The large-scale application of intermittent renewable energy has boosted the prosperous development of secondary batteries for the past several decades. In comparison with batteries based on liquid electrolytes, solid-state batteries are regarded as the goal for future energy storage due to their superior safety, nonvolatilization, excellent mechanical strength, and high energy density. While conventional solid electrolytes (SEs) struggle with their intrinsic limitations, zeolites are emerging as a promising choice for the future SEs of solid-state batteries. Herein, first, we analyze the challenges faced by conventional SEs to highlight the significance of leveraging zeolite-based SEs in secondary batteries. Then, we discuss the application of zeolites in liquid and solid electrolytes and showcase the functions of zeolites contributing to the enhanced wettability, thermal stability, mechanical strength, electrochemical stability, and ionic conductivity of separators and/or SEs. Finally, we provide a perspective on the development directions for zeolite-based SEs toward the design of materials, extension of battery systems, investigation of the conductive mechanism, and their practical applications.

As electricity grids become more renewable energy compliant, there will be a need for novel semiconductors that can withstand high power, high voltage, and high temperatures. Currently used or explored wide-band-gap materials for power electronics are costly ($\mathrm{GaN}$), difficult to synthesize as high-quality single crystals ($\mathrm{SiC}$) and at scale (diamond, $\mathrm{BN}$), have low thermal conductivity ($\beta$-${\mathrm{Ga}}_2{\mathrm{O}}_3$), or cannot be suitably doped ($\mathrm{AlN}$). We conduct a computational search for novel semiconductors across 1340 known metal oxides using first-principles calculations and existing and improved transport models. We calculate the Baliga figure of merit (BFOM) and lattice thermal conductivity (${\kappa }_L$) to identify top candidates for n-type power electronics. We find 47 mostly ternary oxides that have higher ${\kappa }_L$ than $\beta$-${\mathrm{Ga}}_2{\mathrm{O}}_3$ and higher n-type BFOM than $\mathrm{SiC}$ and $\mathrm{GaN}$. We use the branch point energy to rank the likelihood of n-type extrinsic doping, further reducing our top candidates to 14 previously unexplored compounds. Among these, several material classes emerge, including 2-2-7 stoichiometry thortveitites and pyrochlores, II-IV spinels, and calcite-type borates. Within these classes, we propose ${\mathrm{In}}_2{\mathrm{Ge}}_2{\mathrm{O}}_7$, ${\mathrm{Mg}}_2{\mathrm{GeO}}_4$, and ${\mathrm{InBO}}_3$ for power electronics as they are the most favorable for n-type doping based on our preliminary evaluation and could be grown as single crystals or thin-film heterostructures.

A key to ensuring the security of smart electrical power grids is to devise and deploy effective defense strategies against cyberattacks. To achieve this goal, an essential task is to simulate and understand the dynamic interplay between the attacker and defender, for which stochastic game theory and reinforcement learning stand out as a powerful mathematical and computational framework. Existing works are based on conventional $Q$-learning to find the critical sections of a power grid to choose an effective defense strategy, but the methodology is only applicable to small systems. Additional issues with $Q$-learning are the difficulty in considering the timings of cascading failures in the reward function and deterministic modeling of the game, while attack success depends on various parameters and typically has a stochastic nature. Our solution for overcoming these difficulties is to develop a deep $Q$-learning-based stochastic zero-sum Nash strategy solution. We demonstrate the workings of our deep $Q$-learning solution using the benchmark Wood and Wollenberg 6-bus and the IEEE 30-bus systems; the latter is a relatively large-scale power-grid system that defies the conventional $Q$-learning approach. Comparison with alternative reinforcement learning methods provides further support for the general applicability of our deep $Q$-learning framework in ensuring secure operation of modern power-grid systems.

Mechanical impact-induced nonlinearity proves to be effective in the tunable broadening of the frequency response in vibrational energy harvesters at various size scales. However, the substantial impact-induced power loss in impact-driven energy harvesters (IDEHs) negates such advantages. Here, an in situ power compensation mechanism is introduced to recover the impact-induced losses, and thereby, substantially enhance the energy harvesting efficiency. The proposed scheme is realized by designing an IDEH, where a dual transduction barium titanate/SU-8 based nanocomposite is used as the active layer. The piezoelectric transduction of the nanocomposite is used for conventional wideband power generation from the IDEH, whereas the additional triboelectric property enables the impact-induced loss compensation. The proposed improvement is quantified using a suitably defined figure-of-merit “power integral” (PI) that integrates the total power generated over the entire operational frequency range. The PI shows a shift from the region of impact loss to the region of compensation at input accelerations of $<1.5$ g. Through suitable device engineering, the hybrid IDEH is experimentally shown to achieve 3.3 times power gain with bandwidth broadening of 5.4 Hz over conventional IDEH designs, which translates to $100\mathrm{\%}$ PI recovery. The underlying physics of the proposed method is independent of the device scale and is explained using a coupled numerical model.

