All-Solid-State Mg-Air Battery Enhanced with Free-Standing N-Doped 3D Nanoporous Graphene.

Nitrogen (N) doping of graphene with a three-dimensional (3D) porous structure, high flexibility, and low cost exhibits potential for developing metal-air batteries to power electric/electronic devices. The optimization of N-doping into graphene and the design of interconnected and monolithic graphene-based 3D porous structures are crucial for mass/ion diffusion and the final oxygen reduction reaction (ORR)/battery performance. Aqueous-type and all-solid-state primary Mg-air batteries using N-doped nanoporous graphene as air cathodes are assembled. N-doped nanoporous graphene with 50-150 nm pores and ≈99% porosity is found to exhibit a Pt-comparable ORR performance, along with satisfactory durability in both neutral and alkaline media. Remarkably, the all-solid-state battery exhibits a peak power density of 72.1 mW cm-2 ; this value is higher than that of a battery using Pt/carbon cathodes (54.3 mW cm-2 ) owing to the enhanced catalytic activity induced by N-doping and rapid air breathing in the 3D porous structure. Additionally, the all-solid-state battery demonstrates better performances than the aqueous-type battery owing to slow corrosion of the Mg anode by solid electrolytes. This study sheds light on the design of free-standing and catalytically active 3D nanoporous graphene that enhances the performance of both Mg-air batteries and various carbon-neutral-technologies using neutral electrolytes.

Theoretically Revealed and Experimentally Demonstrated Synergistic Electronic Interaction of CoFe Dual-Metal Sites on N-doped Carbon for Boosting Both Oxygen Reduction and Evolution Reactions.

Heteronuclear double-atom catalysts, unlike single atom catalysts, may change the charge density of active metal sites by introducing another metal single atom, thereby modifying the adsorption energies of reaction intermediates and increasing the catalytic activities. First, density functional theory calculations are used to figure out the best combination by modeling two transition-metal atoms from Fe, Co, and Ni onto N-doped graphene. Generally, Fe and Co sites are highly active for the oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER), respectively. The combination of Co and Fe to form CoFe-N-C not only further improves the Fe's ORR and Co's OER activities but also greatly enhances the Co site's ORR and Fe site's OER activities. Then, we synthesize the CoFe-N-C by a two-step pyrolysis process and find that the CoFe-N-C exhibits exceptional ORR and OER electrocatalytic activities in alkaline media, significantly superior to Fe-N-C and Co-N-C and even commercial catalysts.

Gap Opening in Double-Sided Highly Hydrogenated Free-Standing Graphene.

Conversion of free-standing graphene into pure graphane─where each C atom is sp3 bound to a hydrogen atom─has not been achieved so far, in spite of numerous experimental attempts. Here, we obtain an unprecedented level of hydrogenation (≈90% of sp3 bonds) by exposing fully free-standing nanoporous samples─constituted by a single to a few veils of smoothly rippled graphene─to atomic hydrogen in ultrahigh vacuum. Such a controlled hydrogenation of high-quality and high-specific-area samples converts the original conductive graphene into a wide gap semiconductor, with the valence band maximum (VBM) ∼ 3.5 eV below the Fermi level, as monitored by photoemission spectromicroscopy and confirmed by theoretical predictions. In fact, the calculated band structure unequivocally identifies the achievement of a stable, double-sided fully hydrogenated configuration, with gap opening and no trace of π states, in excellent agreement with the experimental results.

Eight-Component Nanoporous High-Entropy Oxides with Low Ru Contents as High-Performance Bifunctional Catalysts in Zn-Air Batteries.

One major challenge in heterogeneous catalysis is to reduce the usage of noble metals while maintaining the overall catalytic stability and efficiency in various chemical environments. In this work, a series of high-entropy catalysts are synthesized by a chemical dealloying method and find the increased entropy effect and non-noble metal contents would facilitate the formation of complete oxides with low crystallinity. Importantly, an optimal eight-component high-entropy oxide (HEO, Al-Ni-Co-Ru-Mo-Cr-Fe-Ti) is identified, which exhibits further enhanced catalytic activity for the oxygen evolution reaction (OER) as compared to the previously reported quinary AlNiCoRuMo and the widely-used commercial RuO2 catalysts, and at the same time similar catalytic activity for the oxygen reduction reaction (ORR) as the commercial Pt/C with a half-wave potential of 0.87 V. Such high-performance bi-functional catalysts, however, only require a half loading amount of Ru as compared to the quinary AlNiCoRuMo, due to the underlying Cr-Fe synergistic effects on tuning the electronic structures at active surface sites, as revealed by the first-principles density functional theory calculations of the authors. The eight-component HEO also demonstrates excellent stability under continuous electrochemical working conditions, suitable for a wide range of applications such as metal-air batteries.

