Thermoelectric devices can directly convert waste heat into electricity, which makes them an important clean energy technology. The underlying materials performance can be evaluated by the dimensionless figure of merit $ZT$. Metal halides are attractive candidates due to their chemical flexibility and ease of processing; however, the maximum $ZT$ realized ($ZT=0.15$) falls far below the level needed for commercialization ($ZT>1$). Using a first-principles procedure, we assess the thermoelectric potential of copper halide $\mathrm{Cs}{\mathrm{Cu}}_2{\mathrm{I}}_3$, which features one-dimensional $\mathrm{Cu}$-$\mathrm{I}$ connectivity. The $n$-type crystal is predicted to exhibit a maximum $ZT$ of 2.2 at 600 K along the $b$ axis. The strong phonon anharmonicity of this system is shown by locally stable noncentrosymmetric Amm2 structures that are averaged to form the observed centrosymmetric Cmcm space group. Our work provides insights into the structure-property relations in metal halide thermoelectrics and suggests a path forward to engineer higher-performance heat-to-electricity conversion.

The operation of power systems is affected by diverse technical, economic, and social factors. Social behavior determines load patterns, electricity markets regulate the generation, and weather-dependent renewables introduce power fluctuations. Thus, power system dynamics must be regarded as a nonautonomous system whose parameters vary strongly with time. However, the external driving factors are usually only available on coarse scales and the actual dependencies of the dynamic system parameters are generally unknown. Here, we propose a physics-informed machine learning model that bridges the gap between large-scale drivers and short-term dynamics of the power system. Integrating stochastic differential equations and artificial neural networks, we construct a probabilistic model of the power grid frequency dynamics in continental Europe. Its probabilistic prediction outperforms the daily average profile, which is an important benchmark, on a time horizon of 15 min. Using the integrated model, we identify and explain the parameters of the dynamical system from the data, which reveal their strong time-dependence and their relation to external drivers such as wind power feed-in and fast generation ramps. Finally, we generate synthetic time series from the model, which successfully reproduce central characteristics of the grid frequency such as their heavy-tailed distribution. All in all, our work emphasizes the importance of modeling power system dynamics as a stochastic nonautonomous system with both intrinsic dynamics and external drivers.

Selenium ($\mathrm{Se}$) has been studied for over 140 years as the first solid-state solar cell, yet it has only achieved a maximum power conversion efficiency of 6.5%. To improve the efficiency, we propose derivative structures via element mutation. Specifically, we replace $\mathrm{Se}$ with Group 15 pnictogens (Pn = $\mathrm{P},\mathrm{As},\mathrm{Sb}$) and fill the interchain space with alkali metals (M = $\mathrm{Li},\mathrm{Na},\mathrm{K},\mathrm{Rb},\mathrm{Cs}$). Our calculations reveal that the band gaps of MPn span the optimal range for solar absorption. We find that $\mathrm{Na}\mathrm{P}$, composed of earth-abundant elements, has excellent properties as a solar cell absorber, including a slightly indirect band gap, high optical absorption coefficient just above the absorption onset, light electron and hole effective masses, and ambipolar dopability. However, carrier capture calculations show that $\mathrm{P}$ vacancies may limit its photovoltaic performance. Therefore, we propose solutions to reduce $\mathrm{P}$ vacancies through chemical potential control. Finally, we present preliminary results of $\mathrm{Na}\mathrm{P}$ powder sample growth; this reveals a direct band gap of 1.66 eV, close to the predicted value of 1.62 eV. MPn represents a new class of absorber to rival other emerging photovoltaic technologies.

Recent studies on the flow phenomena in stratified thermal-energy-storage (TES) systems have shown that heat conduction from the hot upper fluid layer through the vertical tank sidewall into the lower cold fluid layer leads to counterdirected wall jets adjacent to the vertical sidewalls. It was shown that these phenomena destroyed half of the total exergy content in less than a tenth of the storage time constant of a $2$-${\mathrm{m}}^3$ stratified TES system. This paper investigates short-term fluctuations of the wall jets since these fluctuations can potentially mix the hot and cold zones of the thermal stratification that are separated by the thermocline region. Using particle-image velocimetry measurements in two regions of a TES model experiment (near-wall region and far-field region) and analyzing the frequency content of the velocity fields revealed characteristic oscillations for different regions. In the near-wall region, observed fluctuations agreed well with an adjusted boundary layer frequency from the literature, showing that the wall jet is transitioning from laminar to turbulent flow. In the far-field region, the oscillations are related to the Brunt-Väisälä frequency. It is shown that the fluctuations from the boundaries of the thermocline region are most dominant and propagate into deeper regions of the thermocline. A comparison to data from the large-scale test facility for thermal energy storage in molten salt at the German Aerospace Center in Cologne showed good agreement. The consensus between the two experiments proves firstly that a small-scale model experiment with water as a storage liquid can be used to analyze the physical phenomena of large-scale molten salt storage facilities and secondly that these fluctuations are relevant for exergy destruction in real-scale TES.

