Solar thermochemical hydrogen is one of the few potential routes towards direct fuel production from renewable energy sources, but the thermodynamic boundary conditions for efficient and economic energy conversion are challenging. Success or failure of a given oxide working material depends on the subtle balance between enthalpy and entropy contributions in the redox processes. Developing a mechanistic understanding of the behavior of materials on the basis of atomistic models and first-principles calculations is an important part of advancing the technology. One challenge is to quantitatively predict thermochemical equilibria at high concentrations when the redox-active defects start to interact with each other, thereby impeding the formation of additional defects. This problem is of more general importance to applications that rely on high levels of off-stoichiometry or doping, including, for example, batteries, thermoelectrics, and ceramic fuel cells. To account for such repulsive defect interactions, we introduce a statistical mechanics approach, defining an expression for the free energy of defect interaction based on limited sampling of defect configurations in density functional theory supercell calculations. The parameterization of this energy contribution as a function of defect concentration and temperature allows on-the-fly simulation of thermochemical equilibria. The approach consistently incorporates finite temperature effects by including the leading contributions to the temperature-dependent free energy for the case at hand, i.e., the ideal gas and configurational enthalpies and entropies. We demonstrate the capability and utility of the approach by simulating the water splitting redox processes for ${\mathrm{Sr}}_{1-x}{\mathrm{Ce}}_x{\mathrm{Mn}\mathrm{O}}_{3-\delta }$ alloys.

Oxides containing metals or metalloids from the $p$ block of the periodic table (e.g., $\mathrm{In}$, $\mathrm{Sn}$, $\mathrm{Sb}$, $\mathrm{Pb}$, and $\mathrm{Bi}$) are of technological interest as transparent conductors and light absorbers for solar-energy conversion due to the tunability of their electronic conductivity and optical absorption. Comparatively, these oxides have found limited applications in hydrogen photoelectrolysis, primarily due to their high electronegativity, which impedes electron transfer for reducing protons into hydrogen. We have shown recently that inserting $s$-block cations into $p$-block metal oxides is effective at lowering electronegativities while affording further control of band gaps. Here, we explain the origins of this dual tunability by demonstrating the mediator role of $s$-block cations in modulating orbital hybridization while not contributing to frontier electronic states. From this result, we carry out a comprehensive computational study of 109 ternary oxides of $s$- and $p$-block metal elements as candidate photocatalysts for solar hydrogen generation. We down-select the most desirable materials using band gaps and band edges obtained from Hubbard-corrected density-functional theory, with Hubbard parameters computed entirely from first principles, evaluate the stability of these oxides in aqueous conditions, and characterize experimentally four of the remaining materials, synthesized with high phase uniformity, to validate and further develop the computational models. We thus propose nine oxide semiconductors, including $\mathrm{Cs}\mathrm{In}$${}_3$$\mathrm{O}$${}_5$, $\mathrm{Sr}$${}_2$$\mathrm{In}$${}_2$$\mathrm{O}$${}_5$, and $\mathrm{KSbO}$${}_2$, which, to the extent of our literature review, have not been previously considered as water-splitting photocatalysts.

Grain boundaries play an important role in the efficiency of thin-film photovoltaics, where the absorber layer is invariably polycrystalline. Density-functional-theory simulations have previously identified a “self-healing” mechanism in ${\mathrm{Sb}}_2{\mathrm{Se}}_3$ that passivates the grain boundaries. During “self-healing,” extensive structural relaxation at the grain boundary removes the band-gap electronic defect states that give rise to high carrier recombination rates. In this work, lattice imaging in a transmission electron microscope is used to uncover evidence for the theoretically proposed structural relaxation in ${\mathrm{Sb}}_2{\mathrm{Se}}_3$. The strain measured along the [010] crystal direction is found to be dependent on the nature of the grain-boundary plane. For a (010) grain boundary, the strain and structural relaxation is minimal, since no covalent bonds are broken by termination of the grain. On the other hand, strains of up to approximately 4% extending approximately 2 nm into the grain interior are observed for a (041) grain boundary, where grain termination results in significant structural relaxation due to the ideal atomic coordination being disrupted. These results are consistent with theory and suggest that ${\mathrm{Sb}}_2{\mathrm{Se}}_3$ may have a high level of grain-boundary-defect tolerance.