Highly Strengthened and Toughened Zn-Li-Mn Alloys as Long-Cycling Life and Dendrite-Free Zn Anode for Aqueous Zinc-Ion Batteries.

Zn-ion batteries (ZIBs) using aqueous electrolyte, recently, have been a hot topic owing to the high safety, low cost, and high specific energy capacity. However, the formation of dendrite and side reactions on the Zn anode during cycling inhibit the application of ZIBs. An advanced Zn anode by alloying a small amount of Li and Mn with Zn is hereby reported. It is found that Li and Mn can form cationic ions which restrain lateral diffusion of Zn ions and regulate zinc electrodeposition through the electrostatic shield mechanism. As a result, the formation of Zn dendrite is greatly inhibited. This process also mitigates the formation of Zn-based byproduct and Zn passivation. Consequently, the symmetric ZnLiMn/ZnLiMn cell presents a small overpotential of 30 mV at 1 mA cm-2 , greatly enhanced cycling durability (1000 h at a current density of 1 mA cm-2 ), and a dendrite-free morphology after cycles. Moreover, the authors find that the ZnLiMn alloy has greatly enhanced mechanical properties. The assembled ZnLiMn/MnO2 full cell can retain 96% capacity after 400 cycles at 1 C. Thus, the alloying low-cost Li/Mn strategy is very promising for large-scale production of dendrite-free Zn electrode in rechargeable ZIBs.

MOF Structure Engineering to Synthesize CoNC Catalyst with Richer Accessible Active Sites for Enhanced Oxygen Reduction.

Single-atom cobalt-based CoNC are promising low-cost electrocatalysts for oxygen reduction reaction (ORR). However, further increasing the single cobalt-based active sites and the ORR activity remain a major challenge. Herein, an acetate (OAc) assisted metal-organic framework (MOF) structure-engineering strategy is developed to synthesize hierarchical accordion-like MOF with higher loading amount and better spatial isolation of Co and much higher yield when compared with widely reported polyhedron MOF. After pyrolysis, the accordion-structured CoNC (CoNC (A)) is loaded with denser CoN4 active sites (Co: 2.88 wt%), approximately twice that of Co in the CoNC reported. The presence of OAc in MOF also induces the generation of big pores (5-50 nm) for improving the accessibility of active sites and mass transfer during catalytic reactions. Consequently, the CoNC (A) catalyst shows an admirable ORR activity with a E1/2 of 0.89 V (40 mV better than Pt/C) in alkaline electrolytes, outstanding durability, and absolute tolerance to methanol in both alkaline and acidic media. The CoNC-based Zn-air battery exhibits a high specific capacity (976 mAh g-1 Zn ), power density (158 mW cm-2 ), rate capability, and long-term stability. This work demonstrates a reliable approach to construct single atom doped carbon catalysts with denser accessible active sites through MOF structure engineering.

Towards free-standing graphane: atomic hydrogen and deuterium bonding to nano-porous graphene.

Graphane is formed by bonding hydrogen (and deuterium) atoms to carbon atoms in the graphene mesh, with modification from the pure planar sp2 bonding towards an sp3 configuration. Atomic hydrogen (H) and deuterium (D) bonding with C atoms in fully free-standing nano porous graphene (NPG) is achieved, by exploiting low-energy proton (or deuteron) non-destructive irradiation, with unprecedented minimal introduction of defects, as determined by Raman spectroscopy and by the C 1s core level lineshape analysis. Evidence of the H- (or D-) NPG bond formation is obtained by bringing to light the emergence of a H- (or D-) related sp3-distorted component in the C 1s core level, clear fingerprint of H-C (or D-C) covalent bonding. The H (or D) bonding with the C atoms of free-standing graphene reaches more than 1/4 (or 1/3) at% coverage. This non-destructive H-NPG (or D-NPG) chemisorption is very stable at high temperatures up to about 800 K, as monitored by Raman and x-ray photoelectron spectroscopy, with complete healing and restoring of clean graphene above 920 K. The excellent chemical and temperature stability of H- (and D-) NPG opens the way not only towards the formation of semiconducting graphane on large-scale samples, but also to stable graphene functionalisation enabling futuristic applications in advanced detectors for the ß-spectrum analysis.