All-solid-state batteries are attracting increasing interest due to their higher promised energy densities without the use of flammable liquid electrolytes. Two main challenges for solid-state batteries are contact loss and interphase formation; these play a central role in the quality of the solid-electrolyte–electrode interfaces. Here, we present a modular phase-field modeling framework that is generally applicable to solid-state batteries with different electrodes and corresponding microstructures. The model is based on multiphase porous electrode theory, where $\mathrm{Li}$-ion diffusion in solid electrolytes and electrode materials is integrated through a regular solution free energy functional. Modules for contact loss and diffusive interlayers, able to capture solid-solid and solid-liquid interfaces such as solid-electrolyte interphase formation and coatings, are also implemented, providing numerous modeling options for a comprehensive understanding of electrochemical systems. A thorough comparison between the solid-state and conventional liquid-electrolyte models for phase-separating electrodes reveals the optimal conditions and bottlenecks of solid-state diffusion and failure mechanisms. The predictions underline contact loss and interphase formation as the crucial mesoscopic morphological characteristics of solid-state systems, setting the basis for in-depth understanding and optimized performance in all-solid-state batteries.

Quantifying rates of charge carrier recombination is a crucial step in developing solar cells with high power conversion efficiencies. However, recovering characteristic parameters such as carrier mobility, doping concentration, and recombination rate constants is hindered by the interplay of the carrier dynamics with multiple recombination mechanisms. Interpretation of optoelectronic measurements, such as time-resolved photoluminescence (TRPL), usually relies on analytically tractable simplifications to the underlying physics models for carrier mobility and recombination, which sacrifices some of the information content of the measurement. We have recently shown that, by incorporating simulations of the complete carrier physics into a Bayesian analysis, previously unrecoverable material parameters, such as carrier mobility and the doping level, can be determined from TRPL measurements. Unfortunately, the large number of simulations required by a random sampling of the parameter space necessitated access to high-performance computing resources, limiting the usefulness of the approach. Here, we introduce an importance sampling algorithm (Metropolis Monte Carlo), which reduces the computational requirements by 2–3 orders of magnitude, rendering the Bayesian inference tractable on desktop computers. These developments affirm the utility of a simulation-driven analysis of optical characterization measurements, and make a physics-informed Bayesian inference available to all semiconductor researchers.

$\mathrm{K}$-ion batteries (KIBs) are of potential importance in the future energy industries. Recent efforts were committed to increasing the energy storage density in KIBs. Here, we search for anode materials with high energy density that are compatible with KIBs, by a combination of the swarm-intelligence structure search method and first-principles calculations. We predict the ${\mathrm{Be}}_2{\mathrm{C}}_5$ monolayer to be a kind of material that showcases ultrahigh energy density (∼5455 mWh/g, referenced to the standard hydrogen electrode potential), thanks to an exceptional specific capacity of 2060 mAh/g and low average open-circuit voltage of 0.28 V. This energy density value is much larger than that of most currently known 2D KIB-based anode materials. Furthermore, the ${\mathrm{Be}}_2{\mathrm{C}}_5$ anode presents other intriguing behavior, such as (i) the fast diffusion of $\mathrm{K}$ ions, as suggested by an ultralow barrier energy of about 0.074 eV; (ii) excellent mechanical strength, with a Young’s modulus of about 243 N/m; (iii) robust compatibility with electrolytes; (iv) small-scale volume expansion of 0.68% during the process of potassiation; and (v) a unique “self-doping” effect, which is beneficial for high electronic conductivity.

High-entropy alloys (HEAs) have emerged as a promising platform for designing efficient electrocatalysts; however, the relationship between their structure and activity has not yet been clearly established. In particular, the influences of crystalline defects, such as grain boundaries (GBs), on activity and stability remain unclear. This study demonstrates the impacts of GBs on the oxygen evolution reaction (OER) activity in the FeCoCrNi HEA. We observe a logarithmic dependence of OER performance on mean grain size, spanning 3 orders of magnitude, as measured by evaluators like overpotential and Tafel slope. Spatially resolved microscopic imaging of activated samples indicates that the GBs undergo substantial reconstructions and they are enriched with the in situ formed metal oxides (M-O, M $=$ Fe, Co, Ni) and the amorphous regions. By comparing with simple metals, we reveal a “high-entropy effect,” i.e., HEAs exhibit greater tolerance towards the grain refinements, which explains their activity both in bulk samples and nanoparticles. These findings offer physics-informed strategies for developing HEA electrocatalysis.

