BUSCADOR RECOLECTA

Data and code for reproducing the main results of the paper "Nanoporous graphene-based thin-film microelectrodes for in vivo high-resolution neural recording and stimulation"., Peer reviewed

Pre- and post-electrocatalysis characterization data (XRD, SEM, and TEM), electrochemical results, and operando Raman spectroscopy data., Peer reviewed

Synthesis of C1N1 Guanine (2.5 g, Sigma-Aldrich) was heat-treated in a high-temperature tubular furnace at 700 ºC with a heating rate of 1 ºC min-1 under an N2 flow of 150 mL min-1. Prior to heat treatment, the furnace was purged for 1 hour with the same N2 flow rate at room temperature. After heat treatment, C1N1 samples were obtained. Synthesis of NC@Fe3C materials NC@Fe3C materials were prepared by ball milling. C1N1 (450 mg) was mixed with FeCl3·6H2O (750 mg) for 30 min in a planetary mill equipped with an agate (50 mL) bowl and (10) balls (PM 100, Retsch) operating at a rotational speed of 500 rpm. The recovered paste was dried and subjected to heat treatment at 900 °C for 1 hour, with a heating rate of 5 ºC min- 1, under an N2 flow of 150 mL min-1. Temperatures ranging from 500 to 1000 ºC were also used to prepare materials for comparison, following the same synthesis procedure. Prior to pyrolysis, the furnace was purged with an N2 flow of 150 mL min-1 for 1 h. After cooling under the same N2 flow, the NC@Fe3C materials obtained were immersed in 1 M HCl in an ultrasonic bath for 30 min to remove any residual unreacted metal. The materials were then washed successively on a paper filter with 1 M HCl and distilled water. Finally, the materials were dried overnight in an oven set at 100 ºC. Physicochemical characterization Elemental analysis was used to determine C, N, H and O contents using a Vario EL Cube analyzer (Elementar). The materials (2 mg) were heat treated at 1700 ºC in a helium atmosphere containing oxygen. The combustion gases thus obtained were then separated by a chromatographic column and analyzed by a thermal conductivity detector. O was determined and not obtained by difference. X-ray photoelectron spectra (XPS) were obtained using an ESCAPlus OMICRON spectrometer equipped with a non-monochromatized Mg·Kα X-ray source. Shirley-type background and quantification were processed using CASA software. Peak deconvolution of Fe 2p, N 1s, C 1s and O 1s were performed by least-square fitting using Gaussian-Lorentzian (20:80) curves. Crystalline phases of NC@Fe3C-T materials were determined using a Bruker D8 Advance A25 polycrystalline powder X-ray diffractometer. Structural order at the nanoscale was studied for all NC@Fe3C-T materials by Raman spectroscopy using a Horiba XploRa Raman apparatus equipped with a 50 X long-range objective. The spectra are acquired between 500 and 3500 cm-1 with a circularly polarized laser of wavelength 638 nm, filtered at 10 % maximum energy to prevent sample heating, and using a holographic grating of 1200 lines per mm. The intensity ratio between D and G bands (ID/IG) was calculated based on the maximum intensity values between 1200 and 1450 cm-1 for the D band and 1450 and 1800 cm-1 for the G band. High-resolution transmission electron microscopy (HRTEM) and scanning transmission electron microscopy (STEM) investigations were performed in a JEOL JEM-ARM 200F Cold FEG, operating at 200 kV and equipped with a spherical aberration (Cs) probe and image correctors (0.12 nm point resolution in TEM mode and 0.078 nm in STEM mode). STEM images were obtained in high-angle annular dark-field (HAADF) mode. The chemical composition was studied by energy dispersive X-ray spectroscopy (EDX), collected on a JEOL spectrometer (SDD) in STEM mode. X-ray absorption spectroscopy (XAS) measurements were carried out at the Fe K-edges in transmission mode at the SAMBA beamline of the SOLEIL synchrotron (France). The beamline is equipped with a sagittally bent double Si (220) crystal monochromator and two Pd-coated mirrors used to remove X-ray harmonics. The catalysts were pelletized into 10 mm diameter disks using boron nitride as a binder. Data were processed using Athena software [1]. The textural properties of the samples were studied by N2 adsorption measurements performed at -196 °C on a Belsorp Max II manometric sorption analyzer Prior to the adsorption experiments, all samples were outgassed under secondary vacuum for 24 h at 110 ºC. The pore size distributions were obtained using the 2D non-local density functional theory (2D-NLDFT) with SAIEUS® software (Micromeritics), from which the textural properties were calculated, such as: total surface