Heavily Doped and Highly Conductive Hierarchical Nanoporous Graphene for Electrochemical Hydrogen Production.

Heavy chemical doping and high electrical conductivity are two key factors for metal-free graphene electrocatalysts to realize superior catalytic performance toward hydrogen evolution. However, heavy chemical doping usually leads to the reduction of electrical conductivity because the catalytically active dopants give rise to additional electron scattering and hence increased electrical resistance. A hierarchical nanoporous graphene, which is comprised of heavily chemical doped domains and a highly conductive pure graphene substrate, is reported. The hierarchical nanoporous graphene can host a remarkably high concentration of N and S dopants up to 9.0 at % without sacrificing the excellent electrical conductivity of graphene. The combination of heavy chemical doping and high conductivity results in high catalytic activity toward electrochemical hydrogen production. This study has an important implication in developing multi-functional electrocatalysts by 3D nanoarchitecture design.

Data-driven machine learning prediction of glass transition temperature and the glass-forming ability of metallic glasses.

The limited glass-forming ability (GFA) poses a significant challenge for the practical applications of metallic glasses (MGs). The development of high-GFA MGs typically involves trial-and-error processes to screen materials with a large critical diameter (Dmax), which serves as a criterion for determining the GFA. The formation and stability of MGs are influenced by the glass transition temperature (Tg). Over the past decade, the emergence of machine learning (ML) has shown great promise in the exploration of high-GFA materials. However, the contribution of material features to Tg and Dmax predictions, as well as their correlations, remains ambiguous, posing a challenge to achieving high prediction accuracy. Herein, we present a comprehensive dataset consisting of 1764 datapoints for Tg and 1296 datapoints for Dmax. The governing rules for GFA have been established through feature significance analysis. The light gradient boosting (LGB) model exhibits remarkable accuracy in predicting Tg, utilizing sixteen features, achieving a coefficient of determination (R2) score of 0.984 and a root mean square error (RMSE) of 20.196 K. An integrated ML model, based on the weighted voting of three basic models, is developed to enhance the accuracy of Dmax prediction, achieving an R2 score of 0.767 and an RMSE of 2.331 mm. Additionally, a GFA rule is proposed to explore materials with large Dmax values, defined by satisfying the criteria of a thermal conductivity difference ranging from 0.60 to 1.32 and an entropy density exceeding 1.05. Our work provides valuable insights into Tg and Dmax predictions and facilitates the exploration of potential high-GFA MGs through the implementation of a well-established ML model and GFA rules.

Curvature effects on the bifunctional oxygen catalytic performance of single atom metal-N-C.

Understanding the fundamental relationship between the structural information of electrocatalysts and their catalytic activities plays a key role in controlling many important electrochemical processes. Recently, single-atom catalysts (SACs) with the so-called MN4 structure, consisting of a central transition metal quadruply bound to four pyridine nitrogen atoms all situated in an extended carbon-based matrix, have attracted intensive scientific attention owing to their exceptional catalytic performance. In this work, we perform the first-principles density functional theory (DFT) calculations to explore the curvature effects of the carbon matrix surfaces on the catalytic activities for two fundamental electrochemical processes, namely, the oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER). Our DFT results suggest that the curved surface structure can weaken the interaction between the metal atom and the N-doped carbon matrix, modify the electronic structure of the metal atom, and thus increase the adsorption strength of the reaction intermediates, resulting in enhanced OER and ORR catalytic activities of MN4 catalysts. More importantly, a prediction model is developed to evaluate the bifunctional catalytic activities of such catalysts based on their directly obtained parameters including the surface curvature of the catalysts, the number of d electrons of the metal element, and the electronegativity of the metal atom and its coordination atoms in MN4 catalysts. This prediction model not only provides some candidates, for example, FeN4, CoN4 and OsN4 for the ORR; CoN4, NiN4, RuN4, RhN4 and IrN4 for the OER; and CoN4, RuN4, IrN4 and OsN4 for the bifunctional ORR and OER, but also reasonably links the structure of catalysts with their catalytic performance, providing new possibilities for the quick design of high-performance catalysts.