The energy transition introduces new classes of dynamical actors into the power grid. There is, in particular, a growing need for so-called grid-forming inverters (GFIs) that can contribute to dynamic grid stability as the share of synchronous generators decreases. Understanding the collective behavior and stability of future grids, featuring a heterogeneous mix of dynamics, remains an urgent and challenging task. Two recent advances in describing such modern power grid dynamics have made this problem more tractable. First, the normal form for grid-forming actors provides a uniform technology-neutral description of plausible grid dynamics, including grid-forming inverters and synchronous machines. Second, the notion of the complex frequency has been introduced to effortlessly describe how the nodal dynamics influence the power flows in the grid. The major contribution of this paper is to show how the normal-form approach and the complex-frequency dynamics of power grids combine and how they relate naturally to adaptive dynamical networks and control-affine systems. Using the normal form and the complex frequency, we derive a remarkably elementary and universal equation for the collective grid dynamics. Notably, we obtain an elegant equation entirely in terms of a matrix of complex couplings, in which the network topology does not appear explicitly. These complex couplings give rise to new adaptive network formulations of future power grid dynamics. We give a new formulation of the Kuramoto model, with inertia as a special case. Starting from this formulation of the grid dynamics, the question of the optimal design of future grid-forming actors becomes treatable by methods from affine and bilinear control theory. We demonstrate the power of this perspective by deriving a quasilocal control dynamics that can stabilize arbitrary power flows, even if the effective network Laplacian is not positive definite.

Layered nickel-rich lithium transition-metal oxides (${\mathrm{Li}\mathrm{Ni}}_x{\mathrm{Mn}}_y{\mathrm{Co}}_{1-x-y}{\mathrm{O}}_2$; where x ≥ 0.8), with single-crystalline morphology, are promising future high-energy-density $\mathrm{Li}$-ion battery cathodes due to their ability to mitigate particle-cracking-induced degradation. This is due to the absence of grain boundaries in these materials, which prevents the build-up of bulk crystallographic strain during electrochemical cycling. Compared to their polycrystalline counterparts, there is a need to study single-crystalline $\mathrm{Ni}$-rich cathodes using operando x-ray methods in uncompromised machine-manufactured industrylike full cells to understand their bulk degradation mechanisms as a function of different electrochemical cycling protocols. This can help us identify factors to improve their long-term performance. Here, through in-house operando x-ray studies of pilot-line-built ${\mathrm{Li}\mathrm{Ni}}_{0.8}{\mathrm{Mn}}_{0.1}{\mathrm{Co}}_{0.1}{\mathrm{O}}_2$–graphite A7 pouch cells, it is shown that their electrochemical-capacity fade under harsh conditions (2.5–4.4 V and 40 °C for 100 cycles at a C/3 rate) primarily stems from the high-voltage reconstruction of the cathode surface from a layered to a cubic (rock-salt) phase that impedes the ${\mathrm{Li}}^{+}$ kinetics and increases cell impedance. Postmortem electron and x-ray microscopy show that these cathodes can withstand severe anisotropic structural changes and show no cracking when cycled under such conditions. Comparing these results to those from commercial $\mathrm{Li}$-ion cells with surface-modified single-crystalline $\mathrm{Ni}$-rich cathodes, it is identified that cathode surface passivation can mitigate this type of degradation and prolong cycle life. In addition to furthering our understanding of degradation in single-crystalline $\mathrm{Ni}$-rich cathodes, this work also accentuates the need for practically relevant and reproducible fundamental investigations of $\mathrm{Li}$-ion cells and presents a methodology for achieving this.

Metal hydrides are potential candidates for applications in hydrogen-related technologies, such as energy storage, hydrogen compression, and hydrogen sensing, to name just a few. However, understanding the electronic structure and chemical environment of hydrogen within them remains a key challenge. This work presents a new analytical pathway to explore these aspects in technologically relevant systems using hard x-ray photoelectron spectroscopy (HAXPES) on thin films of two prototypical metal dihydrides: $\mathrm{YH}$${}_{2-\delta }$ and $\mathrm{Ti}\mathrm{H}$${}_{2-\delta }$. By taking advantage of the tunability of synchrotron radiation, a nondestructive depth profile of the chemical states is obtained using core-level spectra. Combining experimental valence-band (VB) spectra collected at varying photon energies with theoretical insights from density functional theory (DFT) calculations, a description of the bonding nature and the role of $d$ versus $sp$ contributions to states near the Fermi energy are provided. Moreover, a reliable determination of the enthalpy of formation is proposed by using experimental values of the energy position of metal $s$-band features close to the Fermi energy in the HAXPES VB spectra.

