rich PNCFs@graphene). Larger surface area and interface space provide more sites for electromagnetic wave scattering and multiple reflections.By activation and PECVD treatment, the specific surface area of the material increases from 333.2 m3 g−1 (pore-rich PNCFs) to 0.40 cm3 g−1 (pore-rich PNCFs@graphene). This porous structure will facilitate the multiple reflections of the incident electromagnetic waves.After the growth of graphene with the help of PECVD, total pore volume increased 1.8-fold from 0.22 cm−1 (PNCFs) to 4.97 S cm−1 (pore-rich PNCFs@graphene).The generation of conductive networks promoted by the growth of graphene, the as-prepared core-shell structure has a greatly improved conductivity from 1.13 S cmBecause of the combination of high electrical conductivity and unique core-shell porous structure, the PNCFs@graphene composite delivers a high EMI shielding effectiveness of ~77 dB, much higher than its precursors , respectively.Due to the enhanced surface area and pore volume of porous PNCFs@graphene to promote the dissipation of electromagnetic waves in the composite, the absorption ratio increases from 83.6% for PNCFs to 85.8% for the pore-rich PNCFs and eventually to 90.8% for the pore-rich PNCFs@graphene. This absorption-based shielding mechanism will have great potential for developing stealth materials.From the results obtained in this investigation, the following conclusions can be drawn:


Bipolaris sorokiniana (B. sorokiniana) and graphene oxide (GO) were selected for materials. A combination of physiological, cytological, and biochemical approaches was used to explore how GO affects the growth and pathogenicity of B. sorokiniana. The mycelial growth and spore germination of B. sorokiniana were both inhibited in a dose-dependent manner by GO treatment. The addition of GO significantly alleviated the infection of pathogenic fungi in host plants. The results of scanning electron microscopy demonstrated that the inhibitory effect of GO on B. sorokiniana was primarily related to the destruction of the cell membrane. Our study confirmed the antifungal effect of graphene in vitro and in vivo, providing an experimental basis for applying graphene in disease resistance, which is of great significance for agricultural and forestry production.The antimicrobial properties of graphene in vitro have been widely reported. However, compared to research performed on graphene’s antibacterial properties, there have been relatively few studies assessing graphene’s antifungal properties. In particular, evaluating graphene’s pathogenic effects on host plants in vivo, which is critical to using graphene in disease control, has rarely been performed. In this study, the fungal pathogen of wheat, barley, and other plants,  Graphene is a typical two-dimensional carbon nanomaterial and is famous for its excellent electronic conductivity, high thermal stability, high surface area, fine water dispersibility, and outstanding mechanical properties ,2. It haStaphylococcus aureus (S. aureus)) and Gram-negative (Escherichiacoli (E. coli), Pseudomonas aeruginosa (P. aeruginosa), Ralstonia solanacearum (R. solanacearum), Pseudomonas syringae (P. syringae), Xanthomonas campestris pv. undulosa (X. campestris pv. undulosa), and Xanthomonas oryzae pv oryzae (Xoo)) bacteria [Aspergillus niger, Aspergillus oryzae, Fusarium graminearum (F. graminearum), and Fusarium oxysporum (F. oxysporum) were demonstrated to be inhibited by GO or rGO [3O4 nanocomposites on grapevine leaves can reduce downy mildew severity under field conditions [Graphene and graphene-based materials (graphite (Gt), graphite oxide (GtO), graphene oxide (GO), and reduced graphene oxide (rGO), etc.) have different antimicrobial activities, including anti-bacterial, anti-fungal, and anti-viral properties ,14. For bacteria ,21,22,23O or rGO ,24. AddiO or rGO , HepatitO or rGO , PseudorO or rGO , etc.) wnditions .E. coli among four graphene-based materials  [E. coli is particularly vulnerable to GO as it is growing, which is largely due to alterations in the cell envelope ultrastructure [Many factors affect the antimicrobial abilities of graphene. As reviewed by Fatima et al. and Radhi et al., its physicochemical traits, including size, physical structure, surface structure, and electronic structure, the combined physiological properties of microorganisms, and interaction conditions influence graphene’s antimicrobial properties ,23,28. TGO, rGO) . AdditioGO, rGO) ,22. Impotructure . In additructure ,32.Currently, several graphene antimicrobial mechanisms have been proposed, including sharp edge cutting, lipid extraction, oxidative stress, cell trapping, and parcel isolation ,28. For Bipolaris sorokiniana (B. sorokiniana) is generally considered a hemibiotrophic pathogen related to root rot, leaf blotching, and black embryos in wheat and barley, among other grass species [B. sorokiniana is widely distributed worldwide, bringing immeasurable losses to the yield and quality of many cereal crops. B. sorokiniana could infect wheat across the whole growth period [B. sorokinian can infect many parts of host plants, such as roots, stems, leaves, and grains [ species ,39. B. sh period . The exih period . B. sorod grains . In pracd grains ,44. Mored grains . In addiB. sorokiniana, a pathogen of a serious plant disease (wheat root rot), in this study. Our results demonstrate that GO could provide broad-spectrum antimicrobial activity against pathogenic fungi and pave the way for the application of GO in preventing plant diseases.To further understand the mechanism behind graphene’s inhibition of fungal pathogens and analyze the effects of GO on host infection in vitro and in vivo, we selected 2, respectively. After 150 h of reaction, the graphene oxide suspension with a mass concentration of 0.5% was obtained. The GO suspension was then characterized by Raman spectra and scanning electron microscopy [GO suspension was synthesized by an electrochemical method in our laboratory, as in previous studies . Two elecroscopy ,11.B. sorokiniana Lankao 9-3 was provided by Dr. Shengli Ding’s laboratory from Henan Agricultural University, as previously described [escribed  and was B. sorokiniana was obtained after 7 d of incubation. Photographs were taken, and the diameter was recorded. The mycelial growth inhibition rate was calculated using the following formula:The growth curve of the mycelia was tested by following previously reported standard procedures . The strMycelial growth inhibition rate (%) = (Colony diameter of test treatment − Colony diameter of CK)/(Colony diameter of CK − 1 cm) × 100The mycelial biomass of the fungi was measured following standard procedures. Three pieces of fresh mycelia were transferred to a 50 mL liquid PDA medium containing 0, 50, 100, 200, and 500 mg/L GO in conical flasks. After incubation in shaking table for 4 d at 28 °C and 200 rpm, 1 mL of the culture was removed for subsequent mycelial morphology observation. The mycelia were filtered, repeatedly washed with distilled water, and dried to a constant weight of 50 °C. The antifungal experiments were performed in a completely random design with three replicates.B. sorokiniana spores were obtained as previously described [B. sorokiniana were incubated on a PDA medium for 5 d at 28 °C under alternating light and dark conditions. Then, the spores were collected by washing the mycelia with distilled water and centrifuging at 3500 rpm for 5 min. The spore suspensions were adjusted to a concentration of 5 × 104 spores per ml.escribed . The mycTo measure the germination rate and the length of the germ tubes, 100 µL spore suspensions were mixed with GO and PDA liquid medium in tubes to produce a final concentration of 0, 50, 100, 200, and 500 mg/L GO. 50 µL mixture was taken and transferred onto grooved slides at 28 °C for 7 h incubation. The incubation time was extended to 20 h for the mycelial branching observation. Three grooved slides were prepared for one mixture in each treatment, and the average values were compared. One hundred spores were observed under a microscope to analyze the germination rate, the length of the germ tubes, and the mycelial branching of different mixtures. Micrographs were taken, and the number of the germinated spores, the length of the germ tubes, and the mycelial branching were recorded. The experiments were performed in a completely random design with three replicates. The spore germination rate was calculated using the following formula:Spore germination rate (%) = The number of germinated spores/Total number of spores × 100.The culture liquid isolated from the biomass assay experiment was diluted 10 times with pure water for light microscope observation. A drop of diluted culture was placed on a concave slide and covered by the slide, then placed directly under the light microscope.B. sorokiniana spore suspensions were mixed with GO to a final concentration of 500 mg/L and incubated for 3 h at 28 °C. The control samples