We present a multiscale approach that couples ab initio microkinetic simulations and two-dimensional (2D) continuum transport models to study electrochemical ${\mathrm{CO}}_2$ reduction to $\mathrm{CO}$ on $\mathrm{Au}$ electrodes in a flow reactor configuration. We find the key parameters, including ${\mathrm{CO}}_2$ concentration, $p\mathrm{H}$, the current density towards $\mathrm{CO}$, and the Tafel slopes, to strongly depend on the applied potential and position on the electrode. We find a rapid decrease in the ${\mathrm{CO}}_2$ concentration and current density towards $\mathrm{CO}$ as a function of electrode position. We further discuss two strategies to improve ${\mathrm{CO}}_2$ availability: increasing the shear or flow rate of ${\mathrm{CO}}_2$ and the introduction of a defect in between the electrode. In both cases, increased ${\mathrm{CO}}_2$ availability results in increased $\mathrm{CO}$ current density at the higher potentials. We find good agreement between a 1D continuum transport model with an effective boundary layer thickness corresponding to the shear rate used for the 2D simulations. Finally, we provide a phenomenological model that can be used instead of the microkinetic model to accelerate the multiscale simulations when extended to higher dimensions and more complicated reactor geometries.

The power conversion process of a wind turbine can be characterized by a stochastic differential equation (SDE) of the power output conditioned to certain fixed wind speeds. An analogous approach can also be applied to the mechanical loads on a wind turbine, such as generator torque. The constructed SDE consists of the deterministic and stochastic terms, the latter corresponding to the highly fluctuating behavior of the wind turbine (power output of a wind turbine or wind speed). Here we show how advanced stochastic analysis of the noise contribution can be used to show different operating modes of the conversion process of a wind turbine. The parameters of the SDE, known as Kramers-Moyal coefficients, are estimated directly from the measurement data. Clear evidence is found that both continuous diffusion noise and discontinuous jump noise are present. The difference in the noise contributions indicates different operational regions. In particular, we observe that the jump character or discontinuity in power production has a significant contribution in the regions where the control system switches strategies. We find that there is a high increase in jump amplitude near the transition to the rated region, and the switching strategies cannot result in a smooth transition. The proposed analysis provides new insights into the control strategies of the wind turbine.

The blending of hydrogen generated using clean energy into natural gas pipeline networks is proposed in order to utilize existing energy systems for their planned lifetimes while reducing their reliance on fossil fuels. We formulate a system of partial differential equations (PDEs) that govern the flow dynamics of mixtures of gases in pipeline networks under the influence of time-varying compressor and regulator control actions. The formulation is derived for general gas networks that can inject or withdraw arbitrary time-varying mixtures of gases into or from the network at arbitrarily specified nodes. The PDE formulation is discretized in space to form a nonlinear control system that is used to prove that homogeneous mixtures are well behaved and that heterogeneous mixtures may be ill behaved in the sense of monotone ordering of solutions. We use numerical simulations to compute interfaces in the parameter region of sinusoidal boundary conditions that delimit monotonic, periodic, and chaotic system responses. The interfaces suggest that any solution in the monotonic response region is not chaotic and will eventually approach a periodic orbit. The results are demonstrated using examples for a single pipeline and a small test network.

The size distributions of planned and forced outages and their restoration times in power systems have been studied for almost two decades and have drawn great interest as they display heavy tails. Understanding heavy tails has been provided by various threshold models, which are self-tuned at their critical points, but as many papers pointed out, explanations are intuitive, and more empirical data are needed to support hypotheses. In this paper, we analyze outage data collected from various public sources to calculate the outage energy and outage duration exponents of possible power-law fits. Temporal thresholds are applied to identify crossovers from initial short-time behavior to power-law tails. We revisit and add to the possible explanations of the uniformness of these exponents. By performing power spectral analyses on the outage event time series and the outage duration time series, we find that, on the one hand, while being overwhelmed by white noise, outage events show traits of self-organized criticality, which may be modeled by a crossover from random percolation to a directed percolation branching process with dissipation, coupled to a conserved density. On the other hand, in response to outages, the heavy tails in outage duration distributions could be a consequence of the highly optimized tolerance mechanism, based on the optimized allocation of maintenance resources.