The long-running discussion on the global theoretical and practical potential of renewable energy is strongly affected by the size and connectivity of power networks. Small regional and national networks driven predominantly by intermittent and variable energy sources will require significantly more back-up power than continental networks containing a variety of weather zones. Thus, large networks will require less back-up but this comes with the drawback that some nations or regions rely on import from others. For example, in Europe in 2022, energy dependence on gas imports from Russia led to energy shortages and escalating prices. In this work, we estimate the limits of primary energy supply based on renewables, mainly solar photovoltaic (PV) sources and wind, in this setting. Taking as a starting point an average of 14 European countries, we find the mean energy consumption per unit time in what we argue is sufficient and necessary power per capita to maintain a sustainable life. The required energy generation is then translated into an area of energy production that, together with estimated area requirements for biological diversity, food, and infrastructure, constitutes a given fraction of the land area. When compared with typical human population densities, we find that before reaching one person per hectare, the area requirements exceed what is available. The results support the part of the scientific community that claims that a 100% renewable global power supply is unrealistic, especially as the world population steadily grows toward $10\times {10}^9$ people.

Hysteresis observed in the current-voltage curves of both electronic and ionic devices is a phenomenon where the curve's shape is altered on the basis of the measurement speed. This effect is driven by internal processes that introduce a time delay in the response to an external stimulus, leading to measurements being dependent on the history of the past disturbances. This hysteresis effect has posed challenges, particularly in solution-processed photovoltaic devices such as halide perovskite solar cells, where it significantly complicates the evaluation of performance quality. In other devices, such as memristors and organic electrochemical transistors for neuromorphic applications, hysteresis is an inherent aspect of their functionality, facilitating transitions between different conductivity states. Natural and artificial ionically conducting channels also exhibit pronounced hysteresis, a crucial component for generating action potentials in neurons. In this study, we aim to categorize various forms of hysteresis by identifying shared elements among diverse physical, chemical, and biological conducting systems. Our method involves examining hysteresis from multiple angles, using simplified models that capture essential response types. We analyze system behavior using techniques such as linear sweep voltammetry and impedance spectroscopy and transient currents resulting from small voltage steps. Our investigation reveals two primary hysteresis types based on how current responds to rapid sweep rates: capacitive hysteresis and inductive hysteresis. These terms correspond to the dominant component in the equivalent circuit, determining the transient time response. Remarkably, these concepts provide insights into vastly different systems, spanning solar cells, capacitors, transistors, electrofluidic nanopores, and protein ion channels. The consistency in electrical responses across the different cases enables the identification of the primary cause of hysteresis. We also elucidate the frequency dependence of hysteresis and the stepwise responses of solar cells, illustrating how fundamental relaxations contribute to the overall surplus or deficit of current during extensive voltage sweeps that define the current-voltage curve.

The use of molten salts as coolants, fuels, and tritium breeding blankets in the next generation of fission and fusion nuclear reactors benefits from furthering the characterization of the molecular structure of molten halide salts, paving the way to predictive capability of the chemical and thermophysical properties of molten salts. Due to its neutronic, chemical, and thermochemical properties, 2$\mathrm{Li}\mathrm{F}$-$\mathrm{Be}\mathrm{F}$${}_2$ is a candidate molten salt for several fusion- and fission-reactor designs. We performed neutron and x-ray total-scattering measurements to determine the atomic structure of liquid 2$\mathrm{Li}\mathrm{F}$-$\mathrm{Be}\mathrm{F}$${}_2$. We also performed ab initio and neural-network molecular-dynamics simulations to predict the structure obtained by neutron- and x-ray-diffraction experiments. The use of machine learning provides improvements to the efficiency in predicting the structure at a longer length scales than is achievable with ab initio simulations at significantly lower computational expense while retaining near ab initio accuracy. We found that the NNMD simulations accurately predicted the ${\mathrm{Be}\mathrm{F}}_4^{2-}$ oligomer formations seen in the experimental first-structure-factor peak. Our combination of high-resolution measurements with large-scale molecular dynamics provided an avenue to explore and experimentally verify the intermediate-range ordering beyond the first-nearest neighbor that has posed too many experimental and computational challenges in previous works. With a deeper understanding of the salt structure and ion ordering, the evolution of salt chemistry over the lifetime of a reactor can be better predicted, which is crucial to the licensing and operation of advanced fission and fusion reactors that employ molten salts. To this end, this work will serve as a reference for future studies of salt structure and macroscopic properties with and without the addition of solutes.