The design of novel cathode materials for $\mathrm{Li}$-ion batteries would greatly benefit from accurate first-principles predictions of structural, electronic, and magnetic properties as well as intercalation voltages in compounds containing transition-metal elements. For such systems, density-functional theory (DFT) with standard (semi)local exchange-correlation functionals is of limited use as it often fails due to strong self-interaction errors that are especially relevant in the partially filled $d$ shells. Here, we perform a detailed comparative study of the phospho-olivine cathode materials ${\mathrm{Li}}_x{\mathrm{MnPO}}_4$, ${\mathrm{Li}}_x{\mathrm{FePO}}_4$ and the mixed transition metal ${\mathrm{Li}}_x{\mathrm{Mn}}_{1/2}{\mathrm{Fe}}_{1/2}{\mathrm{PO}}_4$ ($x=0,1/4,1/2,3/4,1$) using four electronic-structure methods: DFT, DFT+$U$, DFT+$U$+$V$, and HSE06. We show that DFT+$U$+$V$, with onsite $U$ and intersite $V$ Hubbard parameters determined from first principles and self-consistently with respect to the structural parameters by means of density-functional perturbation theory (linear response), provides the most accurate description of the electronic structure of these challenging compounds. In particular, we demonstrate that DFT+$U$+$V$ displays very clearly “digital” changes in oxidation states of the transition-metal ions in all compounds, including the mixed-valence phases occurring at intermediate $\mathrm{Li}$ concentrations, leading to voltages in remarkable agreement with experiments. We show that the inclusion of intersite Hubbard interactions is essential for the accurate prediction of thermodynamic quantities, balancing the drive for localization induced by the onsite $U$ with intersite $V$ orbital hybridizations. At variance with other methods, DFT+$U$+$V$ describes accurately such localization-hybridization interplay, and thus opens the door for the study of more complex cathode materials as well as for a reliable exploration of the chemical space of compounds for $\mathrm{Li}$-ion batteries.

Emerging quantum technologies, such as quantum computations and precise sensing, provide new opportunities in the fields of science and engineering. While energy consumption is a major concern for modern industry and society, it is rarely taken into consideration for quantum technologies. Especially, the potential of quantum technologies directly powered by renewable energy has long been neglected. The initialization, manipulation, and readout of quantum systems generally require high-power-consuming equipment, such as a dilution refrigerator, a microwave-power amplifier, and a high-power laser. Here, we discover a direct utilization path for solar energy to steer the quantum states of negatively charged nitrogen-vacancy centers in diamond, which is one of the most promising solid-state quantum systems in the past decades. Following this method, we demonstrate sunlight-driven quantum magnetometry. The initialization and readout of this quantum magnetometry are achieved directly by sunlight, and conventional microwave manipulation can be removed by using a microwave-free scheme. By utilizing ambient energy directly, our method brings potential solutions to the energy-consumption issue of quantum technologies. This technique can be further extended to multiple quantum systems, and thus opens the door to environmentally sustainable quantum technologies and self-powered quantum sensing in the future.

Luminescent solar concentrators (LSCs) are theoretically able to concentrate both direct and diffuse solar radiation with extremely high efficiencies. Photon-multiplier luminescent solar concentrators (PM-LSCs) contain chromophores that exceed 100% photoluminescence quantum efficiency. PM-LSCs have recently been experimentally demonstrated and hold promise to outcompete traditional LSCs. However, we find that the thermodynamic limits of PM-LSCs are different and are sometimes more extreme relative to traditional LSCs. As might be expected, to achieve very high concentration factors, a PM-LSC design must also include a free energy change, analogous to the Stokes shift in traditional LSCs. Notably, unlike LSCs, the maximum concentration ratio of a PM-LSC is dependent on the brightness of the incident photon field. For some brightnesses, but equivalent energy loss, the PM-LSC has a greater maximum concentration factor than that of the traditional LSC. We find that the thermodynamic requirements to achieve highly concentrating PM-LSCs differ from those of traditional LSCs. The new model gives insight into the limits of concentration of PM-LSCs and may be used to extract design rules for further PM-LSC design.