A model is derived for the frequency and time domain optoelectronic response of perovskite solar cells (PSCs) that emphasizes the role of charge carrier exchange, i.e., extraction and injection, from (to) the perovskite through the transport layer to (from) the collecting electrode. This process is described by a charge carrier exchange velocity that depends on the mobility and electric field inside the transport layer. The losses implied by this process are modeled in an equivalent circuit model in the form of a voltage-dependent transport layer resistance. The analysis of the model predicts that the voltage dependence of the measured time constants allows discriminating situations where the transport layer properties dominate the experimental response. Application of this method to experimental impedance spectroscopy data identifies charge extraction velocities between 1 and 100 cm/s under 1-sun open-circuit conditions for p-i-n PSCs with poly(triaryl amine) as the hole transport layer; this corresponds to transport layer mobilities between ${10}^{-4}$ and 3 × ${10}^{-3}$ ${\mathrm{cm}}^2$ ${\mathrm{V}}^{-1}$ s${}^{-1}$. The model paves the way for an accurate estimation of the photocurrent and fill factor losses in PSCs caused by low mobilities in the transport layers, using small-perturbation measurements in the time and frequency domain.

As materials with suppressed ordering temperatures and enhanced ground-state entropies, frustrated magnetic oxides are ideal candidates for cryogenic magnetocaloric refrigeration. While previous material design focused on tuning the magnetic moments, their interactions, and the density of moments on the lattice, relatively little attention has been paid to frustrated lattices. Prior theoretical work has shown that the magnetocaloric cooling rate at the saturation field is proportional to a macroscopic number of soft mode excitations that arise due to the classical ground-state degeneracy. The number of these modes is directly determined by the geometry of the frustrated lattice. For corner-sharing geometries, the pyrochlore lattice has 50% more modes than the garnet and kagome lattices, whereas the edge-sharing fcc lattice has only a subextensive number of soft modes. Here we study the role of soft modes in the magnetocaloric effect of four large-spin ${\mathrm{Gd}}^{3+}$ ($L=0$, $J=S=7/2$) Heisenberg antiferromagnets on a kagome, garnet, pyrochlore, and fcc lattice down to $T=2$ K. By comparing measurements of the magnetic entropy change $\mathrm{\Delta }{S}_m$ of these materials at fields up to $9$ T with predictions obtained with use of mean-field theory and Monte Carlo simulations, we are able to understand the relative importance of spin correlations and quantization effects. We observe that tuning the value of the nearest-neighbor coupling has a more significant contribution to the magnetocaloric entropy change in the liquid-$\mathrm{He}$ cooling regime (2–20 K) than tuning the number of soft mode excitations. Our results provide a base for future refrigerant-material design in terms of dimensionality, degree of magnetic frustration, and lattice geometry.

The hybrid lead halide perovskite (LHP) family exhibits large structural fluctuations that contribute to the remarkable properties of LHP-based optoelectronic devices. In three-dimensional LHPs such as ${\mathrm{CH}}_3{\mathrm{NH}}_3{\mathrm{Pb}\mathrm{Br}}_3$, the structural phase transitions have been well characterized; changes in local structure and origins of structural disorder, however, have received less attention. We investigate the temperature dependence of the Pb—Br bond distribution, effective spring constant, and dynamic correlations with a combined extended x-ray-absorption fine structure (EXAFS), single-crystal x-ray diffraction (SXRD) and ab initio molecular dynamics (AIMD) study. EXAFS provides a snapshot of the local environment around the probe atom, rather than the time- and space-averaged structure obtained from SXRD. Molecular librations are observed below the orthorhombic-tetragonal transition temperature, and across this transition we find an anomalous increase in the Pb-Br effective spring constant. The x-ray absorption near edge structure reveals only subtle changes in the electronic structure; therefore, we propose that the increase in bond strength is the result of a redistribution of the charge density, concomitant with the loss of persistent Br…H bonding. Furthermore, the temperature dependence of the Pb—Br bond asymmetry indicates that the structural disorder in ${\mathrm{CH}}_3{\mathrm{NH}}_3{\mathrm{Pb}\mathrm{Br}}_3$ is primarily driven by thermally activated anharmonic dynamics rather than static disorder. Our results describe the local structure responsible for the optoelectronic performance of LHP devices and bring new insights into modifying halide bonding to prevent halide migration.

