Single-atom catalysts supported on a hybrid structure of boron nitride/graphene for efficient nitrogen fixation via synergistic interfacial interactions.

Hexagonal boron nitride (BN) shows significant chemical stability and promising thermal nitrogen reduction reaction (NRR) activity but suffers from low conductivity in electrolysis with a wide band gap. To overcome this problem, two-dimensional (2D) BN and graphene (G) are designed as a heterostructure, namely BN/G. According to density functional theory (DFT), the higher conductivity of G narrows the band gap of BN by inducing some electronic states near the Fermi energy level (Ef). Once transition metals (TMs) are anchored in the BN/G structure as single atom catalysts (SACs), the NRR activity improves as the inert BN basal layer activates with moderate *NH2 binding energy and further the band gap is reduced to zero. V (vanadium) and W (tungsten) SACs exhibit the best performance with limiting potentials of -0.22 and -0.41 V, respectively. This study helps in understanding the improvement of the NRR activity of BN, providing physical insights into the adsorbate-TM interaction.

High-Performing Atomic Electrocatalyst for Chlorine Evolution Reaction.

Electrocatalysts facilitating chlorine evolution reaction (ClER) play a vital role in chlor-alkali industries. Owing to a huge amount of chlorine consumed worldwide, inexpensive high-performing catalysts for Cl2 production are highly demanded. Here, a superb ClER catalyst fabricated through uniform dispersion of Pt single atoms (SAs) in C2 N2 moieties of N-doped graphene (denoted as Pt-1) is presented, which demonstrates near 100% exclusive ClER selectivity, long-term durability, extraordinary Cl2 production rate (3500 mmol h-1 gPt -1 ), and >140 000-fold increased mass activity over industrial electrodes in acidic medium. Excitingly, at the typical chlor-alkali industries' operating temperature (80 °C), Pt-1 supported on carbon paper electrode requires a near thermoneutral ultralow overpotential of 5 mV at 1 mA cm-2 current density to initiate the ClER, consistent with the predicted density functional theory (DFT) calculations. Altogether these results show the promising electrocatalyst of Pt-1 toward ClER.

Fast atomic structure optimization with on-the-fly sparse Gaussian process potentials.

We apply on-the-fly machine learning potentials (MLPs) using the sparse Gaussian process regression (SGPR) algorithm for fast optimization of atomic structures. Great acceleration is achieved even in the context of a single local optimization. Although for finding the exact local minimum, due to limited accuracy of MLPs, switching to another algorithm may be needed. For random gold clusters, the forces are reduced to ∼0.1 eV Å-1within less than ten first-principles (FP) calculations. Because of highly transferable MLPs, this algorithm is specially suitable for global optimization methods such as random or evolutionary structure searching or basin hopping. This is demonstrated by sequential optimization of random gold clusters for which, after only a few optimizations, FP calculations were rarely needed.

Unveiling the Role of Charge Transfer in Enhanced Electrochemical Nitrogen Fixation at Single-Atom Catalysts on BX Sheets (X = As, P, Sb).

To tune single-atom catalysts (SACs) for effective nitrogen reduction reaction (NRR), we investigate various transition metals implanted on boron-arsenide (BAs), boron-phosphide (BP), and boron-antimony (BSb) using density functional theory (DFT). Interestingly, W-BAs shows high catalytic activity and excellent selectivity with an insignificant barrier of only 0.05 eV along the distal pathway and a surmountable kinetic barrier of 0.34 eV. The W-BSb and Mo-BSb exhibit high performances with limiting potentials of -0.19 and -0.34 V. The Bader-charge descriptor reveals that the charge transfers from substrate to *NNH in the first protonation step and from *NH3 to substrate in the last protonation step, circumventing a big hurdle in NRR by achieving negative free energy change of *NH2 to *NH3. Furthermore, machine learning (ML) descriptors are introduced to reduce computational cost. Our rational design meets the three critical prerequisites of chemisorbing N2 molecules, stabilizing *NNH, and destabilizing *NH2 adsorbates for high-efficiency NRR.

Sparse Gaussian Process Regression-Based Machine Learned First-Principles Force-Fields for Saturated, Olefinic, and Aromatic Hydrocarbons.

Universal machine learning (ML) interatomic potentials (IAPs) for saturated, olefinic, and aromatic hydrocarbons are generated by using the Sparse Gaussian process regression algorithm. The universal potentials are obtained by combining the potentials for the previously trained alkane/polyene systems and the potentials generated with the presently trained cyclic/aromatic hydrocarbon systems, along with the newly trained cross-terms between the two systems. The ML-IAPs have been trained using the PBE + D3 level of density functional theory for the on-the-fly adaptive sampling of various hydrocarbon molecules and these clusters composed of small molecules. We tested the ML-IAPs and found that they correctly predicted the structures and energies of the ß-carotene monomer and dimer. Also, the simulations of liquid ethylene reproduced the molecular volume and the simulations of toluene crystals reproduced higher stability of the α-phase over the ß-phase. These ab initio-level force-fields could eventually evolve toward universal organic/polymeric/biomolecular systems.