Long diffusion lengths of photoexcited charge carriers are crucial for high power conversion efficiencies of photoelectrochemical and photovoltaic devices. Time-resolved photoconductance measurements are often used to determine diffusion lengths in conventional semiconductors. However, effects such as polaron formation or multiple trapping can lead to time-varying mobilities and lifetimes that are not accounted for in the conventional calculation of the diffusion length. Here, a generalized analysis is presented that is valid for time-dependent mobilities and time-dependent lifetimes. The diffusion length is determined directly from the integral of a photoconductivity transient and can be applied regardless of the nature of carrier relaxation. To demonstrate our approach, photoconductivity transients are measured from 100 fs to 1 µs by the combination of time-resolved terahertz and microwave spectroscopy for ${\mathrm{BiVO}}_4$, one of the most studied metal oxide photoanodes for photoelectrochemical water splitting. The temporal evolution of charge carrier displacement is monitored and converges after about 100 ns to a diffusion length of about 15 nm, which rationalizes the photocurrent loss in the corresponding photoelectrochemical device. The presented method is further validated on $a\text{−}\mathrm{Si}:\mathrm{H}$, $c\text{−}\mathrm{Si}$, and halide perovskite, which underlines its potential to determine the diffusion length in a wide range of semiconductors, including disordered materials.

Baroclinicity, which leads to height-dependent driving pressure gradients, occurs in various situations such as the flow transition between land and sea, and sloping terrain. It has been shown that baroclinicity modifies the structure of the atmospheric boundary layer. For example, negative shear baroclinicity creates additional turbulence at higher elevations, which might influence the energy entrainment into large wind farms. Here, we use large-eddy simulations to study the effect of baroclinicity-induced negative shear on the wind farm power production and energy entrainment into a large wind farm. In agreement with the literature, our simulations show that negative geostrophic wind shear significantly modifies the mean wind velocity in the atmospheric boundary layer. Specifically, for the cases considered in the study, the negative geostrophic shear causes a change in the mean velocity up to 2.3 m/s at hub height, which greatly alters the wind farm power production. Additionally, we demonstrate with an energy budget analysis that a wind farm does not necessarily benefit from the additional turbulence created by the negative geostrophic wind shear. The reason for this is that the baroclinicity-induced negative shear alters the height and strength of the low-level jet and creates an upward flux above the jet, limiting the energy entrainment into the wind farm. Our results show that wind resources are altered in the boundary layer due to negative geostrophic wind shear and should be considered in wind farm modeling and power forecasts.

Accurate models of turbulent wind fields have become increasingly important in the atmospheric sciences, e.g., for the determination of spatiotemporal correlations in wind parks, the estimation of individual loads on turbine rotor and blades, or the modeling of particle-turbulence interaction in atmospheric clouds or pollutant distributions in urban settings. Because of the difficult task of resolving the fields across a broad range of scales, one oftentimes has to invoke stochastic wind field models that fulfill specific, empirically observed, properties. Whereas commonly used Gaussian random field models solely control second-order statistics (i.e., velocity correlation tensors or kinetic energy spectra), we explicitly show that our extended model emulates the effects of higher-order statistics as well. Most importantly, the empirically observed phenomenon of small-scale intermittency, which can be regarded as one of the key features of atmospheric turbulent flows, is reproduced with a very high level of accuracy and at considerably low computational cost. Our method is based on a multipoint statistical description of turbulent velocity fields that consists of a superposition of multivariate Gaussian statistics with fluctuating covariances. We propose a new and efficient sampling algorithm for this Gaussian scale mixture and demonstrate how such “superstatistical” wind fields can be constrained on a certain number of real-world measurement data points from a meteorological mast array.

The optimization of thermoelectric materials for use in various applications, such as spacecraft power generation, waste heat recovery, and Peltier coolers, requires a careful optimization of material properties. This can be achieved via defect engineering in which defects are purposefully added to a material to produce desired properties. In this tutorial, we discuss a defect engineering strategy called phase-boundary mapping. While many compound thermoelectric semiconductors are often called “line compounds” due to their appearance as a line on a binary phase diagram, in reality, due to the laws of thermodynamics, all phases have a finite phase width. The edges of this phase space define the chemical potential of the material. By making small compositional changes across this phase space, appreciable differences in thermoelectric properties are observed due to this change in the chemical potential. Additionally, the phase equilibria of a thermoelectric material impacts alloying and dopability that further impacts material properties. Phase-boundary mapping is a strategy that allows us to explore the limits of a material and ultimately reproducibly optimize thermoelectric performance by considering the effects of off-stoichiometry on chemical potential, and thus defect energies and material properties. This technique can be applied in the optimization of numerous thermoelectric materials as well as extended to other semiconductors with properties controlled by defects.

