Facet-Dependent Oxygen Evolution Reaction Activity of IrO2 from Quantum Mechanics and Experiments.

The diversity of chemical environments present on unique crystallographic facets can drive dramatic differences in catalytic activity and the reaction mechanism. By coupling experimental investigations of five different IrO2 facets and theory, we characterize the detailed elemental steps of the surface redox processes and the rate-limiting processes for the oxygen evolution reaction (OER). The predicted complex evolution of surface adsorbates and the associated charge transfer as a function of applied potential matches well with the distinct redox features observed experimentally for the five facets. Our microkinetic model from grand canonical quantum mechanics (GC-QM) calculations demonstrates mechanistic differences between nucleophilic attack and O-O coupling across facets, providing the rates as a function of applied potential. These GC-QM calculations explain the higher OER activity observed on the (100), (001), and (110) facets and the lower activity observed for the (101) and (111) facets. This combined study with theory and experiment brings new insights into the structural features that either promote or hinder the OER activity of IrO2, which are expected to provide parallels in structural effects on other oxide surfaces.

Electrolyte Dependence of Li+ Transport Mechanisms in Small Molecule Solvents from Classical Molecular Dynamics.

As demands on Li-ion battery performance increase, the need for electrolytes with high ionic conductivity and a high Li+ transference number (tLi) becomes crucial to boost power density. Unfortunately, tLi in liquid electrolytes is typically <0.5 due to Li+ migrating via a vehicular mechanism, whereby Li+ diffuses along with its solvation shell, making its diffusivity slower than the counteranion. Designing liquid electrolytes where the Li+ ion diffuses independently of its solvation shell is of significant interest to enhance the transference number. In this work, we elucidate how the properties of the solvent influence the Li+ transport mechanism. Using classical molecular dynamics simulations, we find that a vehicular mechanism can be increasingly preferred with a decreasing solvent viscosity and increasing interaction energy between the solvent and Li+. Thus, a weaker interaction energy can enhance tLi through a solvent-exchange mechanism, ultimately improving Li-ion battery performance. Finally, metadynamics simulations show that in electrolytes where a solvent-exchange mechanism is preferable, the energy barrier to changing the coordination environment of Li+ is much lower than in electrolytes where a vehicular mechanism dominates.

A cross-scale framework for evaluating flexibility values of battery and fuel cell electric vehicles.

Flexibility has become increasingly important considering the intermittency of variable renewable energy in low-carbon energy systems. Electrified transportation exhibits great potential to provide flexibility. This article analyzed and compared the flexibility values of battery electric vehicles and fuel cell electric vehicles for planning and operating interdependent electricity and hydrogen supply chains while considering battery degradation costs. A cross-scale framework involving both macro-level and micro-level models was proposed to compute the profits of flexible EV refueling/charging with battery degradation considered. Here we show that the flexibility reduction after considering battery degradation is quantified by at least 4.7% of the minimum system cost and enlarged under fast charging and low-temperature scenarios. Our findings imply that energy policies and relevant management technologies are crucial to shaping the comparative flexibility advantage of the two transportation electrification pathways. The proposed cross-scale methodology has broad implications for the assessment of emerging energy technologies with complex dynamics.

Examining graph neural networks for crystal structures: Limitations and opportunities for capturing periodicity.

Graph neural networks (GNNs) have recently been used to learn the representations of crystal structures through an end-to-end data-driven approach. However, a systematic top-down approach to evaluate and understand the limitations of GNNs in accurately capturing crystal structures has yet to be established. In this study, we introduce an approach using human-designed descriptors as a compendium of human knowledge to investigate the extent to which GNNs can comprehend crystal structures. Our findings reveal that current state-of-the-art GNNs fall short in accurately capturing the periodicity of crystal structures. We analyze this failure by exploring three aspects: local expressive power, long-range information processing, and readout function. To address these identified limitations, we propose a straightforward and general solution: the hybridization of descriptors with GNNs, which directly supplements the missing information to GNNs. The hybridization enhances the predictive accuracy of GNNs for specific material properties, most notably phonon internal energy and heat capacity, which heavily rely on the periodicity of materials.

Safe and Scalable Syntheses of N,N-Dimethyltrifluoromethanesulfonamide (DMTMSA) and Other Trifluoromethanesulfonamide Solvents for High Energy Density Battery Applications.

