With implications for hydrogen transport and decarbonization of gas and electricity networks, the blending of hydrogen with natural gas for transport via existing infrastructures is explored by computing transitions between monotonic, periodic, and chaotic responses to boundary conditions.

Using simulations, this study explores the atomic-scale mechanism of air sensitivity of a LiNiO${}_2$ cathode material; consequently, doping strategies are proposed to improve stability for lithium batteries.

A proposal for quantum-based classical computation shows how approaches from quantum computing may enable energy-efficient classical information processing and highlights energy costs that currently limit this technology.

Membrane technologies promise energy-efficient processes for separation of mixtures. The tradeoff between operation time and energy consumption posts constraint on the optimization of such processes. Here the authors establish an equivalence between optimizing the energy consumption within given operation time to finding the geodesic curve in a geometric space.

A combination of experimental techniques and simulations show that thermally activated dynamics are the main component of structural disorder in lead halide perovskites, rather than static disorder.

Using experiment and simulations, a comparison of four candidate magnetocaloric materials provides insight for the design of efficient refrigerant materials.

A model is developed to describe the frequency domain response of perovskite solar cells, with an emphasis on the role of transport layers. Application to experimental data identifies that measured time constants are limited by charge carrier extraction rather than recombination.

Toward mitigating large-scale power outages, connections are established between energy research and statistical-nonlinear physics to understand the emergence of pervasive heavy tails in outage statistics.

With implications for hydrogen transport and decarbonization of gas and electricity networks, the blending of hydrogen with natural gas for transport via existing infrastructures is explored by computing transitions between monotonic, periodic, and chaotic responses to boundary conditions.

The unpredictable and intermittent nature of wind and its interaction with wind turbines pose challenges for stable energy production; here, the development of stochastic analysis techniques leads to insight that can benefit wind turbine performance.

Using $ab$ $initio$ microkinetic simulations coupled with a two-dimensional continuum transport model, this study models electrochemical CO${}_2$ reduction in a flow reactor with spatial resolution of key reaction parameters.

Toward developing efficient electrocatalysts for the oxygen evolution reaction, the grain boundary density of high entropy alloys can be increased to enhance electrocatalytic activity.

Structure prediction methods and first-principles calculations are combined to propose a Dirac anode material for potassium ion batteries, namely a thermodynamically stable Be${}_2$C${}_5$ monolayer.

Extraction of material parameters from experimental time-resolved photoluminescence measurements can be problematic; here, a Bayesian inference approach, with Metropolis Monte Carlo sampling, is demonstrated to quickly and accurately infer essential optoelectronic parameters from the characterization measurements.

Toward optimization of safe and sustainable solid-state batteries, a computational framework is developed for modeling of versatile solid-state systems.