Results 151 to 160 of about 356,866 (308)
ABSTRACT Interpreting the impedance response of perovskite solar cells (PSCs) is challenging due to the complex coupling of ionic and electronic motion. While drift‐diffusion (DD) modelling is a reliable method, its mathematical complexity makes directly extracting physical parameters from experimental data infeasible.
Mahmoud Nabil +4 more
wiley +1 more source
Universal Oxychlorination Strategy in Halide Solid Electrolytes for All‐Solid‐State Batteries
A WO2Cl2‐driven oxychlorination strategy enables bulk oxygen incorporation into close‐packed LixMCl6 (M = Zr, Y, Er, In) halide lattices. Oxygen is selectively anchored by W6+ as lattice‐integrated [WO2Cl4]2− units, regulating the anionic framework, diversifying Li coordination, and weakening Li–Cl interactions.
Jae‐Seung Kim +13 more
wiley +1 more source
Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen +4 more
wiley +1 more source
A thin gold nanolayer on lithium metal enables stable and uniform lithium plating in solid‐state batteries, enabling nearly 1 year and 11 months of continuous operation. The conductive coating lowers interfacial resistance, suppresses dendrite growth, and improves cycling stability, attributed to homogeneous deposition of Li+ on the gold layer ...
Natalia Voronina +10 more
wiley +1 more source
This work reports a combined frequency‐domain and time‐domain terahertz (THz) spectroscopic approach to elucidate intrinsic carrier properties and transport mechanisms in framework materials over the extended 0.5–20 THz region. Subtle structural and chemical variations are shown to strongly influence THz charge‐transport behavior across the far ...
Satyapriya Nath +13 more
wiley +2 more sources
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park +19 more
wiley +1 more source
AI in chemical engineering: From promise to practice
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew +4 more
wiley +1 more source
A machine learning‐guided self‐driving laboratory screened over 500 nickel‐based layered double‐hydroxide catalysts for alkaline oxygen evolution. Out of the eight metals, the robot uncovered a quaternary Ni–Fe–Cr–Co catalysts requiring only 231 mV overpotential to reach 20 mA cm−2.
Nis Fisker‐Bødker +3 more
wiley +1 more source
Improved active current limiting control for flexible DC power grid to consider the effects of selective and higher fault resistance. [PDF]
Qin X, Hou J, Fan Y, Song G, Wu X.
europepmc +1 more source
A low‐cost, self‐driving laboratory is developed to democratize autonomous materials discovery. Using this "frugal twin" hardware architecture with Bayesian optimization, the platform rapidly converges to target lower critical solution temperature (LCST) values while self‐correcting from off‐target experiments, demonstrating an accessible route to data‐
Guoyue Xu, Renzheng Zhang, Tengfei Luo
wiley +1 more source

