Boron-doped Graphene Research Articles

Unlocking the Nitrogen Reduction Electrocatalyst with a Dual-Metal-Boron System: From High-Throughput Screening to Machine Learning.

Recently, dual-metal catalysts have attracted much attention due to their abundant active sites and tunable chemical properties. On the other hand, metal borides have been widely applied in splitting the inert chemical bonds in small molecules (such as N2) because of their excellent catalytic performances. As a combination of the above two systems, in this work, 11 kinds of transition metal atoms (TM = Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Mo, and W) were selected to embed in boron-doped graphene (BG) to construct 66 dual-metal-boron systems, and their performances toward the N2 reduction reaction (NRR) were examined using first-principles simulations. Our results revealed that such a dual-TM@BG system exhibits excellent thermodynamic and electrochemical stabilities, which facilitate the experimental synthesis. In particular, Fe-Fe- and Fe-Co-doped BG exhibit excellent performance for NRR, with the limiting potentials of -0.29 and -0.32 V, respectively, and both of them exhibit inhibitory effects on the H2 evolution reaction. Remarkably, the microkinetic modeling analysis revealed that the turnover frequency for the NH3 production on FeFe@BG reaches up to 7.27 × 108 s-1 site-1 at 700 K and 100 bar, which further confirms its ultrafast reaction rate. In addition, the machine learning method was employed to further understand the catalytic mechanism, and it is found that the NRR performances of dual-TM@BG catalysts are closely related to the sum of radii of two TM atoms. Therefore, our work not only proposed two promising electrocatalysts for NRR but also verified the feasibility for the application of a dual-metal-boron system in NRR.

Electron-Spin Relaxation in Boron-Doped Graphene Nanoribbons.

Boron-doped graphene nanoribbons are promising platforms for developing organic materials with magnetic properties. Boron dopants can be used to create localized magnetic states in nanoribbons with tunable interactions. Controlling the coherence times of these magnetic states is the very first step in designing materials for quantum computation or information storage. In this work, we address the connection between the relaxation time and the position of the dopants for a series of boron-doped graphene nanofragments. We combine Redfield theory and ab initio calculations of magnetic properties to unveil the mechanism that governs spin relaxation in solution. We demonstrate that relaxation times can be in the order of 1 ms for the selected graphene nanofragments. A detailed analysis of the relaxation mechanism reveals that the spin decoherence is fundamentally driven by fluctuations of the spin-orbit coupling, and the hyperfine interaction facilitated by the thermal motion of the graphene nanofragments. The close connection between relaxation time, hyperfine interaction and the spin-orbit coupling offers the perspective of designing attractive materials with long-lived spin states.

Pt Nanoparticles Electrochemically Deposited onto Heteroatom-Doped Graphene Supports as Electrocatalysts for ORR in Acid Media

Platinum nanoparticles (PtNPs) are attached to different single heteroatom-doped (N, S, P, and B) and dual heteroatom-doped (N, B and N, P) graphene nanosheets via electrochemical deposition using the chronoamperometric method, which allowed for strong attachment of the PtNPs onto the support surface. The effect of the support material on the electrocatalytic activity of the PtNPs on the oxygen reduction reaction (ORR) in acidic media is examined. The PtNPs supported on boron-doped graphene exhibit the highest specific activity (1.26 mA cm−2), and the PtNPs supported on nitrogen and boron dual heteroatom-doped graphene exhibit the highest mass activity (0.70 A mg−1) at 0.9 V vs reversible hydrogen electrode. The kinetics of the ORR vary significantly depending on the dopants, thus concluding that the heteroatom doping of the graphene support material affects the electrocatalytic activity of PtNPs toward the ORR.

Adsorption and diffusion behavior of lithium atom on boron-doped armchair graphene nanoribbon- A dispersion-corrected DFT study

In this paper, we study the relevance of considering dispersion interactions in DFT calculations to determine the adsorption behavior of lithium atom(s) on boron-doped armchair graphene nanoribbons. Our findings suggest that substitutional doping with boron atoms makes the AGNR electron-deficient which results in charge transfer from Li to the AGNR. Li atom binds more strongly to B-doped AGNR than pristine AGNR. A stronger Li-C interaction prevents the clustering of Li atoms, thus, inhibiting dendrite growth. The maximum storage capacity of B-doped AGNR reaches to 1261 mAh/g. B-doped AGNR offers a lower diffusion barrier of 0.19 eV to Li atom as compared to pristine AGNR. From the analysis of density of states, it is observed that the semiconducting nature of both pristine and B-doped AGNRs turns into metallic after the adsorption of lithium atom. Our results show that B-doped AGNR can be used as a potential anode material of lithium-ion batteries.