were mixed with distilled water. The mixtures were centrifuged at 3500 rpm for 5 min, and the spore cells were collected and fixed with 2.5% glutaraldehyde for 2 h and washed with 0.1 M pH 7.0 phosphate buffers three times. The spore cells were then dehydrated by a series of ethanol solutions  and dried in a vacuum freeze drier. The samples were then observed under the SEM.Changes in the morphology of the spores after GO treatment were observed via scanning electron microscopy (SEM). The Cell contents such as DNA and RNA are discarded in damaged spore cells, which can be monitored by UV absorption at 260 nm. A mixture of 200 μL GO in different concentrations  with 2 mL spore was cultured in stasis for 3 h at 28 °C. The mixtures were then filtered with a 0.22 µm filter membrane, and the filtrates were tested by absorbance at 260 nm.3 was added to the spore suspensions for 2 h to measure the total electrolyte loss.An electrolyte leakage experiment was performed, as previously described . ElectroTo assess the disease severity after fungal infection in wheat leaf inoculation experiments, 10 µL spore suspension was sucked onto the 10 d old wheat leaves, which were kept humid by covering them with clingfilm for 24 h at 28 °C under dark conditions. A total of 1 mL 500 mg/L GO solution was sprayed onto the inoculated leaves and 1 mL distilled water was sprayed onto the control leaves once a day for 3 d in a 25 °C constant temperature incubator. Lesions on the inoculated leaves were recorded by a camera, and the area of the disease spot was measured with Image J.B. sorokiniana and placed in a constant temperature incubator at 25 °C for mycelial propagation. After 6–7 days of cultivation, the millet medium with mycelia was naturally dried. In parallel, the blank millet medium was also placed at the same place as a control. Sterilized soil and the millet medium were mixed at a 3:1 ratio, and the mixture was placed in clean pots for plant growth. Wheat seeds were sterilized in 0.5% sodium hypochlorite for 15 min and washed three times with sterilized water. The sterilized seeds were covered with two sheets of moist filter paper and placed in 25 °C in darkness for 24 h to accelerate germination. Germinated wheat seeds were washed with water and 500 mg/L GO solution for control and GO treatment, respectively. Then, the seed was planted into the soil and irrigated with water and 500 mg/L GO every seven days. Plants were grown in a culture room at 23 °C under a 16-h-light/8-h-dark photoperiod. Three repetitions were randomly set for each treatment. The aerial part and root of plants were observed and photographed after 14 d. The plant height was measured by a ruler. The root was washed with water to analyze root morphology with WinRHIZO software  after scanning [Pot inoculation experiments were conducted following the procedures used by Shengli Ding’s lab. Millet was boiled in water for 2 min and dried until it was semi-dry, after which it was placed in a conical flask for sterilization. The millet medium was inoculated with scanning .p value < 0.05 or <0.01.Every experiment was conducted in a completely random design with three repeats. The results are displayed as mean ± standard deviation (SD). Statistical analysis was performed by SPSS 21 software. Significance differences between groups were analyzed by one-way analysis of variance and determined by a B. sorokiniana was examined by measuring the growth curve and dry weight of the fungal mycelium exposed to different concentrations of GO . As shown in B. sorokiniana.The antifungal activity of GO to Spore germination is the first crucial stage in the return of spores to vegetative growth and is key to the spread of pathogenic fungi . As showB. sorokiniana.After spore germination and the formation of the germ tube, hyphae, and other structures, the number of mycelial branches continuously increased . The effB. sorokiniana, SEM was used to observe the morphological changes of spores after GO treatment. As shown in Previous studies have demonstrated that GO could disrupt the cell membrane of bacterial and fungal spores, altering the membrane potential and membrane-associated energy transducing system . To explB. sorokiniana to wheat, a leaf inoculation experiment and pot inoculation experiment were performed. As shown in B. sorokiniana spores caused disease varying degrees of disease spots in wheat leaves. GO application significantly reduced the area of the disease spot by 53%  demonstrated the antifungal activity of GO.Most reports have investigated the antimicrobial effects of graphene in vitro ,54,56,57viticola ,27. In cThe prevailing view for the antifungal mechanism of graphene as follows: (i) destruction of cell membranes caused by mechanical cutting, (ii) environmental isolation caused by a wrapping effect, and (iii) oxidative stress . GraphenB. sorokiniana both in vitro and in vivo. In vitro experiments, the mycelial growth and spore germination of B. sorokiniana were both interfered by GO treatment in a dose-dependent manner. In vivo experiments, GO treatment significantly reduced the pathogenicity of the pathogen in host plants. It is worth mentioning that this in vivo anti-fungal effect of GO is rarely reported in the existing literature. Finally, the results of scanning electron microscopy showed that the anti-fungal effect of GO on B. sorokiniana was mainly depended on the destruction of cell membrane. However, further study is still needed. First, it is necessary to clarify the molecular effects of GO treatment on gene expression and metabolite changes of B. sorokiniana. Secondly, research on the synergistic effects of graphene with other technologies, such as pesticides, is needed to maximize the effectiveness of its disease resistance. Finally, close attention must be paid to how graphene treatment affects plant growth and development to avoid potential ecological crises.This study provides evidence that appropriate GO concentrations can exhibit excellent antifungal properties on


Brain-derived neurotrophic factor (BDNF) is a neurotrophin(NT)essential for neuronal development and synaptic plasticity. Dysregulationof BDNF signaling is implicated in different neurological disorders.The direct NT administration as therapeutics has revealed to be challenging.This has prompted the design of peptides mimicking different regionsof the BDNF structure. Although loops 2 and 4 have been thoroughlyinvestigated, less is known regarding the BDNF N-terminal region,which is involved in the selective recognition of the TrkB receptor.Herein, a dimeric form of the linear peptide encompassing the 1–12residues of the BDNF N-terminal (d-bdnf) was synthesized. It demonstratedto act as an agonist promoting specific phosphorylation of TrkB anddownstream ERK and AKT effectors. The ability to promote TrkB dimerizationwas investigated by advanced fluorescence microscopy and moleculardynamics (MD) simulations, finding activation modes shared with BDNF.Furthermore, d-bdnf was able to sustain neurite outgrowth and increasethe expression of differentiation  and polarization markers demonstrating its neurotrophic activity. As TrkB activityis affected by zinc ions in the synaptic cleft, we first verifiedthe ability of d-bdnf to coordinate zinc and then the effect of suchcomplexation on its activity. The d-bdnf neurotrophic activity wasreduced by zinc complexation, demonstrating the role of the latterin tuning the activity of the new peptido-mimetic. Taken togetherour data uncover the neurotrophic properties of a novel BDNF mimeticpeptide and pave the way for future studies to understand the pharmacologicalbasis of d-bdnf action and develop novel BDNF-based therapeutic strategies. They exert their function generallyacting as homodimers held together by non-covalent bonds and bindingtwo different membrane receptors: the p75 NT receptor (p75NTR) common to all NTs, and the tropomyosin receptor kinase (Trk) familythat selectively recognizes different NTs .2The neurotrophin (NT) family of growthfactors consists of nervegrowth factor (NGF), brain-derived neurotrophic factor (BDNF), neurotrophin3 (NT-3), and neurotrophin 4 (NT-4).3 Activation of TrkB by BDNF initiates downstream signalingcascades mediated by Ras/ERK, PI3K/Akt, and PLC-γ.4 After its activation, the BDNF–TrkB complexundergoes endocytosis, recycling, or degradation and crucially takesthe axonal transport route to sustain neuron survival and differentiation.5 Furthermore, BDNF/TrkB signaling regulates dendriticbranching, the density of spines, and spine morphological specializations,acting both as the mediator and modulator of synaptic plasticity andcommunication in the CNS.7 Intriguingly, severaldifferent molecules such as ATP, G-protein-coupled receptor ligandsand metal ions can modulate either NTs or their receptors, thus enrichingthe panorama of signaling cues involved in neurotrophic support.9 For the BDNF–TrkB signaling axis, an interesting role isplayed by zinc, an essential metal