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Development of cost-effective hydrogen evolution reaction (HER) catalysts with outstanding catalytic activity, replacing cost-prohibitive noble metal-based catalysts, is critical for practical green hydrogen production. A popular strategy for promoting the catalytic performance of noble metal-free catalysts is to incorporate earth-abundant transition metal (TM) atoms into nanocarbon platforms such as carbon quantum dots (CQDs). Although data-driven catalyst design methods can significantly accelerate the rational design of TM element-doped CQD (M@CQD) catalysts, they suffer from either a simplified theoretical model or the prohibitive cost and complexity of experimental data generation. In this study, we propose an effective and facile HER catalyst design strategy based on machine learning (ML) and ML model verification using electrochemical methods accompanied with density functional theory (DFT) simulations. Based on a Bayesian genetic algorithm (BGA) ML model, the Ni@CQD catalyst on a three-dimensional reduced graphene oxide (3D rGO) conductor is proposed as the best HER catalyst under the optimal conditions of catalyst loading, electrode type, and temperature and pH of electrolyte. We validate the ML results with electrochemical experiments, where the Ni@CQD catalyst exhibited superior HER activity, requiring an overpotential of 189 mV to achieve 10 mA cm-2 with a Tafel slope of 52 mV dec-1 and impressive durability in acidic media. We expect that this methodology and the excellent performance of the Ni@CQD catalyst provide an effective route for the rational design of highly active electrocatalysts for commercial applications.

Spinel-type LiNi0.5Mn1.5O4 (LNMO) is one of the most promising 5 V-class cathode materials for Li-ion batteries that can achieve high energy density and low production costs. However, in liquid electrolyte cells, the high voltage causes continuous cell degradation through the oxidative decomposition of carbonate-based liquid electrolytes. In contrast, some solid-state electrolytes have a wide electrochemical stability range and can withstand the required oxidative potential. In this work, a thin-film battery consisting of a LNMO cathode with a solid lithium phosphorus oxynitride (LiPON) electrolyte is tested and their interface before and after cycling is characterized. With Li metal as the anode, this system can deliver stable performance for 600 cycles with an average Coulombic efficiency > 99%. Neutron depth profiling indicates a slight overlithiated layer at the interface prior to cycling; a result that is consistent with the excess charge capacity measured during the first cycle. Cryogenic electron microscopy further reveals intimate contact between LNMO and LiPON without noticeable structure and chemical composition evolution after extended cycling, demonstrating the superior stability of LiPON against a high voltage cathode. Consequently, we propose design guidelines for interface engineering that could accelerate the commercialization of a high voltage cell with solid or liquid electrolytes., Comment: 21 pages and 5 figures

X-ray Computed Tomography (X-ray CT) is a well-known non-destructive imaging technique where contrast originates from the materials' absorption coefficients. Novel battery characterization studies on increasingly challenging samples have been enabled by the rapid development of both synchrotron and laboratory-scale imaging systems as well as innovative analysis techniques. Furthermore, the recent development of laboratory nano-scale CT (NanoCT) systems has pushed the limits of battery material imaging towards voxel sizes previously achievable only using synchrotron facilities. Such systems are now able to reach spatial resolutions down to 50 nm. Given the non-destructive nature of CT, in-situ and operando studies have emerged as powerful methods to quantify morphological parameters, such as tortuosity factor, porosity, surface area, and volume expansion during battery operation or cycling. Combined with powerful Artificial Intelligence (AI)/Machine Learning (ML) analysis techniques, extracted 3D tomograms and battery-specific morphological parameters enable the development of predictive physics-based models that can provide valuable insights for battery engineering. These models can predict the impact of the electrode microstructure on cell performances or analyze the influence of material heterogeneities on electrochemical responses. In this work, we review the increasing role of X-ray CT experimentation in the battery field, discuss the incorporation of AI/ML in analysis, and provide a perspective on how the combination of multi-scale CT imaging techniques can expand the development of predictive multiscale battery behavioral models., Comment: 33 pages, 5 figures

Perovskite solar cells have drawn much attention in recent years, owing to its world-record setting photovoltaic performances. Despite its promising use in tandem applications and flexible devices, its practicality is still limited by its structural instability often arising from ion migration and defect formation. While it is generally understood that ion instability is a primary cause for degradation, there is still a lack of direct evidence of structural transformation at the atomistic scale. Such an understanding is crucial to evaluate and pin-point how such instabilities are induced relative to external perturbations such as illumination or electrical bias with time, allowing researchers to devise effective strategies to mitigate them. Here, we designed an in-situ TEM setup to enable real-time observation of amorphization in double cation mixed perovskite materials under electrical biasing at 1 V. It is found that amorphization occurs along the (001) and (002) planes, which represents the observation of in-situ facet-dependent amorphization of a perovskite crystal. To reverse the degradation, the samples were heated at 50 oC and was found to recrystallize, effectively regaining its performance losses. This work is vital toward understanding fundamental ion-migration phenomena and address instability challenges of perovskite optoelectronics.