The success of all-solid-state batteries (ASSBs) depends on the solid-state electrolyte (SSE) exhibiting high interfacial stability and room-temperature ionic conductivity. However, the current SSEs, especially those with practical ionic conductivities (≥${10}^{-3}$ S/cm) at room temperature, often develop unstable interfaces at the metal anode, in some cases with even greater severity than with liquid organic electrolytes. Despite persistent efforts, achieving interfacial stability and sufficient ionic conductivity simultaneously represents one of the greatest challenges in ASSBs. The current approaches focus on stabilizing the interface by incorporating secondary interlayers or introducing coatings by surface engineering. The method is often material-specific, and the added interlayers often deteriorate during cycling. In this work, using phase analysis and explicit interface modeling, we demonstrate a strategy to kinetically stabilize the interface between the SSE and metal anode by incorporating selected monoanion clusters in the SSE; they can effectively lower or even halt the reduction kinetics at the interface by promoting on-site formation of interphases that are highly electron insulating. The study provides insight into the kinetic effects to achieve SSEs with superior properties in bulk and at the interface.

Structural defects in organic solar cells (OSCs) can significantly impact their performance by altering the density of the trap states and disrupting the energy-level alignment. These defects are primarily formed during the film-formation process, serving as intrinsic traps. In this study, we employed a dilution approach by blending an insulating polymer, polystyrene (PS), to control the structural-evolution process and reduce structural defects. By incorporating PS into two all-polymer systems, namely, PBDB-T:N2200 and PM6:PY-IT, the presence of enhanced stack ordering led to a reduction in structural defects, thus facilitating the charge-transfer process in all-polymer OSCs. However, this phenomenon was not observed in the nonfullerene system PM6:BTP-eC9. The compatibility between various components was examined in solution using both experiments and theoretical simulations, which revealed better miscibility between PS and the polymer materials. Our study demonstrates that the dilution effect of insulating polymers can effectively regulate the structural-evolution process and defects in organic semiconductors, resulting in enhanced performance of organic electronic devices.

Mitigating the migration of mobile ions within perovskite solar cells is a crucial step on the way to improving their stability. In the past, transient capacitance measurements were applied to extract information about mobile ions, including their activation energy, diffusion coefficient, density, and polarity. However, in this work, we show that the interpretation of capacitance transients is more complex than originally proposed because of the intrinsic nature of the perovskite and the contributions of charge transport layers to the capacitance. Using drift-diffusion simulations and light-intensity-dependent capacitance transient measurements, we show that the direction of capacitance transients is not linked to the polarity of the migrating species. Instead, the direction of the transients is linked to the layer of the cell that dominates capacitance modulation. This work illustrates that transport layers can be crucial for the capacitance and impedance response of perovskite solar cells, and therefore, for characterizing mobile ions in perovskites.

An understanding of the role of metal and oxygen in the redox process of layered $3d$ transition-metal oxides is crucial to build high-density and stable next-generation $\mathrm{Li}$-ion batteries. We combine hard-x-ray photoelectron spectroscopy and ab initio–based cluster-model simulations to study the electronic structure of the prototypical end members $\mathrm{Li}\mathrm{Co}\mathrm{O}$${}_2$ and $\mathrm{Co}\mathrm{O}$${}_2$. The role of cobalt and oxygen in the redox process is analyzed by optimizing the values of $d$-$d$ electron repulsion and ligand-metal $p$-$d$ charge transfer to the $\mathrm{Co}$ $2p$ spectra. We clarify the nature of oxidized cobalt ions by highlighting the transition from positive to negative ligand-to-metal charge transfer upon $\mathrm{Li}$${}^{+}$ deintercalation.