A simple, scalable synthetic methodology for the synthesis of N,N-dimethyltrifluoromethanesulfonamide (DMTMSA) and other trifluoromethanesulfonamide solvents is described. No specialized glassware is required, water is the solvent, and an ice bath is used for cooling. Up to 155 g of DMTMSA is synthesized in a single batch in 92% yield. The optimized process is highly mass efficient (PMI = 9.1), and excess dimethylamine may be recovered (93% recovery, 51% decrease in waste) and recycled via a simple short-path distillation.

Sequencing polymers to enable solid-state lithium batteries.

Rational designs of solid polymer electrolytes with high ion conduction are critical in enabling the creation of advanced lithium batteries. However, known polymer electrolytes have much lower ionic conductivity than liquid/ceramics at room temperature, which limits their practical use in batteries. Here we show that precise positioning of designed repeating units in alternating polymer sequences lays the foundation for homogenized Li+ distribution, non-aggregated Li+-anion solvation and sequence-assisted site-to-site ion migration, facilitating the tuning of Li+ conductivity by up to three orders of magnitude. The assembled all-solid-state batteries facilitate reversible and dendrite-mitigated cycling against Li metal from ambient to elevated temperatures. This work demonstrates a powerful molecular engineering means to access highly ion-conductive solid-state materials for next-generation energy devices.

Lamellar Ionenes with Highly Dissociative, Anionic Channels Provide Lower Barriers for Cation Transport.

Solid polymer electrolytes have the potential to enable safer and more energy-dense batteries; however, a deeper understanding of their ion conduction mechanisms, and how they can be optimized by molecular design, is needed to realize this goal. Here, we investigate the impact of anion dissociation energy on ion conduction in solid polymer electrolytes via a novel class of ionenes prepared using acyclic diene metathesis (ADMET) polymerization of highly dissociative, liquid crystalline fluorinated aryl sulfonimide-tagged ("FAST") anion monomers. These ionenes with various cations (Li+, Na+, K+, and Cs+) form well-ordered lamellae that are thermally stable up to 180 °C and feature domain spacings that correlate with cation size, providing channels lined with dissociative FAST anions. Electrochemical impedance spectroscopy (EIS) and differential scanning calorimetry (DSC) experiments, along with nudged elastic band (NEB) calculations, suggest that cation motion in these materials operates via an ion-hopping mechanism. The activation energy for Li+ conduction is 59 kJ/mol, which is among the lowest for systems that are proposed to operate via an ion conduction mechanism that is decoupled from polymer segmental motion. Moreover, the addition of a cation-coordinating solvent to these materials led to a >1000-fold increase in ionic conductivity without detectable disruption of the lamellar structure, suggesting selective solvation of the lamellar ion channels. This work demonstrates that molecular design can facilitate controlled formation of dissociative anionic channels that translate to significant enhancements in ion conduction in solid polymer electrolytes.

Carbon-cement supercapacitors as a scalable bulk energy storage solution.

The large-scale implementation of renewable energy systems necessitates the development of energy storage solutions to effectively manage imbalances between energy supply and demand. Herein, we investigate such a scalable material solution for energy storage in supercapacitors constructed from readily available material precursors that can be locally sourced from virtually anywhere on the planet, namely cement, water, and carbon black. We characterize our carbon-cement electrodes by combining correlative EDS-Raman spectroscopy with capacitance measurements derived from cyclic voltammetry and galvanostatic charge-discharge experiments using integer and fractional derivatives to correct for rate and current intensity effects. Texture analysis reveals that the hydration reactions of cement in the presence of carbon generate a fractal-like electron-conducting carbon network that permeates the load-bearing cement-based matrix. The energy storage capacity of this space-filling carbon black network of the high specific surface area accessible to charge storage is shown to be an intensive quantity, whereas the high-rate capability of the carbon-cement electrodes exhibits self-similarity due to the hydration porosity available for charge transport. This intensive and self-similar nature of energy storage and rate capability represents an opportunity for mass scaling from electrode to structural scales. The availability, versatility, and scalability of these carbon-cement supercapacitors opens a horizon for the design of multifunctional structures that leverage high energy storage capacity, high-rate charge/discharge capabilities, and structural strength for sustainable residential and industrial applications ranging from energy autarkic shelters and self-charging roads for electric vehicles, to intermittent energy storage for wind turbines and tidal power stations.