Reduced Potential Barrier of Sodium-Substituted Disordered Rocksalt Cathode for Oxygen Evolution Electrocatalysts.

Cation-disordered rocksalt (DRX) cathodes have been viewed as next-generation high-energy density materials surpassing conventional layered cathodes for lithium-ion battery (LIB) technology. Utilizing the opportunity of a better cation mixing facility in DRX, we synthesize Na-doped DRX as an efficient electrocatalyst toward oxygen evolution reaction (OER). This novel OER electrocatalyst generates a current density of 10 mA cm−2 at an overpotential (η) of 270 mV, Tafel slope of 67.5 mV dec−1, and long-term stability >5.5 days' superior to benchmark IrO2 (η = 330 mV with Tafel slope = 74.8 mV dec−1). This superior electrochemical behavior is well supported by experiment and sparse Gaussian process potential (SGPP) machine learning-based search for minimum energy structure. Moreover, as oxygen binding energy (OBE) on the surface closely relates to OER activity, our density functional theory (DFT) calculations reveal that Na-doping assists in facile O2 evolution (OBE = 5.45 eV) compared with pristine-DRX (6.51 eV).

Machine Learning of First-Principles Force-Fields for Alkane and Polyene Hydrocarbons.

Machine learning (ML) interatomic potentials (ML-IAPs) are generated for alkane and polyene hydrocarbons using on-the-fly adaptive sampling and a sparse Gaussian process regression (SGPR) algorithm. The ML model is generated based on the PBE+D3 level of density functional theory (DFT) with molecular dynamics (MD) for small alkane and polyene molecules. Intermolecular interactions are also trained with clusters and condensed phases of small molecules. It shows excellent transferability to long alkanes and closely describes the ab inito potential energy surface for polyenes. Simulation of liquid ethane also shows reasonable agreement with experimental reports. This is a promising initiative toward a universal ab initio quality force-field for hydrocarbons and organic molecules.

Superb water splitting activity of the electrocatalyst Fe3Co(PO4)4 designed with computation aid.

For efficient water splitting, it is essential to develop inexpensive and super-efficient electrocatalysts for the oxygen evolution reaction (OER). Herein, we report a phosphate-based electrocatalyst [Fe3Co(PO4)4@reduced-graphene-oxide(rGO)] showing outstanding OER performance (much higher than state-of-the-art Ir/C catalysts), the design of which was aided by first-principles calculations. This electrocatalyst displays low overpotential (237 mV at high current density 100 mA cm-2 in 1 M KOH), high turnover frequency (TOF: 0.54 s-1), high Faradaic efficiency (98%), and long-term durability. Its remarkable performance is ascribed to the optimal free energy for OER at Fe sites and efficient mass/charge transfer. When a Fe3Co(PO4)4@rGO anodic electrode is integrated with a Pt/C cathodic electrode, the electrolyzer requires only 1.45 V to achieve 10 mA cm-2 for whole water splitting in 1 M KOH (1.39 V in 6 M KOH), which is much smaller than commercial Ir-C//Pt-C electrocatalysts. This cost-effective powerful oxygen production material with carbon-supporting substrates offers great promise for water splitting.

Size-dependent conformational change in halogen-π interaction: from benzene to graphene.

Diatomic halogen molecules X2 (X = Cl/Br) favor the edge-to-face conformation on benzene with significant electrostatic interaction via halogen bonding. In contrast, they favor the stacked conformation on graphene with negligible electrostatic interaction. As the aromatic ring expands, the inner facial side becomes almost electrostatically neutral. On coronene, the two conformations are compatible.

Contractile reserve determined on exercise echocardiography in patients with severe aortic regurgitation.

BACKGROUND: The aim of this study was to assess contractile reserve (CR) in patients with severe aortic regurgitation (AR), and potential clinical applications of CR in minimally symptomatic patients. METHODS AND RESULTS: Symptom-limited treadmill exercise echocardiography was performed in 58 asymptomatic or mildly symptomatic patients with chronic severe AR (male 69%, 50±14 years). Patients with increased ejection fraction (EF) after exercise were denoted as having CR (+) and those without increased EF were categorized as CR (-). CR (+) was found in 31 and CR (-) in 27 patients. Compared with CR (+), the CR (-) group had lower EF, larger effective regurgitant orifice, regurgitant volume, left ventricular (LV) end-diastolic dimension, LV end-systolic dimension (LVESD), and LV mass index. On multiple logistic regression analysis LVESD index (LVESDI; odds ratio -0.354, P<0.0001) was an independent predictor of CR. One-third of patients with LVESD >50mm, however, were found to be CR (+), while one-third of patients with smaller LVESD were CR (-). CONCLUSIONS: Although CR is best related to baseline resting LVESDI, one-third of patients were found to have discordance between the presence of CR and the LV dimension recommended for aortic valve replacement (AVR) in minimally symptomatic patients with severe AR, suggesting that exercise test may be able to further stratify the current guideline for AVR.