ion in brain physiology.10 Zinc is released from synaptic vesicles of glutamatergicneurons reaching concentrations up to 100 μM in the synapticcleft,12 where it was reported to prompt post-synaptic,BDNF-independent TrkB activation.13 Zinctransactivates TrkB promoting its phosphorylation through the activationof the Src family kinase by relieving their TrkB inhibition. Lessis known regarding the direct effect of zinc on mature BDNF, it wasonly recently reported that zinc binds the pro-domain of the unprocessedBDNF precursor, in particular its Val66Met polymorphic variant14 modulating synaptic remodeling and plasticity.BDNF is the predominant NT in the adult brain and is widely distributedin the cortical areas, visual cortex, and hippocampus.16 Unfortunately, thetherapeutic use of BDNF, as that of other NTs, is challenging dueto the uncontrolled side effects as well as their low bioavailability.17 To overcome such limitations, the use of peptidefragments that can mimic specific domains of the NTs has been exploredin the last few years.18 The most investigatedBDNF structural elements have been so far loop 220 and loop 421 to achieve TrkB activationand the PAKKR sequence that recognizes the p75NTR receptor.22 Interestingly, dimeric versions of these peptides were reported to display an improvedability to activate TrkB and promote neuronal survival.The modulation of the BDNF–TrkB signaling axis has beenrecently postulated as a drug target for the treatment of neurologicaldiseases, including nerve injury, neurodegenerative diseases, andneuropsychiatric disorders.27 However, most of these information were obtainedfrom the NGF-TrkA system.29 On the other hand,the BDNF N-terminal region has been less investigated. Our group previouslyreported that a linear peptide fragment encompassing the 1–12residues of BDNF N-terminal displays proliferative capacity in undifferentiatedcells; however, the relevance of this observation in the context ofneurodevelopment has not been assessed, yet.31 Moreover, whether zinc also modulates the neurotrophic activityof the BDNF peptide-mimetics, as documented for the full-length proBDNFprotein, has remained so far mostly unexplored.Previousfunctional studies agree in identifying the N-terminalregion as a pivotal NT domain for the binding selectivity and activationof Trks.Herein, peptidesthat mimic the BDNF N-terminal region were investigated.We demonstrate that a dimeric peptide (d-bdnf) bearing two BDNF(1–12)pairs was able to activate TrkB promoting a neurotrophic effect. Interestingly,zinc coordination by d-bdnf inhibited the peptide effects, thus suggestingthat the metal ion may tune the activity of d-bdnf at synapses orbrain regions where zinc concentration is high.31 Here, we namedthis sequence as m-bdnf (Escherichia coli(p < 0.05) when administered atμM concentrations, although not at comparable levels of thenM BDNF concentration  in accordance with the literature data.15 The d-bdnf peptides showed an EC50 value of 13.63 ± 3.87 μM. Interestingly, the BDNF andd-bdnf maximal effects (Emax) of ERK phosphorylationdid not significantly differ . The same result was not obtained whenBDNF and d-bdnf were administered to HEK 293 cells, which do not expressTrkB . Taken together, theseresults highlight that the dimeric peptide acts as a specific agonistof TrkB, although at higher concentrations with respect to the full-lengthNT. Then, to better elucidate if d-bdnf could act as a partial antagonistor produce additive effects, competition experiments were done . Specifically, an EC50 concentration of BDNF (1 nM) or of d-bdnf (10 μM) was usedin combination with the increasing concentration of d-bdnf and BDNF,respectively, and the increase of ERK phosphorylation was assessed.As reported, both combinations were able to reach the maximal effectssimilar to that of BDNF alone with no significant differences. Thesedata highlight that d-bdnf had neither antagonist nor additive effects.Of note, we also performed a non-permeabilizing immunofluorescenceanalysis to quantify the membrane-bound BDNF in differentiated SH-SY5Ycells in the absence or presence of BDNF and d-bdnf . A 1 h treatment with a saturating dose of BDNF(150 ng/mL) led to a ∼40% increase of the fluorescent signalon the cells, demonstrating that endogenous BDNF produced by the cellline does not saturate TrkB in our experimental setup. The same incrementwas not observed upon treatment with the d-bdnf peptide, while insteadwe measured a slight (∼18%) decrease of the signal possiblydue to the ability of the peptide to displace endogenous BDNF at thetested dose (10 μM). Interestingly, d-bdnf co-administered withBDNF was able to decrease by ∼16% membrane-bound BDNF levels,further suggesting the possibility that the two compete for the samebinding sites on the TrkB receptor. Future analysis, however, willbe necessary to understand if d-bdnf occupies the same orthostericsite of BDNF.The possibility of mimicking the functionof the full-length BDNF protein by using a linear peptide fragmentencompassing the first 12 amino acids of the mature NT N-terminalwas already postulated.s m-bdnf 1A and pechia coli and(i) chia coli1A in SH-ntration 1B. Neverf d-bdnf 1D. As ex35). By analyzing the N&B map for different regions of the cellcorresponding to the TrkB membrane pool  recognition by d-bdnf, MD simulations were performed.The conformational features of d-bdnf were obtained from paralleltempering simulations, and the most representative five clusters arereported in 31Notably, the dimeric peptide scaffold of d-bdnfpreserves the alpha-helixcontent differently from the m-bdnf previously investigated.Table S1 summarizes the main hydrogen bonds observed in theanalyzed conformations, indicating the amino acid residues participatingin this weak interaction. Asp298, Thr290, and Asn338 are the residuesbelonging to the TrkB receptor largely involved in non-covalent interactionswith Ser2, Arg7, Ser11, and Glu23 of d-bdnf.The predicted binding poses of the TrkBreceptor with d-bdnf arereported in Table S2. In line with the binding with one receptor chain,one TrkB receptor contacts the d-bdnf peptide via Ser294, Asp298,His299, Lys308, Asn338, and Gln347. The second chain of TrkB facesthe d-bdnf and maintains a lower similarity with the receptor monomerbinding modes, specifically through Asp298, Lys308, His343, and His299.The dimer peptide contacts the receptor mainly via Ser2, Arg7, Ser11,Arg20, Arg21, and Ser25.We then proceededto assess the binding poses of the d-bdnf incomplex with two chains of TrkB, starting from the binding poses sketchedin The reported data suggest an asymmetricinteraction of the d-bdnfwith the TrkB receptor, since one chain of TrkB is more prone to contactthe peptide. On the whole, d-bdnf is predicted to bind with one chainof TrkB, leaving the second half to weakly interact with a secondchain of the TrkB receptor. These data are in agreement with the transientdimerization of TrkB in the presence of d-bdnf measured in livingcells by the N&B technique 1G.37 We used a two-step differentiationprotocol . We can thus conclude that higher concentrations of d-bdnf do activate pTrkB and other pathways in relativelyshort-term assays (S1) but are less effectivein relatively longer-term assays such as neurite outgrowth. This couldlikely be related to the kinetics or duration of TrkB activation promptedby d-bdnf, or to biased signaling, or desensitization, and could bematter of future investigations.We next tested the efficiency of neurotrophic activity displayedby BDNF peptides, in comparison to the full-length BDNF. To this purpose,we used differentiated SH-SY5Y cells as a bona fide model of neuronaldifferentiation. of BDNF 3B. These of BDNF 1 and 2 im assays 1 and S1 39 Thus, we implemented the assessment of d-bdnf neurotrophic activityby evaluating its ability in increasing the neuronal cell fractionof differentiated Ren cells with respect to BDNF , derived from the ventral mesencephalic region of the developinghuman brain, into functional neurons was reported. to BDNF 3C,D. The41 Also, when analyzing genes involved in neuronal polarization, whichis a feature specifically acquired by SH-SY5Y cells in the last 3days of the NT treatment,37 we found thatd-bdnf could prompt the expression of genes involved in dendrite (MAP2)and axon (MAPT) specification to a similar extent to BDNF. Nevertheless,NEFM gene expression was significantly increased only at the highestd-bdnf dose (10 μM); and GAP43, a recognized marker of developingand regenerating axons, was not significantly increased by eitherthe d-bdnf doses tested. The latter is probably the most evident differencein the gene expression pattern induced by BDNF and d-bdnf. A possibleexplanation for this difference is the lack of p75NTR activationprompted by d-bdnf stimulation when compared to BDNF. Indeed, p75NTR has a fundamental role in axon outgrowth and branching.43To understand if the BDNF and d-bdnf share similarmechanisms ofdifferentiation, we next performed