Understanding the microscopic spatio-temporal dynamics of nonequilibrium charge carriers in heterosystems promises optimization of process and device design towards desired energy transfer. Hot electron transport is governed by scattering with other electrons, defects, and bosonic excitations. Analysis of the energy dependence of scattering pathways and identification of diffusive, superdiffusive, and ballistic transport regimes are current challenges. Beyond our previous studies on the $\mathrm{Au}$/$\mathrm{Fe}$(001) heterostructure, in this work, we determine the energy-dependent change of the electron propagation time through epitaxial $\mathrm{Au}$/$\mathrm{Fe}$(001) heterostructures as a function of $\mathrm{Au}$ layer thickness. We do so by employing femtosecond time-resolved two-photon photoelectron emission spectroscopy for energies of 0.5–2.0 eV above the Fermi energy. We describe the laser-induced nonequilibrium electron excitation and injection across the $\mathrm{Fe}$/$\mathrm{Au}$ interface using real-time time-dependent density functional theory and analyze electron propagation through the $\mathrm{Au}$ layer by microscopic electron transport simulations. We identify ballistic transport of minority electrons at energies with a nascent optically excited electron population, which is determined by the combination of photon energy and the specific electronic structure of the material. At lower energy, superdiffusive transport with 1–4 scattering events dominates. The effective electron velocity accelerates from 0.3 to 1 nm/fs with an increase in the $\mathrm{Au}$ layer thickness from 10 to 100 nm. This phenomenon is explained by electron transport that becomes preferentially aligned with the interface normal for thicker $\mathrm{Au}$ layers, which facilitates electron momentum or energy selection by choice of the propagation layer thickness.

The particle physics community has agreed that an electron-positron collider is the next step for continued progress in this field, giving a unique opportunity for a detailed study of the Higgs boson. Several proposals are currently under evaluation by the international community. Any large particle accelerator will be an energy consumer and so, today, we must be concerned about its impact on the environment. This paper evaluates the carbon impact of the construction and operation of one of these Higgs factory proposals, the Cool Copper Collider. It introduces several strategies to lower the carbon impact of the accelerator. It proposes a metric to compare the carbon costs of Higgs factories, balancing physics reach, energy needs, and carbon footprint for both construction and operation, and compares the various Higgs factory proposals within this framework. For the Cool Copper Collider, the compact 8 km footprint and the possibility for cut-and-cover construction greatly reduce the dominant contribution from embodied carbon.

The high cost of noble metals is a barrier to widespread commercialization of polymer electrolyte membrane fuel cells. Platinum-group-metal-free catalysts are a promising low-cost alternative for catalyzing the oxygen reduction reaction (ORR). Herein, we report a high activity $\mathrm{Fe}$-$\mathrm{N}$-$\mathrm{C}$ cathode catalyst derived from a $\mathrm{Fe}$-porphyrinic framework prepared using low-cost precursors and facile one pot synthesis followed by a single heat treatment. The final product has atomically dispersed iron in proximity to nitrogen groups that share transition metal characteristics, as described by electron energy loss spectrometry and x-ray absorption near edge structure results. Electrochemical studies on a rotating ring-disk electrode indicate a four-electron transfer mechanism for the ORR. Membrane electrode assembly testing of the $\mathrm{Fe}$-porphyrin-derived cathode catalyst shows a high kinetic current density of 22 mA cm${}^{-2}$ at 0.9 V in ${\mathrm{H}}_2$-${\mathrm{O}}_2$ fuel cells.

This article provides an examination of the long-term potential of antimony selenide (${\mathrm{Sb}}_2{\mathrm{Se}}_3$) as a leading photovoltaic absorber by considering lessons learned from the developmental route of $\mathrm{Cd}\mathrm{Te}$. We consider the inherent advantages of ${\mathrm{Sb}}_2{\mathrm{Se}}_3$, such as its ability to function efficiently in both substrate and superstrate configurations, its grain boundary tolerance, and wide scope of deposition possibilities. By drawing parallels with the historical evolution of $\mathrm{Cd}\mathrm{Te}$ solar cells, we highlight critical lessons that could guide the optimization of ${\mathrm{Sb}}_2{\mathrm{Se}}_3$ devices, including the importance of understanding and controlling doping mechanisms, band-gap grading, and device structure on performance. We also consider the feasibility of adapting existing $\mathrm{Cd}\mathrm{Te}$ production capabilities for ${\mathrm{Sb}}_2{\mathrm{Se}}_3$ fabrication, which could potentially lead to reduced production costs and shorter energy payback times. Despite the challenges and uncertainties that lie ahead, we argue that the promise shown by ${\mathrm{Sb}}_2{\mathrm{Se}}_3$ warrants significant research and development efforts, with the view of making it a viable contender in the solar industry.