Light-ray tracing (RT) and the transfer matrix method (TMM) allow detailed optical simulation of single-junction silicon and perovskite solar cells, which critically aids device design towards record performance. However, their accuracy is compromised when simulating monolithic perovskite/silicon tandem devices built from textured silicon substrates, due to the resulting complex top-cell morphologies. The associated front surfaces of such tandem devices are typically either roughly conformal to the textured silicon underneath (for thermally evaporated or hybrid-deposited perovskites) or flattened (for solution-processed perovskites). Here, we develop accurate optical models for each configuration. For the conformal-like morphology, we apply a texture-on-texture model to accommodate for local imperfect conformalities. Contrastingly, for the flattened morphology, we develop a multi-subcell model to solve the limitation that films must be conformal in one-step RT + TMM simulations. We verify our models with experimental light absorption and quantum efficiency data for photovoltaic cells and modules in both morphological configurations and identify primary sources of parasitic light absorption. Finally, we extend our simulations to module-scale optics and find that textured module glass can effectively suppress photon escape for both tandem-device morphologies, improving their module performance.

The solid-electrolyte interphase (SEI) is crucial to the electrochemical performance of all-solid-state batteries (ASSBs). Theoretical characterization of SEI properties will help understand the origin of interfacial stability (and instability) between solid electrolytes and electrodes. Among solid electrolytes for Lithium (Li)-ion ASSBs, the lithium phosphorus oxynitride ${\mathrm{Li}}_x{\mathrm{PO}}_y{\mathrm{N}}_z$ (LiPON) is one of the most stable against the $\mathrm{Li}$ metal anode. However, it has been shown that LiPON reacts with $\mathrm{Li}$ metal and forms SEIs. The SEI formation stops after a thin layer is formed, but the mechanism that enables this apparent stabilization is unclear. Thermodynamics underpins the defect formation in materials and, in turn, creation of electronic charge. Materials for energy storage, including solid electrolytes, are no exception to this fundamental process. Here, we computationally evaluate the electronic passivation of SEIs and its role in stabilizing the $\mathrm{Li}$-LiPON interface. Specifically, we determine the defect and charge carrier concentrations in $\mathrm{Li}$-LiPON SEIs, including ${\mathrm{Li}}_2\mathrm{O}$, ${\mathrm{Li}}_3\mathrm{N}$, ${\mathrm{Li}}_3\mathrm{P}$, and ${\mathrm{Li}}_3{\mathrm{PO}}_4$. The defect and charge carrier concentrations are calculated from defect thermodynamics. We then predict the electronic conductivities of the SEIs under different electrochemical conditions, which correspond to varying potentials to the Li metal anode. Our results reveal that the stoichiometrically abundant and uniformly distributed ${\mathrm{Li}}_2\mathrm{O}$ has expectedly negligible electronic conductivity, while the electronically conducting components, such as ${\mathrm{Li}}_3\mathrm{N}$ and ${\mathrm{Li}}_3\mathrm{P}$, show preferential distribution in the SEI. We posit that the overall electronically insulating nature of the SEI is responsible for the stability of the $\mathrm{Li}$-LiPON interface. The computational approach adopted here can be extended to reveal the origin of the interfacial stability in other ASSBs.

Strain is present at the interfaces of solid electrolytes with the cathode and the anode in solid-state batteries due to interfacial reactions and due to volumetric expansion and contraction of the electrodes during battery charging and discharging cycles. This work quantifies the effect of elastic strain on $\mathrm{Li}$-ion diffusion in a model solid electrolyte, β-${\mathrm{Li}}_3{\mathrm{PS}}_4$, by using ab initio molecular dynamics (AIMD). We find that the strain tensors which compress the c axis (+2% a-b, −2% a-c, −2% b-c, −2% isotropic) increase the $\mathrm{Li}$-ion diffusivity, and the strain tensors which stretch the c axis (−2% a-b, +2% a-c, +2% b-c, +2% isotropic) reduce it. Ionic conductivity increases by 2–14-fold with a reduction in activation energy for c-axis compressive strains and decreases by 1–14-fold with an increase in activation energy for c-axis tensile strains at room temperature. The c-axis compression increases disorder in the lattice and promotes jumps along all migration pathways. In particular, the $\mathrm{Li}$ 4c site occupancy increases from about 3% to 6% (at 500 K), thus creating more vacancies at otherwise fully occupied and bottleneck 8d and 4b sites. The c-axis compression also reduces the distance between the 8d and 4b sites, thus increasing Coulomb repulsion between these sites. Increased Coulomb repulsion can destabilize $\mathrm{Li}$ at the 8d and 4b sites, promoting reduced occupancy and increased diffusivity associated with these sites. The results show that elastic strain can promote $\mathrm{Li}$ disorder and superionic conductivity, with an effect on $\mathrm{Li}$-ion diffusion comparable to that of chemical substitution, and is important to consider for an accurate understanding and prediction of the solid-state $\mathrm{Li}$-ion battery interface properties.