Construction of electron-interactive CoMoO4 @ CoP core–shell structure on boron-doped graphene aerogel as strongly interface coupled hybrid electrodes for high flexible supercapacitor

Exploring high energy density, lightweight and self-supporting flexible electrodes is essentially significant to flexible energy storage equipment. Herein, boron-doped three-dimensional porous graphene aerogel (BGA) is engineered and novel flake-like CoP encapsulating flower-like CoMoO4 core–shell structure is arranged on it (CoMoO4@CoP/BGA) utilizing a combination of solvothermal, freeze-drying and vapor deposition techniques. Boron-doped graphene aerogel as flexible self-supporting positrode creates a unique three-dimensional porous interface with a larger specific surface area, which is conducive to exposing more active sites and avoids the additional process of adding binders and conductive agents. The heterointerface engineering of CoP epitaxial growth on CoMoO4 can efficiently enhance electrolyte ions adsorption ability and fast reaction kinetics. As expected, the fabricated CoMoO4@CoP/BGA demonstrates a better specific capacitance of 3056.4F/g than that of CoMoO4/BGA (1582.4F/g) and pure CoMoO4 (669.2F/g), apart from retains the remarkable cyclic stability of 88.4 % after 10,000 cycles. Furthermore, a hybrid supercapacitor composed of CoMoO4@CoP/BGA and BGA can provide a high energy density of 50.2 Wh kg−1 at 800.0 W kg−1, and retains good capacitance retention of 95.6 % after 10,000 cycles, which can be attributed to the large specific surface of B doping 3D porous graphene aerogel and the rich strong coupling interface synergy between CoP and CoMoO4. More importantly, this work provides important guidance for the design of heterojunction electrodes based on heteroatom doped graphene aerogel and phosphides @ oxide based flexible energy storage devices.

Data-driven deep learning prediction of boron-doped graphene work function

The atomic-scale doping plays a crucial role in determining the work function of doped graphene. However, it is a challenge to comprehensively investigate its work function due to the large range of possible molecular configurations. In this study, more than 30,000 pieces of data of the work functions from boron-doped graphene with different doping concentrations and positions is calculated via DFT, and the dataset is further expanded to 115,000 based on the molecular structural symmetry. Then, a simple structural descriptor-based method is developed to characterize the surface of the doped graphene. Finally, a deep learning model is proposed to predict the work function. The results show that the proposed model can accurately predict the work function with the R2 > 0.95 and MSE < 0.0013 for a 3×3 supercell. In addition, this study also provides the possibility to quickly investigate the work function of other heteroatom 2D materials.

Construction of advanced Nb9VO25 electrode material by introducing graphene quantum dot for high energy supercapacitors with exceptionally high diffusive capacitance

Unreasonable tunnel structure and low intrinsic conductivity limit practical applications of niobium oxide in high-performance supercapacitors. The study reports the construction of Nb9VO25 electrode material via coordination of Nb(V) and V(V) with histidine and serine-functionalized and boron-doped graphene quantum dot (HSBGQD) and subsequent annealing. The introduction of HSBGQD and rambutan peel leads to formation of small Nb9VO25 nanocrystal and low valent Nb and V species. The combination of small size and more reasonable tunnel structure accelerates the ion diffusion. The Nb(IV) and V(IV) double doping optimizes the tunnel structure, narrows the bandgap and creates new pathways for high-speed electron transfer. The integration of defect engineering with graphene surface modification enhance the intrinsic conductivity. The Nb9VO25 electrode shows exceptionally high specific capacitance of 2925.3 F/g, which is more than 142 times that of Nb2O5. The symmetrical supercapacitor with Nb9VO25 electrodes and PVA/Li2SO4 gel electrolyte offers high specific capacitance (263 F/g at 1 A/g), high-rage capacity (138 F/g at 50 A/g), cycling stability (capacitance retention of 99.4 % after 10000-cycle), energy density (146 W h Kg−1 at 996 W Kg−1 and 77 W h Kg−1 at 50181 W kg−1) and broad application prospect in wearable electronic devices.