Deep Generative Model for Inverse Design of High-Temperature Superconductor Compositions with Predicted Tc > 77 K.

Identifying new superconductors with high transition temperatures (Tc > 77 K) is a major goal in modern condensed matter physics. The inverse design of high Tc superconductors relies heavily on an effective representation of the superconductor hyperspace due to the underlying complexity involving many-body physics, doping chemistry and materials, and defect structures. In this study, we propose a deep generative model that combines two widely used machine learning algorithms, namely, the variational auto-encoder (VAE) and the generative adversarial network (GAN), to systematically generate unknown superconductors under the given high Tc condition. After training, we successfully identified the distribution of the representative hyperspace of superconductors with different Tc, in which many superconductor constituent elements were found adjacent to each other with their neighbors in the periodic table. Equipped with the conditional distribution of Tc, our deep generative model predicted hundreds of superconductors with Tc > 77 K, as predicted by the published Tc prediction models in the literature. For the copper-based superconductors, our results reproduced the variation in Tc as a function of the Cu concentration and predicted an optimal Tc = 129.4 K, when the Cu concentration reached 2.41 in Hg0.37Ba1.73Ca1.18Cu2.41O6.93Tl0.69. We expect that such an inverse design model and comprehensive list of potential high Tc superconductors would greatly facilitate future research activities in superconductors.

Accurate and efficient machine learning models for predicting hydrogen evolution reaction catalysts based on structural and electronic feature engineering in alloys.

Predictive materials design of high-performance alloy electrocatalysts is a grand challenge in hydrogen production via water electrolysis. The vast combinatorial space of element substitutions in alloy electrocatalysts offers a wealth of candidate materials, but presents a significant challenge in terms of experimental and computational exploration of all possible options. Recent scientific and technological developments in machine learning (ML) have offered a new opportunity to accelerate such electrocatalyst materials design. Herein, by incorporating both the electronic and structural properties of alloys, we are able to construct accurate and efficient ML models and predict high-performance alloy catalysts for the hydrogen evolution reaction (HER). We have identified the light gradient boosting (LGB) algorithm as the best-performed method, with an excellent coefficient of determination (R2) value reaching 0.921 and the corresponding root-mean-square error (RMSE) being 0.224 eV. The average marginal contributions of alloy features towards ΔGH* values are estimated to determine the importance of various features during the prediction processes. Our results indicate that both the electronic properties of constituent elements and the structural adsorption site features play the most critical roles in the ΔGH* prediction. Furthermore, 84 potential alloys with |ΔGH*| values less than 0.1 eV are successfully screened out of 2290 candidates selected from the Material Project (MP) database. It is reasonable to expect that the ML models with structural and electronic feature engineering developed in this work would provide new insights in future electrocatalyst developments for the HER and other heterogeneous reactions.

Large Data Set-Driven Machine Learning Models for Accurate Prediction of the Thermoelectric Figure of Merit.

The figure of merit (zT) is a key parameter to measure the performance of thermoelectric materials. At present, the prediction of zT values via machine leaning has emerged as a promising method for exploring high-performance materials. However, the machine learning-based predictions still suffer from unsatisfactory accuracy, and this is related to the size of the data set, the hyperparameters of models, and the quality of the data. In this work, 5038 pieces of data of thermoelectric materials were selected, and several regression models were generated to predict zT values. This large data set-driven light gradient boosting (LGB) model with 57 features performed with an excellent accuracy, achieving a coefficient of determination (R2) value of 0.959, a root mean squared error (RMSE) of 0.094, a mean absolute error (MAE) of 0.057, and a correlation coefficient (R) of 0.979. Owing to the large size of the data set, the prediction accuracy exceeds that of most reported zT predictions via machine learning. The "ME Lattice Parameter" was verified as the most important feature in the zT prediction. Furthermore, nine potential candidates were screened out from among one million pieces of data. This study solves the problem of the data set size, adjusts the hyperparameters of the models, uses feature engineering to improve data quality, and provides an efficient strategy to perform wide-ranging screening for promising materials.

Simultaneous Improvement of Oxygen Reduction and Catalyst Anchoring via Multiple Dopants on Mesoporous Carbon Frameworks for Flexible Al-Air Batteries.