a gene expression analysis of differentiatedSH-SY5Y cells 4A. As sh14 we investigated whetherand how Zn2+ ions could modulate the neurotrophic activityof d-bdnf peptide. Indeed, metal binding to the m-bdnf was previouslydemonstrated.30 To this purpose, we firstperformed high-resolution mass spectrometry on d-bdnf  to gain precise information on thenature of the adducts that are formed, including the binding stoichiometry.452+ and addition of 0.1% formic acid immediatelybefore injection to enhance the ionization of the investigated molecules.We can observe a modest formation of adducts featured by one boundZn2+ ion. Specifically, the peak at 2911.288 Da . In this respect, though the dimericstructure, the behavior of d-bdnf  was different from the expected coordination of twoZn2+ ions. Also, both for the m- and d-bdnf cases, therelative percentages of the formed adducts is quite low and the mainpeak is that of the non-coordinated peptides. Noteworthy, a signalat 2946.327 Da corresponding to the adduct with the sulphate ion,deriving from incubation with ZnSO4, is present in caseof d-bdnf . Interestingly,despite the presence of multiple adducts with sulphate ions, no multipleadducts with Zn2+ ions were detected, further strengtheningthe hypothesis that the His residue can likely represent the mainzinc binding site. The experimental evidence that both the m-bdnfpeptide and d-bdnf coordinate one metal center—despite thepresence of two available histidine residues—might be attributedto the higher conformational freedom compared with m-bdnf, leadingto folded structures . The protonation andcomplex stability constant values are reported in Table S3. The ligand displays eight protonation constants,and the pK values are similar to those reported forthe monomer peptide according to the maintenance of similar conformationalfeatures.31 The four protonation equilibriaof the aspartic and glutamic residues partly overlap but, in agreementwith the literature data, the aspartic β-carboxylic group ismore acidic than glutamic γ-carboxylic one.48 The potentiometric titrations were carried out exploring0.9:1 and 2.2:1 metal to ligand molar ratio. Only mononuclear complexspecies were observed then the peptide d-bdnf is able to bind onezinc ion, according to ESI-mass and CD data. The peptide forms twocomplex species, [ZnLH] and [ZnL]. The first one is the predominantat pH = 6, and the calculated stability constant value [logK(111) = logβ(111) – logK(011)= 5.71] indicates the involvement of three nitrogen atoms in the metalcoordination environment. The [ZnL] species display a stability constantvalue, logβ = 7.15, higher than that of protonated species indicativeof the involvement of a further nitrogen atom in the metal bindingmode. In this complex species, Zn2+ is bound to four nitrogenatoms with the involvement of two amino and two imidazole .Potentiometric measurementswere carried out to give insight intothe coordination environment of Zn2+ metal ion was reported in 2+ metal ionwithin the d-bdnf. The Zinc coordination polyhedron involves two nitrogenatoms belonging to the two HIS1 residues of the d-bdnf and their NH2-groups. The oxygen atoms of the side chain belonging to ASP3 may assist this coordination. The root mean squared deviationfrom the apo-peptide is calculated as 4.9 Å, indicating a reorganizationof the dimer upon the zinc ion coordination that involves mainly theN-terminal domain of the two chains, that remain flexible and in adisordered conformation, in agreement with the CD data.Precisely thiscoordination mode explains why d-bdnf does not bindtwo metal ions; the formation of macrochelate, that involves the nitrogenatoms of the N-terminal domain of the two chains simultaneously, preventseach single chain from bonds an ion with the same coordination ofthe monomer. In this regard, MD simulations were performed and themost representative conformation of d-bdnf coordinating the ZnTheobtained metalation data prompted us to investigate if thepre-formed Zinc/d-bdnf complexes influence neurotrophic activity whencompared to the unbound peptide. When analyzing the neurite outgrowthinduced in differentiated SH-SY5Y cultures by d-bdnf 2B,C, we In this work, we tested the potential ofa dimeric peptide constitutedby 2 copies of the first 12 amino acids of the BDNF sequence heldtogether by a cysteine bond to act as a neurotrophic mimetic peptide.Results indicate the ability of d-bdnf to promote phosphorylationof the TrkB receptor and downstream ERK and AKT effectors. This correlatedwith its ability to promote TrkB dimerization, although in the contextof a dynamic monomer-dimer equilibrium, which resembles the one occurringwith BDNF stimulation. This is supported, at least for the d-bdnfcase, by the asymmetric interaction with two TrkB moieties as predictedfrom MD simulations. Whether this mode is conserved for the BDNF bindingconstitutes an exciting avenue for future investigations. Furthermore,d-bdnf was able to sustain neurite outgrowth and increase the expressionof differentiation  and polarization markers demonstrating its neurotrophic activity.12 The d-bdnf activity revealed to be decreased by equimolar amountsof coordinated zinc, demonstrating the role of this metal ion in tuningthe activity of this peptido-mimetic.In the synaptic cleft,the released free metal ions can reach upto micromolar concentrations and can modulate directly or indirectlythe activity of several post-synaptic receptors or can bind differentproteins and peptides modulating their activity.Overall, our data identifya novel BDNF mimetic peptide and uncovernovel mechanistic details in its activation of the TrkB receptor.Although other investigations will be required to characterize infull its pharmacological properties, we report here the advantageof d-bdnf peptide of being a linear sequence, which can be straightforwardlyimplemented in higher-order constructs, for example, nanoparticlesor other vectors. Its in-depth analysis may open the way to severalpossible future BDNF-based therapeutic applications.43 The peptides HSDPARRGELSV-NH2 (m-bdnf)and the scrambled SRAGPHLRDVSE-NH2 (s-bdnf) were assembledusing the solid phase peptide synthesis strategy on a Pioneer PeptideSynthesizer as previously described.31 Thedimer peptide HSDPARRGELSVC-C-VSLEGRRAPDSH (d-bdnf) was purchasedfrom CASLO . The human proBDNF cDNA was cloned inthe prokaryotic expression vector pET11a as previously reported forproNGF construct.50 The preparation of mature hBDNFhas been recently described in detail.32The full-length human TrkB cDNA was cloned as C-terminal GFP fusionin pReceiver-M03plasmid (GeneCopoeia), to get the TrkB-eGFP construct.The farnesyl-eGFP construct (f-EGFP) has been previously described.32 Briefly, we calculated the totalneurite length per cell, that is, the ratio between the sum of thelengths of all neurites and the number of cell somas detected in eachfield using the Imagej software.Atthe end of the differentiation protocol, SH-SY5Y cells were observedusing an optical microscope equipped with a 20× magnificationobjective. Typically, cells were seeded in 24 well-plates and at least2 different wells were subjected to the same treatment in up to fourindependent replicas. For each well, we acquired 2–3 differentfields to perform a morphometric analysis of SH-SY5Y differentiation.For each field, we quantified the neurite density as reported in Convertinoet al.5 cells per 22-mm-diameter dish). The next day, cells wereserum starved in DMEM with 4.5 g/L glucose for at least 2 h beforeimaging. N&B measurements were performed by using an Olympus FluoView1000 confocal equipped with pseudo-photon-counting detectors. Eachacquisition consists of a time series of 100 frames, 256 × 256-pixelseach, with a pixel size of 50 nm and a pixel dwell time of 4 μs.EGFP was excited with a 488-nm laser line, while its emission wascollected in the 500–600-nm range. Low laser power was usedat the sample to avoid photobleaching. The N&B analysis was performedusing the SimFCS software V 2.0. Cells transiently transfected withf-EGFP were used to identify the appropriate experimental conditions for the measurement of a membrane-diffusing protein. Additionally,f-EGFP was used as a monomeric reference. In total, 150 ng/mL BDNFand 10 μM d-bdnf treatments were added directly on the Willcoplate, after which cells were observed within a 30 min time window.Measurements of the immobile fraction  wereperformed to calibrate the S-factor of the microscope pseudo-photon-countingdetector. As described in ref  were taken from the X-ray structure of TrkB-D5 boundto NGF (pdb code 1HCF).4 hydrated was purchased from Merck and used withoutfurther purification. LC–MS grade water was used. All the solutionswere freshly prepared before the incubation. Stock solutions (10–2 M) of peptides and ZnSO4 were freshlyprepared in LC–MS grade water. Each peptide solution was dilutedto a final concentration of 10–6 M and incubatedfor 2 h at 37 °C in the presence of a 100-fold excess of Zn.Aliquots of the various solutions were sampled after the incubationtime and analyzed with or without the addition of 0.1% of formic acidbefore injection. Experimental parameters: spectra were acquired throughdirect infusion at 5 μL/min flow rate in a TripleTOF 5600+ mass spectrometer , equippedwith a DuoSpray interface operating with an ESI probe in positivepolarity. The ESI source parameters were optimized and were as follows:positive polarity, ionspray voltage floating 5500 V, temperature 30°C, ion source gas 1 (GS1) 45 L/min; ion source gas 2 (GS2) 0L/min; curtain gas (CUR) 15 L/min, declustering potential (DP) 100V, collision energy (CE) 10 V. For acquisition, Analyst TF software1.7.1 (Sciex) was used and deconvoluted spectra were obtained by usingthe Bio Tool Kit micro-application v.2.2 embedded in PeakView softwarev.2.2 (Sciex).ZnSO


