PRX Energy

This study identifies key challenges in hydrogen storage and proposes computational strategies to design more effective storage materials for next-generation energy systems.

By establishing a standardized protocol for high-rate testing, this study uncovers how distinct battery chemistries respond to fast charging/discharging and pinpoints the degradation pathways that limit performance.

A self-consistent theoretical framework for osmotic energy conversion provides the electrical response of realistic nanofluidic energy systems.

By evaluating energy consumption alongside computational complexity, this work introduces a framework for optimizing the energetic efficiency of sequential quantum phase estimation protocols.

Machine-learning-based simulations and experimental validation reveal intrinsically low thermal conductivity in LiYTiO${}_4$, which may limit its practical application as a lithium-ion battery anode.

This study introduces a framework for nonlinear thermoelectric effects, connecting fundamental theory to specific candidate materials for advanced thermoelectric technologies.

A simple yet quantitatively accurate theory, informed by computer simulations, explains how water scarcity in extremely concentrated aqueous electrolytes drives the formation of branched ion clusters and ultimately a gel-like network.

Spatially and temporally resolved X-ray characterization reveals how the selenium distribution governs the local optoelectronic properties of Cd(Se,Te) solar cells.

This study establishes a comprehensive framework to resolve coupled structural and chemical processes in alloying anodes, providing a pathway to guide the rational design of battery materials under realistic electrochemical conditions.

A theoretical model of lithium metal electrodeposition and morphological evolution connects lithium plating dynamics to key experimental variables and, ultimately, battery performance.

A multiscale model explores complex ion and fluid dynamics in alkaline water electrolyzers, elucidating the transformative impact of elevated temperature on the physical and electrochemical properties.

This Tutorial demonstrates how Bayesian methods can be applied to analyze temperature-dependent transport data, offering practical workflows to model ionic conductivity in energy materials.

A report on atmospheric carbon dioxide removal (CDR) underscores the importance of CDR technologies in meeting carbon dioxide management targets, while emphasizing that reducing carbon dioxide emissions must remain a top priority.