Influence of Sr-Site Deficiency, Ca/Ba/La Doping on the Exsolution of Ni from SrTiO3.

Cermet catalysts formed via exsolution of metal nanoparticles from perovskites promise to perform better in electro- and thermochemical applications than those synthesized by conventional wet-chemical approaches. However, a shortage of robust material design principles still stands in the way of widespread commercial adoption of exsolution. Working with Ni-doped SrTiO3 solid solutions, we investigated how the introduction of Sr deficiency as well as Ca, Ba, and La doping on the Sr site changed the size and surface density of exsolved Ni nanoparticles. We carried out exsolution on 11 different compositions under fixed conditions. We elucidated the effect of A-site defect size/valence on nanoparticle density and size as well as the effect of composition on nanoparticle immersion and ceramic microstructure. Based on our experimental results, we developed a model that quantitatively predicted a composition's exsolution properties using density functional theory calculations. The model and calculations provide insights into the exsolution mechanism and can be used to find new compositions with high exsolution nanoparticle density.

Chemistry-Informed Machine Learning for Polymer Electrolyte Discovery.

Solid polymer electrolytes (SPEs) have the potential to improve lithium-ion batteries by enhancing safety and enabling higher energy densities. However, SPEs suffer from significantly lower ionic conductivity than liquid and solid ceramic electrolytes, limiting their adoption in functional batteries. To facilitate more rapid discovery of high ionic conductivity SPEs, we developed a chemistry-informed machine learning model that accurately predicts ionic conductivity of SPEs. The model was trained on SPE ionic conductivity data from hundreds of experimental publications. Our chemistry-informed model encodes the Arrhenius equation, which describes temperature activated processes, into the readout layer of a state-of-the-art message passing neural network and has significantly improved accuracy over models that do not encode temperature dependence. Chemically informed readout layers are compatible with deep learning for other property prediction tasks and are especially useful where limited training data are available. Using the trained model, ionic conductivity values were predicted for several thousand candidate SPE formulations, allowing us to identify promising candidate SPEs. We also generated predictions for several different anions in poly(ethylene oxide) and poly(trimethylene carbonate), demonstrating the utility of our model in identifying descriptors for SPE ionic conductivity.

Calibrating DFT Formation Enthalpy Calculations by Multifidelity Machine Learning.

The application of machine learning to predict materials properties measured by experiments are valuable yet difficult due to the limited amount of experimental data. In this work, we use a multifidelity random forest model to learn the experimental formation enthalpy of materials with prediction accuracy higher than the Perdew-Burke-Ernzerhof (PBE) functional with linear correction, PBEsol, and meta-generalized gradient approximation (meta-GGA) functionals (SCAN and r2SCAN), and it outperforms the hotly studied deep neural network-based representation learning and transfer learning. We then use the model to calibrate the DFT formation enthalpy in the Materials Project database and discover materials with underestimated stability. The multifidelity model is also used as a data-mining approach to find how DFT deviates from experiments by explaining the model output.

The low overpotential regime of acidic water oxidation part II: trends in metal and oxygen stability numbers.

The operating conditions of low pH and high potential at the anodes of polymer electrolyte membrane electrolysers restrict the choice of catalysts for the oxygen evolution reaction (OER) to oxides based on the rare metals iridium or ruthenium. In this work, we investigate the stability of both the metal atoms and, by quantitative and highly sensitive 18O isotope labelling experiments, the oxygen atoms in a series of RuO x and IrO x electrocatalysts during the OER in the mechanistically interesting low overpotential regime. We show that materials based on RuO x have a higher dissolution rate than the rate of incorporation of labelled oxygen from the catalyst into the O2 evolved ("labelled OER"), while for IrO x -based catalysts the two rates are comparable. On amorphous RuO x , metal dissolution and labelled OER are found to have distinct Tafel slopes. These observations together lead us to a full mechanistic picture in which dissolution and labelled OER are side processes to the main electrocatalytic cycle. We emphasize the importance of quantitative analysis and point out that since less than 0.2% of evolved oxygen contains an oxygen atom originating from the catalyst itself, lattice oxygen evolution is at most a negligible contribution to overall OER activity for RuO x and IrO x in acidic electrolyte.