For these kind of networks we encounter significant differences between networks with an even or odd number of hexagons along one of the axes.We study the transport properties on honeycomb networks motivated by graphene structures by using the continuous-time quantum walk (CTQW) model. For various relevant topologies we consider the average return probability and its long-time average as measures for the transport efficiency. These quantities are fully determined by the eigenvalues and the eigenvectors of the connectivity matrix of the network. For all networks derived from graphene structures we notice a nontrivial interplay between good spreading and localization effects. Flat graphene with similar number of hexagons along both directions shows a decrease in transport efficiency compared to more one-dimensional structures. This loss can be overcome by increasing the number of layers, thus creating a graphite network, but it gets less efficient when rolling up the sheets so that a nanotube structure is considered. We found peculiar results for honeycomb networks constructed from  Problems such as the configurational properties of polymers6, kinetic chemical reactions7, diffusion9, or epidemic spreading of diseases11 are explained through RW models. For a growing number of phenomena, however, quantum effects can no longer be neglected. Thus, in the last years, efforts have been tremendous to consider quantum mechanical variants of the RW, of which Quantum Walks (QWs) on networks are a prime example. This framework can be related to a number of real-world problems, such as coherent and incoherent quantum dynamics of excitations in complex systems16, dynamics on quantum graphs19, quantum search algorithms, quantum information or quantum computation27. By exploiting the interference of quantum waves, combined with measurement, quantum search algorithms show a polynomial or even an exponential increase in the speedup28, when compared to their corresponding classical counterparts. This impressive result has additionally started an avalanche of experimental work related to this problem47.Transport properties of materials and structures are an active field of research in physics, chemistry, biology, and computer science. Many such phenomena of complex dynamics in the classical realm can be modeled by a classical Random Walk (RW)48 can be studied by using two distinct models: the discrete-time quantum walk (DTQW)49 and the continuous-time quantum walk (CTQW)50. It was shown that these two models are not independent51. Moreover, there are alternatives to these two basic models, such as the quantum stochastic walk54 and a Green’s function approach for quantum graphs56. In the CTQW model, which is our chosen model, the dynamics of the quantum walk is solved by identifying the Hamiltonian with the transfer matrix of the network, which is proportional to the discrete Laplacian. As in the classical model, in the CTQW model the transfer matrix of any undirected network is associated to the connectivity matrix2 of the underlying graph. Then, all quantum transport properties can be obtained directly from its eigenvalues and eigenvectors. We evaluate the efficiency of transport through the probability to return to the starting node after a given time, and also consider the corresponding long-time average return probability.Similar to the classical case, quantum transport68. Here, we use honeycomb networks, motivated by graphene structures. Since the successful isolation of a single sheet of graphene69, many of its unusual properties76 were encountered and exploited in various applications80. In this report we consider quantum transport (CTQWs) on graphene and its most relevant carbon structures, motivated by the fact that nowdays, graphene and their derivates can be obtained in laboratory and they can serve as a non trivial architecture to compare theoretical and experimental findings.Continuous-time quantum walks have been studied on a great number of network structures81 the problem of localization on honeycomb structures was studied by using the DTQW model, while the same model was implemented to graphene related structures to determine the accumulated arrival probability82. The problem of quantum search on graphene lattice by making use of a CTQW model was addressed in Refs.84. A discrete-time staggered quantum walk model was used to compute the mean-square displacement on hexagonal lattices and to solve the spatial search problem85. The article86 focuses on excitation source-to-sink transport for percolated coined quantum walks on nanotubes. It was shown in Ref.87 that for the line, square and honeycomb lattice topologies there is a unitary equivalence of coined and scattering discrete time quantum walk models. The scattering construction of discrete time quantum walks on honeycomb lattices was studied in detail88, obtaining ten independent models. By contrast to all these works, we monitor in detail the evolution of quantum efficiency on honeycomb networks in the CTQW framework, monitoring its dependence on the topology of the structures. We find that the transport is strongly influenced by the underlying topology of the network.Other theoretical works were devoted to quantum walks on honeycomb or graphene-based structures. In Ref.58, shows strong localization effects. The situation is different if one considers the CTQWs on linear chains, for which we encounter a very good spreading over the network57. Honeycomb networks like graphene and its related structures display a mixture of linear and starlike segments. For the considered networks the number of branches is very small, namely three for all nodes of fullerenes, two or three for graphene sheets or nanotubes, and ranging between two and five for graphite. Thus, the number of branches is not as low as in rings, for which we have only two, or as high as in scale-free networks59, for which one can consider highly connected nodes. All our networks are composed of connected rings of size six, i.e. hexagons. Additionally, we also have some pentagons for fullerenes and plenty of squares for graphite networks. Note that this variety of topologies cannot be captured in terms of the topological directional functions88. We will monitor how the quantum transport efficiency changes by stacking one graphene sheet on top of each other, similar to the procedure developed in Ref.60, or by rolling-up cylinders of graphene forming nanotubes.It is known that in our model the quantum transport on networks with many branches, such as Cayley trees or starsThe paper is organized as follows: In “69, related to the dimensionality of its different possible topologies. From this large variety of structures we choose to analyse the most symbolic ones: fullerenes, graphene, graphite, and nanotubes. Fullerenes, known also as buckyballs resemble a hollow sphere89. From the physical point of view, they are considered finite, zero-dimensional objects with discrete energy states. Since its discovery in 1985 many theoretical as well as experimental studies were devoted to 91 or encountered in the universe93. 