For decades of catalysis research, the d-band center theory that correlates the d-band center and the adsorbate binding energy has successfully enabled the accelerated discovery of novel catalyst materials. Recent studies indicate that, on top of the d-band center value, the full consideration of the d-band shapes describing higher moments of the d-band as well as sp-band properties can help better capturing surface reactivity. However, the density-of-states (DOS) patterns themselves have never been used as a descriptor in combined computational-experimental studies. Here, we propose the full DOS patterns as a key descriptor in high-throughput screening protocols, and prove its effectiveness. For the hydrogen peroxide (H2O2) synthesis as our demo catalytic reaction, the present study focuses on discovering bimetallic catalysts that can replace the prototypic palladium (Pd) one. Through a series of screening processes based on DOS pattern similarities (evaluated using first-principles calculations) and synthetic feasibility, 9 candidates are finally proposed out of 4,350 bimetallic alloy, which then are expected to have a catalytic performance comparable to that of Pd. The subsequent experimental tests demonstrate that 4 bimetallic catalysts (Ni61Pt39, Au51Pd49, Pt52Pd48, Pd52Ni48) indeed exhibit the catalytic properties comparable to those of Pd. Moreover, we discovered a novel bimetallic (Ni-Pt) catalyst, which has not yet been reported for H2O2 direct synthesis. In particular, Ni61Pt39 outperforms the prototypical Pd catalyst for the chemical reaction and exhibits a 9.5-fold enhancement in cost-normalized productivity. This protocol provides a new opportunity for the catalyst discovery for the replacement or reduction in use of the platinum-group metals.

Nonelectrochemical hydrogen peroxide direct synthesis (HPDS) under ambient conditions is an environmentally benign and energy-efficient process that produces a green oxidizer. Despite its industrial importance, the reaction mechanism of HPDS is still controversial, even for the prototypical catalyst Pd. Density functional theory (DFT) calculations with a comprehensive consideration of entropic and solvation effects reveal that the conventionally accepted Langmuir-Hinshelwood mechanism fails to explain why H2O2 production dominates over H2O production, which was experimentally reported. Inspired by the recently suggested heterolytic mechanism that involves electron and proton transfer at Pd catalysts, we propose a new electrochemical DFT model that is applicable for nonelectrochemical systems where a protonation intrinsically occurs. Our model is based on combining the Butler-Volmer equation and constant potential DFT with hybrid explicit-implicit solvent treatments. Application of this model to Pd(111) surfaces produces accurate descriptions of the activation barriers of both H2O2 and H2O production (within only ~0.1 eV of experimentally measured values). The heterolytic mechanism has a lower barrier for the protonation steps for H2O2 production than the nonelectrochemical hydrogenation steps, leading to advantageous kinetics for H2O2 production over H2O production. This work is the first theoretical and computational study supporting the heterolytic H2O2 production mechanism, and it resolves the unanswered discrepancies between previous experimental and DFT results. We expect that these results will readily help the systematic development of improved catalysts for H2O2 synthesis.

The solid electrolyte interphase (SEI) is regarded as the most complex but the least understood constituent in secondary batteries using liquid and solid electrolytes. The nanostructures of SEIs were recently reported to be equally important to the chemistry of SEIs for stabilizing Li metal in liquid electrolyte. However, the dearth of such knowledge in all-solid-state battery (ASSB) has hindered a complete understanding of how certain solid-state electrolytes, such as LiPON, manifest exemplary stability against Li metal. Characterizing such solid-solid interfaces is difficult due to the buried, highly reactive, and beam-sensitive nature of the constituents within. By employing cryogenic electron microscopy (cryo-EM), the interphase between Li metal and LiPON is successfully preserved and probed, revealing a multilayer mosaic SEI structure with concentration gradients of nitrogen and phosphorous, materializing as crystallites within an amorphous matrix. This unique SEI nanostructure is less than 80 nm and is shown stable and free of any organic lithium containing species or lithium fluoride components, in contrast to SEIs often found in state-of-the-art organic liquid electrolytes. Our findings reveal insights on the nanostructures and chemistry of such SEIs as a key component in lithium metal batteries to stabilize Li metal anode.

Despite soaring performance of organic-inorganic hybrid perovskite materials in recent years, the mechanism of their decomposition at actual operation condition has been elusive. Herein, we elucidated the decrystallization process of CH3NH3PbI3 perovskite crystals via localized charges and identified polarity-dependent degradation pathway by carrying out time-evolution measurements for absorption spectra of perovskite films with underlying different charge transport layers and ab initio molecular dynamics calculations. It was found that the carrier polarity (hole-rich or electron-rich) inside the perovskite films played a critical role in the degradation rate, and polarity-dependent degradation pathway strongly depended on the combination of surrounding gaseous molecules. The hole-rich perovskite films degraded more rapidly in the existence of H2O than the electron-rich one, while the degradation trend became opposite in only-oxygen ambient. Strikingly, the hole-rich one was extremely weak to atmospheric air containing both H2O and O2, whereas the MAPbI3 film with excessive electrons rather stabilized in air. Ab initio molecular dynamics (AIMD) simulation was also done to find the detailed degradation pathway of MAPbI3 under atmosphere for different polarity of localized charge, which are in good agreement with experimental results. Furthermore, X-ray assisted spectroscopic measurements confirmed the production of Pb(OH)I as predicted from the simulation result.

DFT is used throughout nanoscience, especially when modeling spin-dependent properties that are important in spintronics. But standard quantum chemical methods (both CCSD(T) and self-consistent semilocal density functional calculations) fail badly for the spin adiabatic energy difference in Fe(II) spin-crossover complexes. We show that all-electron fixed-node diffusion Monte Carlo can be converged at significant computational cost, and that the B3LYP single-determinant has sufficiently accurate nodes, providing benchmarks for these systems. We also find that density-corrected DFT, using Hartree-Fock densities (HF-DFT), greatly improves accuracy and reduces dependence on approximations for these calculations. The small gap in the self-consistent DFT calculations for the high-spin state is consistent with this. For the spin adiabatic energy differences in these complexes, HF-DFT is both accurate and reliable, and we make a strong prediction for the Fe-Porphyrin complex. The "parameter-dilemma" of needing different amounts of mixing for different properties is eliminated by HF-DFT., Comment: 37 pages