Improvement of boron-doped graphene material as an anode candidate for sustainable energy storage based lithium batteries

Improvement of boron-doped graphene material as an anode candidate for sustainable energy storage based lithium batteries

An ultrasensitive electrochemical sensor based on boron-doped porous graphene for efficient detection of nitrite in food

Boron (B) doping plays a critical role in adjusting the electronic properties of carbon materials. In this work, B-doped porous graphene (B-PG) was successfully prepared by a simple strategy and used as an advanced electrode material for the detection of nitrite (NO-2) in food. The scanning electron microscopy (SEM), high resolution transmission electron microscopy (HRTEM), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) were employed to characterize this B-PG. Due to the ideal doping of boron atoms, the material has good conductivity and exhibits excellent electrochemical catalytic performance. NO-2 was used as the representative analytes to demonstrate the sensing performance of B-PG. It is found that the B-PG modified GCE exhibited a linear response over the concentration range from 3 μM – 3 mM and 3 mM – 15 mM, with a detection limit of 1.1 μM. The designed electrochemical sensor has good analytical performance, such as high sensitivity, good reproducibility and acceptable accuracy, and can be successfully applied to the accurate and precise detection of residual NO-2 in food.

Work Function Prediction by Graph Neural Networks for Configurationally Hybridized Boron-Doped Graphene.

Graphene, serving as electrodes, is widely applied in electronic and optoelectronic devices. Work function as one of the fundamental intrinsic characteristics of graphene directly affects the interfacial properties of the electrodes, thereby affecting the performance of the devices. Much work has been done to regulate the work function of graphene to expand its application fields, and doping has been demonstrated as an effective method. However, the numerous types of doped graphene make the investigation of its work function time-consuming and labor-intensive. In order to quickly obtain the relationship between the structure and property, a deep learning method is employed to predict the work function in this study. Specifically, a data set of over 30,000 compositions with the work function on boron-doped graphene at different concentrations and doping positions via density functional theory simulations was established through ab initio calculations. Then, a novel fusion model (GT-Net) combining transformers and graph neural networks (GNNs) was proposed. After that, improved effective GNN-based descriptors were developed. Finally, three different GNN methods were compared, and the results show that the proposed method could accurately predicate the work function with the R2 = 0.975 and RMSE = 0.027. This study not only provides the possibility of designing materials with specific properties at the atomic level but also demonstrates the performance of GNNs on graph-level tasks with the same graph structure and atomic number.

Enhancing Interfacial Capacitance by Boron Doping in Vertically Porous Carbon Toward High-Performance AC Filtering Electrochemical Capacitors.

Electrochemical capacitors (ECs) show great perspective in alternate current (AC)filtering once they simultaneously reach ultra-fast response and high capacitance density. Nevertheless, the structure-design criteria of the two key properties are often mutually incompatible in electrode construction. Herein, it is proposed that combining vertically oriented porous carbon with enhanced interfacial capacitance (Ci) can efficiently solve this issue. Theoretically, the density function theory calculation shows that the Ci of a carbon electrode can be enhanced by boron doping due to the corresponding compact induced charge layer. Experimentally, the vertical-oriented boron-doped graphene nanowalls (BGNWs) electrodes, whose Ci is enhanced from 4.20 to 10.16µFcm-2 upon boron doping, are prepared on a large scale (480 cm2) using a hot-filament chemical vapor deposition technique (HFCVD). Owing to the high Ci and vertically oriented porous structure, BGNWs-based EC has a high capacitance density of 996µFcm-2 with a phase angle of - 79.4° at 120Hz in aqueous electrolyte and a high energy density of 1953µFV2cm-2 in organic electrolyte. As a result, the EC is capable of smoothing 120Hz ripples for 60Hz AC filtering. These results provide enlightening insights on designing high-performance ECs for high-frequency applications.