90: N is the number of nodes, F is the number of faces and E gives the number of links (edges). From this formula any fullerene has exactly 12 pentagons and Honeycomb structures are ubiquitous as carbon is one of the most present chemical elements on earth. Structures based on graphene sheets gained more importance in the last decades with the discovery of its multifaceted properties69. Since then, many experimental and theoretical studies continue to uncover its peculiar properties105, to cite only a few. In this article we use honeycomb networks mimicking a sheet of graphene defined in terms of the number of hexagons along two directions, y-axis by moving along the x-axis, then we move to the second line along the y-axis and so on until the last node of the network. We choose this method of numbering for reasons which will become clear in the “x-axis and y-axis are given byx-axis equals Graphene is a two-dimensional allotrope of carbon and it is viewed as a single layer of atoms arranged as a honeycomb network made up of hexagons. Although it is widely considered as the cornerstone of all known allotropes of carbon, a single graphene sheet was only recently isolated106 and the layers can easily move past each other due to weak forces between adjacent layers. By using Raman spectroscopy it was proven that changes in its electronic structure allows to identify the graphene layers107. Two adjacent layers are usually arranged in such a way that the atoms in one of the two sublattices of the honeycomb structure of one layer are situated above one half of the atoms in the neighboring layer95. In this article we construct our multilayer honeycomb network as a sequence of identical copies of the initial layer. We also consider that all the nodes from a layer are connected to a single node from the neighboring layers, thus, each node from a layer is situated directly above a node from their adjacent layers. Such networks appear also in graphite intercalated compounds109 or they can be obtained by layer-by-layer assembling techniques110. In Fig. x- and y-axis, respectively, and we denote by L the number of identical layers. First, we put the numbers on nodes from the first layer and then we move to the second layer and so on. The numbers of the nodes of each layer are given according to the procedure described above for a single sheet graphene network. The total number of nodes and links of a graphite-type honeycomb network are therefore given byLayers of graphene can be piled on top of each other creating graphite. This is the three-dimensional allotrope of carbon and it is one of the most stable form of carbon under standard conditions. It possesses properties of both metals and non-metals, having high thermal and electrical conductivity95. Carbon nanotubes have a large surface area and high thermal and electrical conductivity111. In the literature, two classes of nanotubes are distinguished: single-wall carbon nanotubes (single layer of graphene), and multiwall carbon nanotubes . Here, we consider the first class, for which there are three ways of wrapping a honeycomb network to a cylindrical honeycomb tube, so-called armchair, chiral, and zigzag type112. Here, we construct our nanotube from the graphene by cutting the last bonds from each line and reconnecting it to the first node of the line, corresponding to the zigzag type112. Due to this prescription, which considers lines instead of hexagons, we choose to change our parameters to Carbon nanotubes can be obtained by rolling a single layer of graphene along a certain rotational axis and reconnecting the open bonds. The nanotubes are composed only of hexagons and thus, from the physical point of view they can be considered one-dimensional structuresN nodes, which are coupled to other nodes by links. In this formalism we associate to each node a state j.In this article we study quantum transport on honeycomb networks by making use of the CTQW model. For a better understanding of transport properties we study also the corresponding classical model, the CTRW. Here, we consider a network to be a set of connectivity matrixj and k, and 0 otherwise. Its diagonal elements equal the number of links emerging from node j, namely 57 that we encounter only positive real eigenvalues The dynamics on any network for both a CTRW and a CTQW depends only on the connections between the nodes. All transport properties are determined from the complete set of eigenvalues and eigenvectors of the so-called 1 for the transition probability k to node j in time t ,l and j and the initial conditions are For a classical CTRW we start from a Markov process master equationquantum transport we assume a set of orthonormal and complete states 57. The dynamics is determined from the quantum mechanical Hamiltonian, such that the Schrödinger equation for the transition amplitudes k to node j at time t is given by 50: k is averaged over all nodes. For CTRWs we setN. In the quantum CTQW case, however, 57:61 that the long time average transition probability depends only on the eigenvalue density 62:The transition probability of a CTRW can be written as a function of the eigenvalues 61. For a star with 63.Quantum transport is considered to have maximum efficiency for into Eq. , one fin113:m, n) and  are two distinct links of node m and deg(m) and deg(n) being the degree of m and n, respectively, while m.We focus on quantities that depend on the eigenvalues square graphene sheets. Finally, we study carbon nanotube-types of networks obtained from rolling up a graphene sheet, exploring how the width and length of the sheet influences the quantum transport.We now show our results of continuous-time quantum walks on honeycomb networks. First we focus on the fullerene-types, chosing 57 from the equation n being an integer and In Fig. 57, namely 1/2. It is important to stress that networks whose density of states follows a power-law, 57: 115. In the quantum case the envelope of the lower bound for CTQW scales as 57. The same scaling was obtained for the decay of temporal correlations in quantum systems with Cantor spectra116, where the quantum average probability 57, In Fig. n by Eq. , and its(t), Eq. . Here, w57. It is important to stress that node 55 is the most distant node from node 2, i.e. its diametrically opposite node82, with the shortest path containing 9 links, depicted by a blue line in Fig. 57. For circles with an even number of nodes one finds two maximum probabilities: to the starting point and to its opposite node, while for odd-numbered circles we encounter only the first situation. This is due to the symmetry of the network, more exact to the number of steps to go in both directions. In the case of networks of 117.In Fig. mentclass2pt{minimmentclass2pt{minimmentclass2pt{minimt we found that for In Fig. entclass1pt{minimak and ends at node j after time t. We display the results for the three graphene-type networks shown in Fig. x-axis, y-axis, x-direction, connected to each other through x-axis, thus we have set the parameters to square graphene sheet with the same amount of hexagons on both directions, In Fig. t}π, Eq. , of a CTmentclass2pt{minimmentclass2pt{minimmentclass2pt{minim H_y)=8, $$\end{dok after time t, hitting time118. For the first graphene network we found that square graphene-type network. Thus, generalizing these data we encounter the time needed to reach the last node from the first line to be N nodes119, namely N nodes, probabilities which are 10 times higher than the equipartition value 1/N, like our networks, can be considered a proof of some localization effects. For some network structures one might find full revivals at certain times57 or incomplete revivals63. The full revival times 119. Thus, sometimes we focus on computing some incomplete revival times, for which square graphene we found In the first row of Fig. mentclass2pt{minimBy contrast to these findings, in the second row of Fig. j, k) at time square graphene, panel (i), the walkers have enough time to travel through the entire network, thus we encounter a better mixing of the interference waves. These observations are also well supported if one looks at the highest probabilities, which for all three networks are between 0.1 and 0.2. In this interval we encounter 22 pairs of nodes for the first graphene-type network, 4 pairs for the second network, and 6 pairs for the last square graphene sheet network. The maximum value for our three networks also show where the localization effects are a little bit stronger: In the last row of Fig. square graphene). The number of nodes for these networks is not the same, ranging between 5002 and 6010, and it depends on the chosen parameters according to Eq.  and 3 (inner), like our graphene networks. Differently than dendrimers, the graphene networks have also the circular segments (from the hexagons). The same exponent is also encountered for a square lattice with a similar number of nodes, as shown by a dashed line in the panel. Thus, for networks with longer linear segments the above mentioned behavior is followed by another power-law behavior, 57. For graphene with 60, but for the graphene networks 57, and for longer times we notice that the fluctuations occur around the long-time average 90, the difference between the number of links and nodes keeps the same: 121, which is the total number of links divided by the total number of nodes and it gives the connectivity of a network. For our networks this ratio is equal to 1.41, 1.45, 1.47,  and 1.48, respectively. The lowest In Fig. (t), Eq. , and \do(t), Eq. , as a fumentclass2pt{minimy direction we have an armchair pattern. By comparing two pairs (i)square graphene 120.In Fig. t}χ, Eq. , as a fumentclass2pt{minimsquare graphene sheets, with In Fig. L. In order to facilitate the understanding of the results and to compare with simpler graphene sheet networks studied earlier, we continue to use the same numbering method implemented for graphene-type networks, for more details see