area, S2D-NLDFT, total pore volume, VT, mesopore volume, VMESO, and micropore volume (pore diameter less than 2.0 nm), VMIC [2]. Electrochemical characterization Electrochemical experiments were carried out with a rotating ring-disk electrode (RRDE) in a conventional three-electrode cell using an Autolab PGSTAT302 potentiostat. The rotating electrode is equipped with a 5 mm diameter carbon disk and a platinum ring that acts as a second working electrode. A reversible hydrogen electrode (RHE) immersed in the working electrolyte and a graphite rod were used as reference and counter electrodes, respectively. The working electrodes were prepared as follows: 0.5 mg of NC@Fe3C-T was suspended ultrasonically in 0.125 mL of an aqueous solution of 0.2 wt.% Nafion ® and 20 wt.% isopropanol [3]. 33 μL of the 4 mg mL-1 suspension thus obtained was drop-cast on the carbon disk electrode to a catalyst loading of 685 μg·cm-2. A commercial Pt/C electrocatalyst was also analyzed for comparison. Prior to ORR testing, the Pt/C electrocatalyst underwent electrochemical cycling from 0.0 to 1.25 V vs RHE to clean effectively the platinum surface of any carbon contaminants. The electrocatalytic activity towards the oxygen reduction reaction (ORR) was studied by linear sweep voltammetry (LSV) in O2-saturated 0.1 M KOH and 0.5 M H2SO4 solutions between 1.0 and 0.0 V vs RHE at 1600 rpm and a scan rate of 5 mV·s-1. The platinum ring potential was set at 1.5 V vs RHE to calculate the yield of hydrogen peroxide (H2O2) during the ORR measurements. Sample stability was studied by chronoamperometric tests. For this, the working electrode was held at 0.6 V vs RHE for 10,000 s at 1600 rpm, under continuous oxygen saturation. AEMFC experiments To produce the gas diffusion electrodes (GDEs) for the anode and cathode, inks were prepared by combining NC@Fe3C-900, ETFE ionomer powder[4], and isopropanol. The ink was prepared by manually grinding polytetrafluoroethylene (PTFE) with 200 mg of NC@Fe3C-900 and 1 mL of ultrapure water for 10 min using a mortar and pestle. Then, 1.5 mL of isopropanol was introduced into the mortar and the mixture homogenized for a further 5 min. The ink was subsequently sprayed onto a Toray TGP-H-60 gas diffusion layer (containing 5 wt.% PTFE) using an air-assisted sprayer (Iwata) to prepare the GDEs. The anode electrodes were prepared using a similar method to that described previously [5], with 60 wt.% PtRu/C catalysts. The anode electrode, cathode electrode and anion-exchange membrane were hydrated with ultrapure water for 20 min and then soaked three times in 1.0 M KOH solution to exchange the polymer from bromide to hydroxide form. The AEM consisted of 20 μm-thick poly(norbornene)-based tetrablock copolymer membrane with an ion-exchange capacity of 3.88 meq·g-1 [6–8]. Membranes and GDEs were assembled immediately after functionalization in a Scribner cell with 5 cm2 active area featuring single-channel serpentine flow fields. Teflon gaskets 152 μm and 203 μm thick were used at the anode and cathode electrodes, respectively, to maintain a compression of approximately 25%. The Scribner 850e fuel cell test station was used to control the AEMFC. The relative humidity (RH) of both cathode- and anode-reacting gases were adjusted to optimize cell performance at an operating temperature of 60 ºC. The gases used in this study comprised ultra-high purity H2 and O2 from Airgas. Structural Models To comprehensively compare and assess the influence of Fe3C on catalytic performance, five distinct model systems were designed and tested. Initially, a 1 nm diameter carbon nanotube, saturated with hydrogens atoms at the edges, was modeled to represent the base carbon layer and referred to as C in the manuscript. To investigate the impact of nitrogen doping, an Ndoped carbon layer, called NC, was created by partially substituting C atoms with N atoms. Subsequently, a Fe3C nanoparticle was modeled and incorporated into both the C and NC systems, resulting in two additional models called C@Fe3C and NC@Fe3C, respectively. To explore the effect of C layer thickness on the catalytic performance of Fe3C, a double C-layer was also constructed. The two C-layers were separated by a distance of 0.37 nm, reflecting the experimental results and the model material was denoted as 2NC@Fe3C in the manuscript. Each system was meticulously designed to isolate and evaluate the specific factors contributing to the overall catalytic behavior studied. DFT computational details TGCC supercomputing facility, a high-performance scientific computing resource at CEA, was used for all