The low overpotential regime of acidic water oxidation part I: the importance of O2 detection.

The high overpotential required for the oxygen evolution reaction (OER) represents a significant barrier for the production of closed-cycle renewable fuels and chemicals. Ruthenium dioxide is among the most active catalysts for OER in acid, but the activity at low overpotentials can be difficult to measure due to high capacitance. In this work, we use electrochemistry - mass spectrometry to obtain accurate OER activity measurements spanning six orders of magnitude on a model series of ruthenium-based catalysts in acidic electrolyte, quantifying electrocatalytic O2 production at potential as low as 1.30 VRHE. We show that the potential-dependent O2 production rate, i.e., the Tafel slope, exhibits three regimes, revealing a previously unobserved Tafel slope of 25 mV decade-1 below 1.4 VRHE. We fit the expanded activity data to a microkinetic model based on potential-dependent coverage of the surface intermediates from which the rate-determining step takes place. Our results demonstrate how the familiar quantities "onset potential" and "exchange current density" are influenced by the sensitivity of the detection method.

Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties.

Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology. Large scale screening of polymer electrolytes is hindered by the significant cost of molecular dynamics (MD) simulation in amorphous systems: the amorphous structure of polymers requires multiple, repeated sampling to reduce noise and the slow relaxation requires long simulation time for convergence. Here, we accelerate the screening with a multi-task graph neural network that learns from a large amount of noisy, unconverged, short MD data and a small number of converged, long MD data. We achieve accurate predictions of 4 different converged properties and screen a space of 6247 polymers that is orders of magnitude larger than previous computational studies. Further, we extract several design principles for polymer electrolytes and provide an open dataset for the community. Our approach could be applicable to a broad class of material discovery problems that involve the simulation of complex, amorphous materials.

Water electrolysis: from textbook knowledge to the latest scientific strategies and industrial developments.

Replacing fossil fuels with energy sources and carriers that are sustainable, environmentally benign, and affordable is amongst the most pressing challenges for future socio-economic development. To that goal, hydrogen is presumed to be the most promising energy carrier. Electrocatalytic water splitting, if driven by green electricity, would provide hydrogen with minimal CO2 footprint. The viability of water electrolysis still hinges on the availability of durable earth-abundant electrocatalyst materials and the overall process efficiency. This review spans from the fundamentals of electrocatalytically initiated water splitting to the very latest scientific findings from university and institutional research, also covering specifications and special features of the current industrial processes and those processes currently being tested in large-scale applications. Recently developed strategies are described for the optimisation and discovery of active and durable materials for electrodes that ever-increasingly harness first-principles calculations and machine learning. In addition, a technoeconomic analysis of water electrolysis is included that allows an assessment of the extent to which a large-scale implementation of water splitting can help to combat climate change. This review article is intended to cross-pollinate and strengthen efforts from fundamental understanding to technical implementation and to improve the 'junctions' between the field's physical chemists, materials scientists and engineers, as well as stimulate much-needed exchange among these groups on challenges encountered in the different domains.

Surface Oxygen Vacancies Confined by Ferroelectric Polarization for Tunable CO Oxidation Kinetics.

Surface oxygen vacancies have been widely discussed to be crucial for tailoring the activity of various chemical reactions from CO, NO, to water oxidation by using oxide-supported catalysts. However, the real role and potential function of surface oxygen vacancies in the reaction remains unclear because of their very short lifetime. Here, it is reported that surface oxygen vacancies can be well confined electrostatically for a polarization screening near the perimeter interface between Pt {111} nanocrystals and the negative polar surface (001) of ferroelectric PbTiO3. Strikingly, such a catalyst demonstrates a tunable catalytic CO oxidation kinetics from 200 °C to near room temperature by increasing the O2 gas pressure, accompanied by the conversion curve from a hysteresis-free loop to one with hysteresis. The combination of reaction kinetics, electronic energy loss spectroscopy (EELS) analysis, and density functional theory (DFT) calculations, indicates that the oxygen vacancies stabilized by the negative polar surface are the active sites for O2 adsorption as a rate-determining step, and then dissociated O moves to the surface of the Pt nanocrystals for oxidizing adsorbed CO. The results open a new pathway for tunable catalytic activity of CO oxidation.