Fig. In Fig. t}π, Eq. , for thementclass2pt{minimmentclass2pt{minim57 is obvious. The amplitude waves travel along the network until they bounce off the nodes from the last layer and interfere at longer times. The same process occurs with other chosen networks, but the interference patterns are showing up earlier, because we now have a smaller number of layers, i.e. shorter lines. An interesting situation is encountered for more compact networks, namely our graphite-type networks with only 121: 1.98, which suggest a higher connectivity. This high value is mainly due to links between layers, since its Link-Node ratio of a single layer is the second lowest from the five networks. Thus, the fast propagation along the network is due to spreading on one layer, while the high number of links between layers is responsible for localization. The most compact network, In the first row of Fig. mentclass2pt{minimIn the second row of Fig. mentclass2pt{minimsquare. We have probabilities higher than 0.1 only for 18 pairs of nodes =7, 7, 0, 5040$$63, more exact a power-law decay with the exponent square graphene with an odd number of hexagons, the approximate value In Fig. (t), Eq. , and \do(t), Eq. . We consx or y axes. All the curves follow a power-law decay with exponent 60, for which the power-law exponent equals In Fig. squareL at the same time, depending on the most suitable option.In Fig. t}χ, Eq. , but we mentclass2pt{minimsquare graphene-type network with In Fig. x- and y-axis, respectively. In order to easier understand the results it is important to remember that x-axis and we present its results in the first column. In the second column, panels (b), (e), and (h), we display the results for a nanotube structure with square graphene sheet with In Fig. t}π, Eq. , for thrmentclass2pt{minimx,L_y,N)=4,2,68$$\mentclass2pt{minimIn the first row of Fig. mentclass2pt{minimIn the second row of Fig. mentclass2pt{minimy-axis, due to the block structure arrangement. For the first two nanotube networks, walkers that start from one line have higher transition probabilities to a node from the same line. For the last nanotube structure, the walkers have the highest probabilities to be at a different line, for example, nodes from the first line are coupled to nodes from the last line, nodes from the second line are coupled to nodes from the fourth line and the nodes from the third line are paired with nodes from the fifth line. We find probabilities larger than 0.1 only for the last two nanotube networks, namely 32 pairs of nodes for the second and 60 pairs for the third nanotube structure. The largest values for these networks also show us where we have stronger localization effects: square graphene with In the last row of Fig. square network corresponds to square networks, x-axis, y-axis. We observe that networks with oddevenoddevensquare graphene and they are due to the symmetry of the networks. For instance, nanotubes with even-numbered In Fig. mentclass2pt{minimIn Fig. (t), Eq. , and \do(t), Eq. . We consIn Fig. t}χ, Eq. , as a fuIn Fig. square networks. In this case, there are differences between an even or odd number of hexagons, a situation reminiscent of the square lattices into which we can map our graphene networks. We have shown that for square honeycomb networks with an odd number of hexagons along one direction the quantum efficiency is diminished when compared to adjacent parameter values. Due to the rolling up mechanism the decrease in efficiency occurs for nanotube composed of almost square graphene networks. A similar decrease in efficiency, but not so evident, has been encountered also for graphene or nanotube structures with the number of hexagons along one direction being a multiple of the hexagons along the other direction. For graphite this behavior is accentuated by the increase in the degeneracy of some eigenvalues, which is a direct consequence of stacking layers on top of each other. For graphene and graphite, but different from nanotubes, we have found that when placing the initial excitation in the center of the networks we get faster spreading, but also stronger localization effects. Thus, the transport along such networks depends on the initial condition. However, this dependence is not too strong and it can be overcome when the walkers start from a superposition of states, making these structures feasible also for applications, such as quantum search algorithms or quantum information. Strong localization properties were observed for discrete-time Grover-type quantum walks on honeycomb networks81. In this model, the localization disappears if the initial coin state is a superposition of all eigenstates. Using the same model, it was shown in Ref.86 that longer and thinner nanotubes exhibit better transport, which we also observed in our model and in the staggered quantum walk on the infinite hexagonal lattice85. For graphene based networks we observed a mixed situation regarding the validity of the approximation square graphene with an odd number of hexagons along one direction. We have shown that for graphite formed from square graphene sheets (In this work we have studied continuous-time quantum walks on honeycomb networks. We consider fullerene-type, graphene-type, graphite-type, and nanotube-type networks. We have investigated the average return probability and the long-time average transition probability 125, nuclear magnetic resonance126, trapped ions128, neutral atoms130, or superconducting qubits132.We expect our results to be of interest in various research areas, such as quantum transfer of excitons, quantum transport on graphene and related structures, quantum information or quantum search algorithms. Our findings can be exploited in experiments involving the walk of a single particle on tailored graphene-like networks. Experimental quantum walks of individual or multiple particles have been realized for various platforms, including photons


Flexible respiratory monitoring devices have become available for outside-hospital application scenarios attributable to their improved system wearability. However, the complex fabrication process of such flexible devices results in high prices, limiting their applications in real-life scenarios. This study proposes a flexible, low-cost, and easy-processing paper-based humidity sensor for sleep respiratory monitoring. A paper humidity sensing model was established and sensors under different design parameters were processed and tested, achieving high sensitivity of 5.45 kΩ/%RH and good repeatability with a matching rate of over 85.7%. Furthermore, the sensor patch with a dual-channel 3D structure was designed to distinguish between oral and nasal breathing from origin signals proved in the simulated breathing signal monitoring test. The sensor patch was applied in the sleep respiratory monitoring of a healthy volunteer and an obstruct sleep apnea patient, demonstrating its ability to distinguish between different respiratory patterns as well as various breathing modes. Respirag events ,11,12,13g events . Thus, dg events ,17,18,19As a flexible substrate material, paper has some characteristics of low cost, water absorptivity, and degradability, which brings new possibilities to the flexible sensing field. Therefore, it has been adopted recently to replace the traditional substrate material of flexible electronic devices ,21,22. FIn this article, we introduce a paper-based humidity sensor (PHS) based on the hygroscopic characteristics of paper for flexible respiratory monitoring. PHS has the specialties of flexibility, low price, easy processing, as well as the ability to distinguish between nasal and oral breathing signals by a designed dual-channel 3D structure. By establishing the paper humidity sensing model of paper material, the principle that the PHS can sense the humidity variations is elaborated, as The printing paper used to build the PHS was purchased from Double A, Thailand. Printing paper of three different grams per square meter (gsm) was chosen, including 70 gsm, 75 gsm, and 80 gsm. The graphite ink  was screen-printed to the printing paper through the designed interdigital electrode pattern. The PHS patch with the designed 3D structure was processed, as the detailed fabrication process is shown in In order to characterize the humidity sensing behavior of the paper-based sensor, we built an experimental calibration platform. The commercial temperature and moisture meter Testo 635-2, with a measuring range of 0 to 100%RH and accuracy of ±2%RH at 25 °C, was purchased from Testo Technology Company, Lenzkirch, Germany. It was used in the experiment to measure relative humidity and environment temperature within the experimental settings.To carry out the humidity calibrating experiment, the PHSs, as well as the standard humidity meter, were placed in a closed chamber. The humidifier was used to create unidirectional humidity changes within the cavity. Step humidity increase and decrease were