calculations. Each calculation involved the use of 128 cores, and the optimization of each configuration took over a month of continuous processing. The electrocatalytic properties of NC@Fe3C materials were investigated with periodic density functional theory (DFT) calculations[9,10], using the Vienna ab initio simulation package (VASP)[11,12]. The Perdew-Burke-Ernzerhof (PBE) functional, a widely accepted choice within the Generalized Gradient Approximation (GGA) framework, was employed to describe the exchange–correlation interaction[13]. Structural relaxation of all studied configurations was achieved using the conjugate gradient method, with a plane wave cut-off energy set at 600 eV. The Kohn-Sham self-consistent energy was iterated until convergence, with a criterion of 10⁻⁶ eV for forces and 0.2 eV nm-1 per atom. Brillouin zone sampling used a Gamma-centered mesh at the gamma point. In order to accurately model the adsorption of various gas molecules, the Grimme dispersion correction (DFT-D3(BJ)) scheme was incorporated to effectively capture van der Waals forces [14,15]. Spin-polarized calculations were conducted for all scenarios, and a Hubbard-like U parameter (4.0) was introduced to address strong correlation effects at the Fe3C sites, as determined by Wang et al. [16]. In order to reveal the interaction between ORR intermediates gas molecules and NC@Fe3C, the adsorption energy was computed at 0 K using the formula: ΔEads = E(NC@Fe3C+guest) – (ENC@Fe3C + Eguest) Eq.1 where E(CN@Fe3C+guest) represents the total energy of the NC@Fe3C model material with a single gas molecule adsorbed, while ECN@Fe3C and Eguest are the total energies of the NC@Fe3C and the isolated gas molecule, respectively. To assess the impact of the Fe3C core on charge transfer to molecules adsorbed on the C-layer, the Bader charge difference (ΔQ) was determined using Bader’s approach [17]. This method partitions space based on the topological properties of charge density, defining boundaries as surfaces where the charge density gradient has zero flux. The electronic charge difference (ΔQ) is then calculated by: ΔQ = Q(NC@Fe3C+guest) – (QNC@Fe3C + Qguest) Eq.2 Similarly, here Q(NC@Fe3C+guest) represents the Bader charge of all interacting atoms, while QNC@Fe3C and Qguest are the Bader charges of the NC@Fe3C system and the isolated molecule in the gas phase, respectively. Following the same methodology, the variations in electron density (Δρ) induced by the evolution of the chemical system were calculated and then visualized using VESTA software. Calculation methods for evaluating ORR activity Methods for assessing ORR activity were used to elucidate the reaction mechanism of selected materials under specific conditions. At a pressure of 1 bar and a temperature of 298.15 K, it is proposed that the ORR proceeds through four proton-coupled electron transfer (PCET) steps: * + O2 + (H+ + e-) → OOH*, (S1) OOH* + (H+ + e-) → O* + H2O, (S2) O* + (H+ + e-) → OH*, (S3) OH* + (H+ + e-) → * + H2O, (S4) Here, the asterisk (*) denotes subsequent intermediate species absorbed on the active sites of the catalysts. To evaluate the differences in free energy associated with these four PCET steps, the computational hydrogen electrode (CHE) method, developed by Nørskov et al.[18] was used. The free energies of the ORR reactions (i.e., (S1) to (S4)) are given by: ΔGOOH* = G(OOH*) − G(H+) − 𝜇(e-) − G(O2) − G(NC@Fe3C) (S5) ΔGO* = G(O*) + G(H2O) −2 G(H+) − 2 𝜇(e-) − G(O2) − G(NC@Fe3C) (S6) ΔGOH* = G(OH*) − 3 G(H+) − 3 𝜇(e-) − G(O2) − G(NC@Fe3C) + G(H2O) (S7) The expressions for Gibbs free energy (G) and chemical potential (μ) are given by: G = EDFT + ZPE – TS (S8) G(H+) − 𝜇(e-) = ½ G(H2) (S9) where EDFT is the density functional theory energy, ZPE is the zero-point energy, T is the temperature, and S is the entropy. These equations enable us to calculate the free energy differences for each step of the ORR process, providing valuable insights into the reaction mechanism., Materials and methods: Synthesis of C1N1.-- Synthesis of NC@Fe3C materials.-- Physicochemical characterization.-- Electrochemical characterization.-- AEMFC experiments.-- Structural Models.-- DFT computational details.-- Calculation methods for evaluating ORR activity., The authors have received funding from the French PIA project “Lorraine Université d'Excellence”, reference ANR-15-IDEX-04-LUE, and from the ERDF-funded TALiSMAN and TALiSMAN2 projects. SGD thanks the Ministerio de Universidades, the European Union, and the University of Oviedo for their financial support (MU-21-UP2021-030 30 267 158)., Peer reviewed