Designing of multifunctional graphene quantum dot-polyvinyl alcohol-polyglycerol luminescent film for fluorescence detection of pH in sweat

BackgroundWound infection, skin disease, renal failure, cancer, cystic fibrosis, and other pathologies may induce obvious pH changes in sweat. Thus, tracking skin pH changes can help monitor human health in a convenient manner. Owing to their biocompatibility, easy preparation, and sensitive response to pH changes, graphene quantum dots (GQDs) have received increased attention in the optical detection of pH changes. However, their poor luminescent efficiency under visible light excitation and lack of functional diversification limit their application in skin pH monitoring. Therefore, the development of GQDs with excellent ultraviolet protection ability and antibacterial and luminescence performance is essential. ResultsFolic acid-, histidine-, and serine-functionalized boron-doped graphene quantum dots (FHSB-GQDs) were designed and synthesized via thermal treatment. The resulting FHSB-GQDs exhibit strong yellow fluorescence emission under excitation with 490-nm visible light and sensitive pH responsiveness. The peak fluorescence intensity linearly decreases with increasing pH from 4 to 9. Furthermore, the FHSB-GQDs were integrated with polyvinyl alcohol and polyglycerol to form a luminescent film via hydrogen bond interactions. The film exhibits high transparency, mechanical flexibility, ultraviolet protection ability, and antibacterial activity. The presence of polyvinyl alcohol and polyglycerol restricts the free movement of the FHSB-GQDs and improves fluorescence behavior. The film was successfully applied in an intelligent pH-sensing system for monitoring pH changes in human sweat. SignificanceThe graphene quantum dot–polyvinyl alcohol–polyglycerol luminescent film offers excellent transparency, mechanical flexibility, ultraviolet protection ability, antibacterial activity, and luminescence performance. It was successfully applied in an intelligent pH sensing system for the detection of pH changes in human sweat. This study provides a new strategy for the design and construction of wearable sensing systems for health monitoring, facial masks, and medical dressings.

High density electron doping in boron-doped twisted bilayer graphene: a ladder to extended flat-band

Realizing Von Hove singularity (VHS) and extended flat bands of graphene near the Fermi level (EF) to explore many-body interactions with a high tendency towards superconductivity.

First-principles study of catalytic mechanism of boron-doped graphene oxide on oxygen evolution reaction of lithium peroxide

Lithium-oxygen batteries stand out among post-lithium-ion batteries due to their theoretically high energy density, while the sluggish reaction kinetics of lithium peroxide reduces the rate performance of the batteries. Therefore, improving the reaction kinetics of the lithium peroxide and then lowering the charge overpotential are of great importance for realizing reversible lithium-oxygen batteries with high energy density. In this work, the catalytic mechanism of graphene oxide (GO) and boron-doped graphene oxide (BGO) on the oxygen evolution reaction of (Li&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;2&lt;/sub&gt;)&lt;sub&gt;2&lt;/sub&gt; cluster is investigated by first-principles calculations. The results show that the charge transfer from (Li&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;2&lt;/sub&gt;)&lt;sub&gt;2&lt;/sub&gt; cluster to GO and from (Li&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;2&lt;/sub&gt;)&lt;sub&gt;2&lt;/sub&gt; cluster to BGO are 0.59 e and 0.96 e, respectively, suggests that B doping improves the charge transfer from the discharged product to the cathode material. The Gibbs free energy of the 4-electron decomposition process shows that the (Li&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;2&lt;/sub&gt;)&lt;sub&gt;2&lt;/sub&gt; cluster favors the Li-O&lt;sub&gt;2&lt;/sub&gt;-Li decomposition pathway, and the rate-determining step for the reaction on both GO and BGO is the third step, that is, the removal of the third lithium. At the equilibrium potential, the charge overpotential of GO and BGO are 0.76 V and 0.23 V, respectively, showing that B doping greatly reduces the charging overpotential of lithium-oxygen batteries. Moreover, mechanistic analysis shows that B doping enhances the electronic conductance of GO and forms an electron-deficient active center, which facilitates charge transport in cathode and charge transfer from lithium peroxide to cathode materials, thereby reducing the charging overpotential of the lithium-oxygen batteries and improving its cycling performance. The B and O play a synergistic role in catalyzing the oxygen evolution reaction of (Li&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;2&lt;/sub&gt;)&lt;sub&gt;2&lt;/sub&gt; clusters.

Boron-doped graphene topological defects: unveiling high sensitivity to NO molecule for gas sensing applications.