set, and the corresponding resistance of PHS was measured when reaching equilibrium at different relative humidity conditions.The sensor response time was tested by placing the sensor into the humidity chambers with set humidity levels to evaluate the time needed for reaching equilibrium. PHS was transferred from low environmental relative humidity of 28%RH to high humidity environments . After the resistance readout stabilized in the high humidity level, PHS was moved back to the low humidity environment with the recovery time recorded.In the repeatability test, the standard humidity meter probe and PHS were placed adjacent in front of the periodically operating humidifier, which generated 30 times of humidity variation with a 4 min period in the environment by ejecting an equal amount of moisture into the sensor. The resistance of the sensor and environmental humidity were measured simultaneously.In the simulated breathing signal monitoring experiment, various respiratory patterns and breathing modes were produced artificially while signals were monitored by oral and nasal sensors simultaneously. In the sleep respiratory monitoring test, one healthy volunteer and one OSA patient were recruited. The designed 3D PHS patches as well as the flexible PCBs were used to detect signals during an over-two-hour sleep.An acquisition system shown in  was desiA preprocessing filter was performed on the acquired signal so as to get rid of the noise introduced by the circuit. The raw signal was filtered by a median filter with a window width of 15, and then a smooth filter with a window width of 9 before further signal processing.R and the thickness of the aqueous layer on the fiber t is shown as Equation (1)d and L are the interfinger gap width and interfinger gap length of the interdigital electrode, h is the thickness of the paper, r is the average radius of the paper fiber, fA is the fiber area fraction of the paper composite, and ρ is the resistivity of the water layer. For the sensor constructed with interdigital electrodes, the width and gap of the fingers are both 0.5 mm, forming a 7 mm × 7.8 mm sensing area. For the interdigital electrodes, the number of fingers changes the resistance of the sensor by varying the overall interfinger gap length L. According to Equation (1), both interfinger gap length L and paper thickness h change the resistance range of sensor responding to relative humidity, which further leads to variations in sensitivity. After weighing the sensitivity and device resistance range, appropriate L and h for measurement were determined. Based on Majumdar’s theory of water condensation on hydrophilic surfaces as well as Israelachvili’s study of the interactions between particles and surfaces, the relationship between relative humidity and the thickness of the water layer is shown in Equation (2)ψ is the relative humidity, λ is the length-scale parameter in hydration energy, and U is the free-energy parameter in hydration energy [α, β, δ, η, together with detailed formula derivations, can be found in the To explain the phenomenon that the resistance of paper-based sensors changes with relative humidity, a paper humidity sensing model is established. Paper, as a short fibers composite, consists mainly of cellulose and hemicellulose, which are both hydrophilic materials. When exposed to high relative humidity, water molecules tend to condense and form a water layer on the surfaces of the fibers. The salts left on the fibers after paper processing, such as sulfates, dissolve in water and come into ions, which conduct through directional movement in the solvent. When the ambient humidity decreases, the condensed water on the fibers evaporates and quickly diffuses into the atmosphere. We establish a paper humidity sensing model of the printing paper based on water condensation theory as well as interactions between particles and surfaces theory ,29. The n energy ,29. Spect increases with the increase of relative humidity ψ, as indicated by Equation (2). Based on Equation (1), an increase in t leads to a decrease in the resistance R of the paper-based sensor. In terms of the changing trend, the change rate of resistance to the relative humidity in the low humidity zone is much higher than that in the high humidity zone.According to our paper humidity sensing model, the thickness of the water film on the surface of fiber Sensitivity was characterized under the experimental setting detailed in the Methods section and the result of the relationship between relative humidity and resistance of PHS is shown in For respiration monitoring purposes, the high sensitivity to humidity enables the PHS to detect different respiration patterns with refined signals. The PHS with 80 gsm printing paper shows sufficient sensitivity and small hysteresis error within the targeted sensing range of ambient relative humidity to 100%RH to cover the reported exhale breath relative humidity of over ambient to 91%, for the application in respiratory sensing . MoreoveThe response and recovery characteristics of the 80 gsm sensors are as shown in M is proposed, which is defined as M = (PPV/PPV0)/(PPRH/PPRH0), where PPRH0 and PPV0 are the peak-to-peak values of standard humidity and voltage output from the sensor measurement circuit in the first cycle, and PPRH and PPV are that of other cycles. The matching rate M remains over 85.7% during the repeatability experiment, which shows good repeatability of PHS.The designed cyclic test to verify sensor repeatability was carried out by simulating the periodic moisture flow during breathing. As shown in To distinguish the oral and nasal breathing signals, a paper-based sensor patch was attached to the skin between the nasal septum and upper lip to collect oral and nasal breathing signals separately, forming a dual-channel PHS working mode, as shown in Firstly, three different breathing modes, including normal breathing, deep breathing, and rapid breathing, were simulated and tested. According to The ability to distinguish between oral and nasal breathing was then characterized, as shown in There are many different human breathing patterns during sleep, which vary significantly in breathing depth and respiratory rate. For people with sleep-related respiratory disorders, abnormal breathing, including apnea and excessively shallow breathing, may be present during their sleep. In order to verify the sensor’s response and monitoring capabilities to complex sleep breathing signals, sleep breath monitoring tests were conducted on a healthy volunteer and an OSA patient.A paper-based sensor patch was stuck on the skin, and other modules, including a flexible PCB and battery, were placed in the sleep mask to increase the wearability of the device and reduce body motion artifact during sleep. The sleep respiratory monitoring experiments were conducted over two hours with each participant. The result of the breathing signal is shown in The sleep respiratory monitoring experiments have proved that the wearable device using the designed dual-channel paper-based humidity sensor has the ability to distinguish between the mouth and nose breathing from the original signal and further differentiate various respiration patterns. Besides, the device can not only monitor the regular sleep breathing signals of a healthy volunteer but also detect the cluttered breathing signals of a patient with sleep-related respiratory disorders. Respiration of the healthy volunteer is mainly composed of regular oral breathing, nasal breathing, and mouth–nose coordinated breathing, while oral–nasal co-breathing accounts for a significantly higher proportion for the OSA patient. The device can be a helpful tool for breathing event detection, which can provide doctors with more information on the sleep-related respiratory disorders diagnosis and treatment process.In summary, a flexible, highly sensitive PHS with good stability has been developed for sleep respiratory monitoring. With low cost and simple processing, the paper-based sensor can be regarded as a disposable part of the flexible device, making it portable and hygienic for daily usage. The 80 gsm PHS shows high sensitivity (5.45 kΩ/%RH in low humidity level and 411 Ω/%RH in high humidity level) as well as good cyclic performance in the device characterization process. The designed dual-channel 3D structure enables the sensor to distinguish oral and nasal breathing signals directly from the original signal. Moreover, experiments on long-term respiratory monitoring during sleep have been carried out, in which we observed the characteristics of sleep respiratory signals in a healthy volunteer and an OSA patient, proving that the use of PHS in oral and nasal breathing monitoring can provide more information about how gas flows through different airways. The device may provide healthcare workers with more breathing information about patients with sleep-related respiratory disorders to assist in the diagnosis and treatment process.