Characterization of the MBE growth, XRD spectrum, RHEED patterns, cross-sectional transmission electron microscopy image and chemical analysis, planar Hall effect measurements, methodology to disentangle SOT from the anomalous Nernst effect (in-plane and out-of-plane thermal gradients are considered), additional transport, SOT, and switching data on several BST/FGT(7) and BST/FGT(2) devices, and additional magnetic characterizations of BST/FGT(2)., Peer reviewed

Epitopes are specific regions on an antigen’s surface that the immune system recognizes. Epitopes are usually protein regions on foreign immune-stimulating entities such as viruses and bacteria, and in some cases, endogenous proteins may act as antigens. Identifying epitopes is crucial for accelerating the development of vaccines and immunotherapies. However, mapping epitopes in pathogen proteomes is challenging using conventional methods. Screening artificial neoepitope libraries against antibodies can overcome this issue. Here, we applied conventional sequence analysis and methods inspired in natural language processing to reveal specific sequence patterns in the linear epitopes deposited in the Immune Epitope Database (www.iedb.org) that can serve as building blocks for the design of universal epitope libraries. Our results reveal that amino acid frequency in annotated linear epitopes differs from that in the human proteome. Aromatic residues are overrepresented, while the presence of cysteines is practically null in epitopes. Byte pair encoding tokenization shows high frequencies of tryptophan in tokens of 5, 6, and 7 amino acids, corroborating the findings of the conventional sequence analysis. These results can be applied to reduce the diversity of linear epitope libraries by orders of magnitude., Peer reviewed

Figure S1. Amino acid global propensity in epitopes. Figure S2. Overall global propensity variation in epitopes. Figure S3. Relative entropy values for the epitopes. Figure S4. Distribution of epitopes by source organism. Figure S5. Tokenization of epitopes with tokens of 4 and 5 residues. Figure S6. Amino acid frequency after tokenization. Table S1: Average relative entropies and p-values for the entropy analysis. Table S2: Tokens obtained through BPE tokenization of the entire dataset., Epitopes are specific regions on an antigen’s surface that the immune system recognizes. Epitopes are usually protein regions on foreign immune-stimulating entities such as viruses and bacteria, and in some cases, endogenous proteins may act as antigens. Identifying epitopes is crucial for accelerating the development of vaccines and immunotherapies. However, mapping epitopes in pathogen proteomes is challenging using conventional methods. Screening artificial neoepitope libraries against antibodies can overcome this issue. Here, we applied conventional sequence analysis and methods inspired in natural language processing to reveal specific sequence patterns in the linear epitopes deposited in the Immune Epitope Database (www.iedb.org) that can serve as building blocks for the design of universal epitope libraries. Our results reveal that amino acid frequency in annotated linear epitopes differs from that in the human proteome. Aromatic residues are overrepresented, while the presence of cysteines is practically null in epitopes. Byte pair encoding tokenization shows high frequencies of tryptophan in tokens of 5, 6, and 7 amino acids, corroborating the findings of the conventional sequence analysis. These results can be applied to reduce the diversity of linear epitope libraries by orders of magnitude., Peer reviewed