Mesoporous carbon supported non-noble metals, as promising catalysts for boosting the oxygen reduction reaction (ORR) in metal-air batteries, usually face challenges of low activity and performance degradation caused by the catalyst detachment from carbon substrates. Herein, a one-stone-two-birds strategy is reported to simultaneously improve the ORR activity and anchor nanosized MnS catalysts on a mesoporous carbon framework via nitrogen (N) and sulfur (S) dopants (MnS/NS-C). Synchrotron-based X-ray absorption spectroscopy (XAS) confirms the existence of Mn-N and Mn-S bonds, which firmly anchor active MnS nanoparticles. Density functional theory (DFT) calculations reveal that the N, S codoping lowers the d-band center of Mn and optimizes ORR intermediate adsorption. An excellent ORR performance (the onset and half-wave potential of 1.07 and 0.91 V) and long-term durability are achieved for MnS/NS-C in alkaline media. The flexible Al-air battery, using MnS/NS-C as the cathode catalyst, shows a power density of 134.6 mW cm-2 in comparison to the Pt/C-based counterpart of 106.2 mW cm-2. This study constructs a stable interaction with non-noble catalysts and carbon substrates for enhancing catalytic activity and durability in metal-air batteries.

A fourteen-component high-entropy alloy@oxide bifunctional electrocatalyst with a record-low ΔE of 0.61 V for highly reversible Zn-air batteries.

Nanostructured high-entropy materials such as alloys, oxides, etc., are attracting extensive attention because of their widely tunable surface electronic structure/catalytic activity through mixing different elements in one system. To further tune the catalytic performance and multifunctionality, the designed fabrication of multicomponent high-entropy nanocomposites such as high-entropy alloy@high-entropy oxides (HEA@HEO) should be very promising. In this work, we design a two-step alloying-dealloying strategy to synthesize ultra-small HEA nanoclusters (∼2 nm) loaded on nanoporous HEO nanowires, and the compositions of both the HEA and HEO can be adjusted separately. To demonstrate this concept, a seven-component HEA (PtPdAuAgCuIrRu) clusters@seven-component HEO (AlNiCoFeCrMoTi)3O4 was prepared, which is highly active for both oxygen evolution and reduction reactions. Our comprehensive experimental results and first-principles density functional theory (DFT) calculations clearly show that better oxygen evolution reaction (OER) performance is obtained by optimizing the composition of the HEO support, and the seven-component HEA nanocluster is much more active for the ORR when compared with pure Pt due to the modified surface electronic structure. Specifically, the high-entropy composite exhibits an OER activity comparable to the best reported value, and the ORR activity exceeded the performance of commercial Pt/C in alkaline solutions with a record-low bifunctional ΔE of 0.61 V in 0.1 M KOH solution. This work shows an important route to prepare complex HEA@HEO nanocomposites with tuned catalytic performance for multifunctional catalysis and energy conversion.

Catalytic activity of graphene-covered non-noble metals governed by proton penetration in electrochemical hydrogen evolution reaction.

Graphene-covering is a promising approach for achieving an acid-stable, non-noble-metal-catalysed hydrogen evolution reaction (HER). Optimization of the number of graphene-covering layers and the density of defects generated by chemical doping is crucial for achieving a balance between corrosion resistance and catalytic activity. Here, we investigate the influence of charge transfer and proton penetration through the graphene layers on the HER mechanisms of the non-noble metals Ni and Cu in an acidic electrolyte. We find that increasing the number of graphene-covering layers significantly alters the HER performances of Ni and Cu. The proton penetration explored through electrochemical experiments and simulations reveals that the HER activity of the graphene-covered catalysts is governed by the degree of proton penetration, as determined by the number of graphene-covering layers.

Designing Ru-doped Zn3V3O8 bifunctional OER and HER catalysts through a unified computational and experimental approach.