Tunable metal hydroxide-organic frameworks for catalysing oxygen evolution.

The oxygen evolution reaction is central to making chemicals and energy carriers using electrons. Combining the great tunability of enzymatic systems with known oxide-based catalysts can create breakthrough opportunities to achieve both high activity and stability. Here we report a series of metal hydroxide-organic frameworks (MHOFs) synthesized by transforming layered hydroxides into two-dimensional sheets crosslinked using aromatic carboxylate linkers. MHOFs act as a tunable catalytic platform for the oxygen evolution reaction, where the π-π interactions between adjacent stacked linkers dictate stability, while the nature of transition metals in the hydroxides modulates catalytic activity. Substituting Ni-based MHOFs with acidic cations or electron-withdrawing linkers enhances oxygen evolution reaction activity by over three orders of magnitude per metal site, with Fe substitution achieving a mass activity of 80 A [Formula: see text] at 0.3 V overpotential for 20 h. Density functional theory calculations correlate the enhanced oxygen evolution reaction activity with the MHOF-based modulation of Ni redox and the optimized binding of oxygenated intermediates.

Switchable wetting of oxygen-evolving oxide catalysts.

The surface wettability of catalysts is typically controlled via surface treatments that promote catalytic performance. Here we report on potential-regulated hydrophobicity/hydrophilicity at cobalt-based oxide interfaces with an alkaline solution. The switchable wetting of single particles, directly related to their activity and stability towards the oxygen evolution reaction, was revealed by electrochemical liquid-phase transmission electron microscopy. Analysis of the movement of the liquid in real time revealed distinctive wettability behaviour associated with specific potential ranges. At low potentials, an overall reduction of the hydrophobicity of the oxides was probed. Upon reversible reconstruction towards the surface oxyhydroxide phase, electrowetting was found to cause a change in the interfacial capacitance. At high potentials, the evolution of molecular oxygen, confirmed by operando electron energy-loss spectroscopy, was accompanied by a globally thinner liquid layer. This work directly links the physical wetting with the chemical oxygen evolution reaction of single particles, providing fundamental insights into solid-liquid interfacial interactions of oxygen-evolving oxides.

Electronic Structure-Based Descriptors for Oxide Properties and Functions.

ConspectusThe transition from fossil fuels to renewable energy requires the development of efficient and cost-effective energy storage technologies. A promising way forward is to harness the energy of intermittent renewable sources, such as solar and wind, to perform (electro)catalytic reactions to generate fuels, thus storing energy in the form of chemical bonds. However, current catalysts rely on the use of expensive, rare, or geographically localized elements, such as platinum. Widespread adoption of new (electro)catalytic technologies hinges on the discovery and development of materials containing earth-abundant elements, which can efficiently catalyze an array of (electro)chemical reactions.In the context of catalysis, descriptors provide correlations between fundamental physical properties, such as the electronic structure, and the resulting catalytic activity. The use of easily accessible descriptors has proven to be a powerful method to advance and accelerate discovery and design of new catalyst materials. The position of the oxygen electronic 2p band center has been proposed to capture the basic physical properties of oxides, including oxygen vacancy formation energy, diffusion barrier of oxygen ions, and work function. Moreover, the adsorption strength of relevant reaction intermediates at the surface of oxides can be strongly correlated with the energy of the oxygen 2p states, which affects the catalytic activity of reactions, such as oxygen electrocatalysis, and oxidative dehydrogenation of organic molecules. Such descriptors for catalytic activity can be used to predict the activity of new catalysts and understand trends and behavior among different catalysts.In this Account, we discuss how the energy of the oxygen 2p states can be used as a descriptor for oxide bulk and surface chemical properties. We show how the oxide redox properties vary linearly with the position of the oxygen 2p band center with respect to the Fermi level, and we discuss how this descriptor can be expanded across different materials and structural families, including possible generalizations to compounds outside oxides. We highlight the power of the oxygen 2p band center to predict the catalytic activity of oxides. We conclude with an outlook examining under which conditions this descriptor can be applied to predict oxide properties and possible opportunities for further refining and accelerating property predictions of oxides by leveraging material databases and machine learning.