Epitopes are specific regions on an antigen’s surface that the immune system recognizes. Epitopes are usually protein regions on foreign immune-stimulating entities such as viruses and bacteria, and in some cases, endogenous proteins may act as antigens. Identifying epitopes is crucial for accelerating the development of vaccines and immunotherapies. However, mapping epitopes in pathogen proteomes is challenging using conventional methods. Screening artificial neoepitope libraries against antibodies can overcome this issue. Here, we applied conventional sequence analysis and methods inspired in natural language processing to reveal specific sequence patterns in the linear epitopes deposited in the Immune Epitope Database (www.iedb.org) that can serve as building blocks for the design of universal epitope libraries. Our results reveal that amino acid frequency in annotated linear epitopes differs from that in the human proteome. Aromatic residues are overrepresented, while the presence of cysteines is practically null in epitopes. Byte pair encoding tokenization shows high frequencies of tryptophan in tokens of 5, 6, and 7 amino acids, corroborating the findings of the conventional sequence analysis. These results can be applied to reduce the diversity of linear epitope libraries by orders of magnitude., Peer reviewed

This PDF file includes: Supplementary Text and Figs. S1 to S11., Peer reviewed

Appendix A: Fig. A1. Example spread of the traverse and descent, as well as a 2D plot with elevation contours, a 3D plot using 2 m/pixel NAC images and 5 m/pixel LOLA DEM, and a histogram of the distance traveled as a function of the slope. Fig. A2. Example of the tables associated with each traverse with general values calculated, and the 2 m/pixel NAC image and a detailed 2D map of the host crater. Fig. A3. Example of the tables associated with each PSR optimal descent with general values calculated, and a detailed 2D and 3D image of the host crater., Appendix B. Supplementary data: Multimedia component 1. Multimedia component 2. Multimedia component 3., Peer reviewed

In this work, a sequential covalent co-immobilization of graphene oxide (GO) and hyaluronic acid (HA) is performed to obtain a biocompatible wear-resistant nanocoating on the surface of the biomedical grade Cobalt-Chrome (CoCr) alloy. Nanocoated CoCr surfaces were characterized by Raman spectroscopy and electrochemical impedance spectroscopy (EIS) in 3 g/L HA electrolyte. Tribocorrosion tests of the nanocoated CoCr surfaces were carried out in a pin on flat tribometer. The biological response of covalently HA/GO biofunctionalized CoCr surfaces with and without wear-corrosion processes was studied through the analysis of the proteome of macrophages. Raman spectra revealed characteristic bands of GO and HA on the functionalized CoCr surfaces. The electrochemical response by EIS showed a stable and protective behavior over 23 days in the simulated biological environment. HA/GO covalently immobilized on CoCr alloy is able to protect the surface and reduce the wear volume released under tribocorrosion tests. Unsupervised classification analysis of the macrophage proteome via Hierarchical clustering and Principal Component Analysis (PCA) revealed that the covalent functionalization on CoCr enhances the macrophage biocompatibility in vitro. On the other hand, disruption of the HA/GO nanocoating by tribocorrosion processes induced a macrophage proteome which was differently clustered and was distantly located in the PCA space. In addition, tribocorrosion induced an increase in the percentage of upregulated and downregulated proteins in the macrophage proteome, revealing that disruption of the covalent nanocoating impacts the macrophage proteome. Although macrophage inflammation induced by tribocorrosion of HA/GO/CoCr surfaces is observed, it is ameliorated by the covalently grafting of HA, which provides immunomodulation by eliciting downregulations in characteristic pro-inflammatory signaling involved in inflammation and aseptic loosening of CoCr joint arthroplasties. Covalent HA/GO nanocoating on CoCr provides potential applications for in vivo joint prostheses led a reduced metal-induced inflammation and degradation by wear-corrosion in vivo., Proteome Dataset: Macrophage Proteomic Analysis of Covalent Immobilization of Hyaluronic Acid and Graphene Oxide on CoCr Alloy in a Tribocorrosive Environment, Peer reviewed