Global air quality has deteriorated significantly in recent years due to large emissions from the transformation industry and combustion vehicles. This issue requires the development of portable, highly sensitive, and selective gas sensors. Nanostructured materials, including defective graphene, have emerged as promising candidates for such applications. In this work, we investigated the B-doped topological line defect in graphene as a sensing material for various gas molecules (CO, CO2, NO, and NH3) based on a combination of density functional theory and the non-equilibrium Green's function method. The electronic transport calculations reveal that the electric current can be confined to the line defect region by gate voltage control, revealing highly reactive sites. The B-doped topological line defect is metallic, favoring the adsorption of NO and NH3 over CO and CO2 molecules. We notice changes in the conductance after gas molecule adsorption, producing a sensitivity of 50% (16%) for NO (NH3). In addition, the recovery time for nitride gases was calculated for different temperatures and radiation frequencies. At 300 K the ultraviolet (UV) has a fast recovery time compared to the visible (VIS) one by about two orders of magnitude. This study gives an understanding of how engineering transport properties at the microscopic level (by topological line defect and chemical B-doping) leads to promising nanosensors for detecting nitride gas.

Synthesis of Co- and V-doped Ta1.1O1.05 electrode material using tryptophan- and aspartic-acid-functionalized boron-doped graphene quantum dots with excellent supercapacitor performance

The study reports one Co and V-doped Ta1.1O1.05-graphene quantum dot composite electrode material with good supercapacitor behavior.

Significantly enhanced supercapacitor performance of W3Nb14O44 by introducing serine and histidine-functionalized and boron-doped graphene quantum dots

This study reported one solution for the synthesis of a W3Nb14O44 electrode material. The multiple structure engineering constructed by special graphene quantum dot significantly improves the supercapacitor performance of W3Nb14O44.

Concurrent Thermal Reduction and Boron-Doped Graphene Oxide by Metal–Organic Chemical Vapor Deposition for Ultraviolet Sensing Application

We synthesized a boron-doped reduced graphene oxide (BrGO) material characterized by various electrical properties, through simultaneous thermal reduction and doping procedures, using a metal–organic chemical vapor deposition technique. X-ray photoelectron spectroscopy (XPS) was used to study the impact of the doping level on the B bonding in the reduced graphene oxide (rGO) layer that is influenced by the annealing temperature. The synthesized BrGO layer demonstrated a high B concentration with a considerable number of O-B bonds, that were altered by annealing temperatures. This resulted in a decreased work function and the formation of a Schottky contact between the BrGO and n-type Si substrate. Due to the higher proportion of B-C and B-C3 bonding in the BrGO/Si device than that in the rGO/Si, the decreased Schottky barrier height of the BrGO/n-Si vertical junction photodetector resulted in a higher responsivity. This study showcases a promise of a simple B-doping method in use to alter the electrical characteristics of graphene materials.

Machine learning approach on the prediction of mechanical characteristics of pristine, boron doped and nitrogen doped graphene

Machine Learning (ML), a subset of Artificial Intelligence has been widely applied in various domains, but it has only just begun to be employed in the field of engineering. In the present investigation, various ML algorithms and artificial neural network (ANN) structures are used for the first time to predict the mechanical properties of pristine, boron-doped, and nitrogen-doped graphene while also taking into account the effects of various types of vacancy defects. Fracture strain, Ultimate Tensile Strength (UTS), and Young’s modulus are all predicted. ML technique reduces the computational cost and time required to find out mechanical properties of these materials. The training dataset for the ML models is developed using Molecular Dynamics (MD) simulations. It was shown that defects and doping both had an adverse effect on mechanical characteristics. While ANN, LASSO, and LASSO Lars have all performed quite well at predicting these features, pipeline polynomial regression has performed best across all datasets. New insights on the research of mechanical characteristics utilizing cutting-edge computational techniques are provided by the discoveries in this research.

Density Functional Theory Analysis of the Impact of Boron Concentration and Surface Oxidation in Boron-Doped Graphene for Sodium and Aluminum Storage

Graphene is thought to be a promising material for many applications. However, pristine graphene is not suitable for most electrochemical devices, where defect engineering is crucial for its performance. We demonstrate how the boron doping of graphene can alter its reactivity, electrical conductivity and potential application for sodium and aluminum storage, with an emphasis on novel metal-ion batteries. Using Density Functional Theory calculations, we investigate both the influence of boron concentration and the oxidation of the material on the mentioned properties. It is demonstrated that the presence of boron in graphene increases its reactivity towards atomic hydrogen and oxygen-containing species; in other words, it makes B-doped graphene more prone to oxidation. Additionally, the presence of these surface functional groups significantly alters the type and strength of the interaction of Na and Al with the given materials. Boron-doping and the oxidation of graphene is found to increase the Na storage capacity of graphene by a factor of up to four, and the calculated sodiation potentials indicate the possibility of using these materials as electrode materials in high-voltage Na-ion batteries.