${\mathrm{Sr}\mathrm{Ti}\mathrm{O}}_3$ is a highly efficient photocatalyst for the overall water splitting reaction under UV irradiation. However, an atomic-level understanding of the active surface sites responsible for the oxidation and reduction reactions is still lacking. Here we present a unified experimental and computational account of the photocatalytic activity at the SrO and ${\mathrm{Ti}\mathrm{O}}_2$ terminations of aqueous solvated [001] ${\mathrm{Sr}\mathrm{Ti}\mathrm{O}}_3$. Our experimental findings show that the overall water-splitting reaction proceeds on the ${\mathrm{Sr}\mathrm{Ti}\mathrm{O}}_3$ surface only when the two terminations are simultaneously exposed to water. Our simulations explain this, showing that the photogenerated hole-driven oxidation primarily occurs at SrO surfaces in a sequence of four single hole transfer reactions, while the ${\mathrm{Ti}\mathrm{O}}_2$ termination effects the crucial band alignment of the photocatalyst relative to the water oxidation potential. The present work elucidates the interdependence of the two chemical terminations of ${\mathrm{Sr}\mathrm{Ti}\mathrm{O}}_3$ surfaces, and has consequent implications for maximizing sustainable solar-driven water splitting.

Recently, a new version of laser-induced fusion was proposed where implanted nanoantennas regulated and amplified the light absorption in the fusion target [L.P. Csernai et al., Phys. Wave Phenom. 28, 187–99 (2020)]. In this paper we estimate the nanoantenna lifetime in a dynamical kinetic model and describe how electrons are leaving the nanoantenna’s surface, and for how long the plasmonic effect is maintained. Our model successfully shows a nanorod antenna lifetime that will allow future fusion studies with top-energy short laser ignition pulses.

Future power grids will be operating a large number of heterogeneous dynamical actors. Many of these will contribute to the fundamental dynamical stability of the system, and play a central role in establishing the self-organized synchronous state that underlies energy transport through the grid. We derive a normal form for grid-forming components in power grids. This allows analyzing the grids’ systemic properties in a technology neutral manner, without detailed component models. Our approach is based on the physics of the power flow in the grid on the one hand, and on the common symmetry that is inherited from the control objectives grid-forming power grid components are trying to achieve. We provide an initial experimental validation that this normal form can capture the behavior of complex grid-forming inverters without any knowledge of the underlying technology, and show that it can be used to make technology-independent statements on the stability of future grids.

Despite being capable of very fast charging, the pseudocapacitive properties of electrochemical capacitors still require significant research to attain energy densities comparable to that of batteries. Herein we discuss and theoretically benchmark the physics of quantized capacitance as a Faradaic charge storage mechanism, providing near “ideal” pseudocapacitive properties in the context of batterylike energy storage. Through careful electrolyte and reactant engineering, our physical analysis suggests that this less explored “pseudocapacitive battery” mechanism could provide power densities of approximately ${10}^4$ W/L combined with volumetric energy densities in the range of 100 Wh/L (or potentially greater). These benchmarks are arrived at though a comprehensive analysis of two-dimensional (2D) graphitic nanoparticles considering the impact of solvation, electron-electron interactions, and electron transfer processes. In general, our findings indicate that 2D nanomaterials exhibiting quantized capacitance provide a promising and underexplored physical axis within electrochemical capacitors towards realizing very fast charging at energy densities comparable to that of batteries.

The atmospheric concentration of methane has more than doubled since the start of the Industrial Revolution. Methane is the second-most-abundant greenhouse gas created by human activities and a major driver of climate change. This APS-Optica report provides a technical assessment of the current state of monitoring U.S. methane emissions from oil and gas operations, which accounts for roughly 30% of U.S. anthropogenic methane emissions. The report identifies current technological and policy gaps and makes recommendations for the federal government in three key areas: methane emissions detection, reliable and systematized data and models to support mitigation measures, and effective regulation.