With its promise for low energy consumption, membrane technology has stimulated efficient approaches to separate mixtures. Technological progress has been made to achieve better purification with lower energy cost and higher productivity. Separation with high productivity is typically accomplished within a limited operation time, which unavoidably results in an excess energy consumption due to the fundamental laws of thermodynamics. Reduction of the energy consumption is of practical importance in the application of membrane separation. However, little is known about the fundamental limit of the least excess energy consumption in a finite operation time. We derive such a limit for the separation of binary mixed gases and show its proportionality to the square of the geometric distance between the initial state and the final state and its inverse proportionality to the operation time. The result shows that optimizing the separation protocol is equivalent to finding the geodesic curve in a geometric space. We predict that for the symmetric protocols, the complete separation of 1 mol of a binary mixture of equally mixed ideal single-atom gases at environmental temperature $T_0$ within operation time $\tau$ requires at least $(12-8\sqrt{2})N_A{k}_B{T}_0{\tau }_p/\tau$ for a particle-transport-dominated process and $2(\ln 2{)}^2{N}_A{k}_B{T}_0{\tau }_h/3\tau$ for a heat-exchange-dominated process, where $N_A$ is the Avogadro number and ${\tau }_p$ and ${\tau }_h$ are the timescales for particle transport and heat exchange, respectively. Interestingly, for a symmetric system, the minimum excess energy consumption is achieved by symmetry-breaking protocols. Our geometric approach may inspire the optimization of industrial membrane separation protocols.

We present a proposal for a one-bit full adder to process classical information based on a few electrons in a triple quantum dot system, serving as a proof of principle for the development of energy-efficient information technologies operating through coherent quantum dynamics. The device works via the repeated execution of a Fredkin gate implemented through the dynamics of a single time-independent Hamiltonian. Our proposal uses realistic parameter values and could be implemented on currently available quantum dot architectures. We compare the estimated energetic costs for operating our full adder with those of well-known fully classical devices, and we identify a few important factors for the future success of this technology.

Nickel-rich layered oxides are important cathode materials for lithium batteries, but their effectiveness is compromised by air sensitivity. Using ${\mathrm{Li}\mathrm{Ni}\mathrm{O}}_2$ as a prototype in ab initio molecular dynamics simulations, we find that oxygen active sites provide molecular orbitals and metal activation that work in synergy to form impurity hydroxyl and carbonate species in the presence of air. This mechanism successfully explains the high air sensitivity on the specific surfaces, the air-sensitivity transition after Li/Ni mixing, and the treatment effect of cation ($\mathrm{Co}$ and $\mathrm{Mn}$) and anion ($\mathrm{F}$ and $\mathrm{S}$) doping methods. Doping with sulfur ions proves to have the best protective effect against ${\mathrm{H}}_2\mathrm{O}$ and ${\mathrm{CO}}_2$. A series of first-principles calculations show that oxygen active sites originate from electron loss, and the activation effect of metals is limited by the type of elements, the number of coordination bonds, the coactivation effect and the Jahn-Teller distortion. While impurity formation is usually a localized phenomenon, the autoprotolysis of ${\mathrm{H}}_2\mathrm{O}$ allows long-distance proton transport to support the hydrolysis reaction. The adsorption energy of ${\mathrm{H}}_2\mathrm{O}$ and ${\mathrm{CO}}_2$ is not directly related to the air sensitivity, but is inversely proportional to the surface thermodynamic stability in the study of Li/Ni mixing. However, the concentration of Li/Ni mixing depends on the exposed surface, implying that the influence of Li/Ni mixing on air sensitivity and air adsorption capacity is uneven. Our studies contribute to realizing a cost-effective and convenient experimental strategy for treating air sensitivity of ${\mathrm{Li}\mathrm{Ni}\mathrm{O}}_2$ cathode materials.

Luminescent semiconductors are the key material in a host of optoelectronic devices, including solar cells, light-emitting diodes, and x-ray scintillators, and have been discovered at an increasing rate over the last decades. To optimize any device, a luminescent semiconductor’s photophysics must be understood and its loss processes minimized. Several accessible spectroscopic techniques exist, which can together give all relevant photophysical information, namely, UV-Vis spectroscopy, photoluminescence quantum efficiency, and time-resolved photoluminescence. However, these measurements are often poorly used, incorrectly fitted, or important information is missed. Here, we present best practices in applying these techniques to characterize luminescent semiconductors with unknown photophysical properties. We highlight which information can be obtained from each measurement, when it is appropriate to apply different mathematical models, and give examples from a range of semiconductors. This work will help to standardize and streamline the characterization of luminescent semiconductors, enabling more efficient devices.