Developing stable and cost-effective catalysts is the key to the next-generation renewable energy conversion technology. Here we unify computational and experimental approaches to use the Zn3V3O8 (001) surface supporting noble metal Ru as a bifunctional catalyst for the OER and HER in alkaline media. In particular, different reaction sites have been studied at four surface terminations along the [001] orientation: the A-layer with V atoms at octahedral sites, the C-layer with V and Zn atoms at octahedral sites, and with additional Zn atoms at tetrahedral sites (B-layer and D-layer, respectively). The first-principles density functional theory (DFT) results indicate that the B-layer termination with V and tetrahedrally coordinated Zn on the top showed the best OER catalytic effect, while the HER favored the D-layer termination with extra Zn atoms at the octahedral sites on the top layer. Our DFT results also suggest that Ru doping by substituting V and Zn atoms at the octahedral site could dramatically enhance the catalytic activities for the OER and HER, respectively. In particular, compared to undoped Zn3V3O8, Ru doping could reduce the calculated OER overpotential from 0.58 V to 0.30 V, which has been confirmed by our experimental results that the OER overpotential decreased from 480 mV to 260 mV at a current density of 10 mA cm-o. Moreover, the experimental results show that Ru doping could reduce the HER overpotential from 152 mV to 70 mV at a current density of 10 mA cm-r. The new insights into the underlying catalytic mechanisms may be further extended to many similar electrocatalytic processes.

In situ coupling of Ag nanoparticles with high-entropy oxides as highly stable bifunctional catalysts for wearable Zn-Ag/Zn-air hybrid batteries.

With the combination of the advantages of both Zn-Ag and Zn-air batteries, hybrid Zn-Ag/Zn-air batteries nevertheless suffer greatly from structural instability and activity degradation of the catalysts at the air electrodes. Herein, we introduce a scalable chemical dealloying procedure to synthesize mutually interacting and stable bifunctional catalysts, consisting of imbedded Ag nanoparticles for the oxygen reduction reaction (ORR) and quantitatively designed multicomponent high-entropy oxides (HEOs) for the oxygen evolution reaction (OER). The ORR performance and the Zn-Ag battery capacity can be precisely controlled by the content of Ag nanoparticles. Impressively, with a significantly low Ag content (∼9.13 wt%) in the bifunctional (AlNiCoFeCr)3O4/Ag, our hybrid Zn-Ag/Zn-air batteries using such catalysts are able to be continuously charged/discharged for more than 450 h and deliver a high energy density of 810 W h kg-1. We expect that these stabilized noble metals in HEO nanocomposites may work as multifunctional electrocatalysts in many other energy conversion devices.

Deuterium Adsorption on Free-Standing Graphene.

A suitable way to modify the electronic properties of graphene-while maintaining the exceptional properties associated with its two-dimensional (2D) nature-is its functionalisation. In particular, the incorporation of hydrogen isotopes in graphene is expected to modify its electronic properties leading to an energy gap opening, thereby rendering graphene promising for a widespread of applications. Hence, deuterium (D) adsorption on free-standing graphene was obtained by high-energy electron ionisation of D2 and ion irradiation of a nanoporous graphene (NPG) sample. This method allows one to reach nearly 50 at.% D upload in graphene, higher than that obtained by other deposition methods so far, towards low-defect and free-standing D-graphane. That evidence was deduced by X-ray photoelectron spectroscopy of the C 1s core level, showing clear evidence of the D-C sp3 bond, and Raman spectroscopy, pointing to remarkably clean and low-defect production of graphane. Moreover, ultraviolet photoelectron spectroscopy showed the opening of an energy gap in the valence band. Therefore, high-energy electron ionisation and ion irradiation is an outstanding method for obtaining low defect D-NPG with a high D upload, which is very promising for the fabrication of semiconducting graphane on large scale.

Chemical Dopants on Edge of Holey Graphene Accelerate Electrochemical Hydrogen Evolution Reaction.

Carbon-based metal-free catalysts for the hydrogen evolution reaction (HER) are essential for the development of a sustainable hydrogen society. Identification of the active sites in heterogeneous catalysis is key for the rational design of low-cost and efficient catalysts. Here, by fabricating holey graphene with chemically dopants, the atomic-level mechanism for accelerating HER by chemical dopants is unveiled, through elemental mapping with atomistic characterizations, scanning electrochemical cell microscopy (SECCM), and density functional theory (DFT) calculations. It is found that the synergetic effects of two important factors-edge structure of graphene and nitrogen/phosphorous codoping-enhance HER activity. SECCM evidences that graphene edges with chemical dopants are electrochemically very active. Indeed, DFT calculation suggests that the pyridinic nitrogen atom could be the catalytically active sites. The HER activity is enhanced due to phosphorus dopants, because phosphorus dopants promote the charge accumulations on the catalytically active nitrogen atoms. These findings pave a path for engineering the edge structure of graphene in graphene-based catalysts.