The integration of computing and communication capabilities into the power grid has led to vulnerabilities enabling attackers to launch cyberattacks on the grid. The resources that can be deployed to protect a power grid are limited, rendering the need to impose preferences and priorities in optimal resource allocation. Due to the complexity of modern power grids, exploitation of machine learning is desired for developing optimal preferential cybersecurity defense strategies, where choosing a suitable mathematical framework to describe preference satisfaction and articulating a specific machine-learning method are key. We develop a reinforcement-learning approach with the objective of satisfying the preferences as quantitatively described by linear temporal logic. To characterize the preferences, we exploit a probabilistic planning approach that transforms preference satisfaction into a mixed-integer programming (MIP) problem, incorporate MIP into the resource-allocation problem, and use reinforcement learning to obtain the optimal policy. Due to the time-varying nature of the problem, the transformation needs to be carried out and MIP is to be solved at each time step. Utilizing the benchmark W&W 6-bus power-grid network, we validate our preferential machine-learning framework to defend the system against attacks under limited resources. Although our framework is computationally intensive at the present, it provides a stepping stone toward developing more efficient machine-learning frameworks to preferentially defend large cyberphysical systems.

Batteries, supercapacitors, and several other electrochemical devices charge by accumulating ions in the pores of electrolyte-immersed porous electrodes. The charging of such devices has long been interpreted using equivalent circuits and the partial differential equations these give rise to. Here, we discuss the validity of the transmission line (TL) circuit and equation for modeling a single electrolyte-filled pore in contact with a reservoir of resistance $R_r$. The textbook derivation of the pore-reservoir impedance, $R_r+Z_p$, from the TL equation does not correctly account for ionic current conservation at the pore-reservoir interface. However, correcting this shortcoming leads to the same impedance. We also show that the pore impedance, $Z_p$, can be derived directly from the TL circuit, bypassing the TL equation completely. The TL circuit assumes equipotential lines in an electrolyte-filled pore to be straight, which is not the case near the pore entrance and end. To determine the importance of these regions, we numerically simulated the charging of pores of different lengths, ${\ell }_p$, and radii, ${\varrho }_p$, through the Poisson-Nernst-Planck equations. We find that pores with aspect ratios beyond ${\ell }_p/{\varrho }_p⪆5$ have impedances in good agreement with $Z_p$.

Modern battery materials can contain many elements with substantial site disorder, and their configurational state has been shown to be critical for their performance. The intercalation voltage profile is a critical parameter to evaluate the performance of energy storage. The application of commonly used cluster expansion techniques to model the intercalation thermodynamics of such systems ab initio is challenged by the combinatorial increase in configurational degrees of freedom as the number of species grows. Such challenges necessitate the efficient generation of lattice models without overfitting and proper sampling of the configurational space under the requirement of charge balance in ionic systems. In this work, we introduce a combined approach that addresses these challenges by (1) constructing a robust cluster expansion Hamiltonian using the sparse regression technique, including ${\ell }_0{\ell }_2$-norm regularization and structural hierarchy; and (2) implementing semigrand-canonical Monte Carlo to sample charge-balanced ionic configurations using the table-exchange method and an ensemble average approach. These techniques are applied to a disordered rocksalt oxyfluoride ${\mathrm{Li}}_{1.3-x}{\mathrm{Mn}}_{0.4}{\mathrm{Nb}}_{0.3}{\mathrm{O}}_{1.6}{\mathrm{F}}_{0.4}$ (LMNOF) that is part of a family of promising earth-abundant cathode materials. The simulated voltage profile is found to be in good agreement with experimental data and particularly provides a clear demonstration of the $\mathrm{Mn}$ and oxygen contributions to the redox potential as a function of $\mathrm{Li}$ content.