Results 221 to 230 of about 112,560 (300)

Self‐Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers

Advanced Intelligent Discovery, EarlyView.
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

Sub-micron-resolution temperature mapping of Zn negative electrode for flow batteries. [PDF]

Nat Commun
Wang S, Gao Y, Wang S, Ai M, Guo Y, Liu X, Wang Y, Wei Z, Zhu J, Nian Q, Zhang C, Wang L, Lu S, Liu TC, Li Q, Zhi C.   +15 more
europepmc   +1 more source

Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows

Advanced Intelligent Systems, EarlyView.
This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He, Martin Singull, T. Jesper Jacobsson   +2 more
wiley   +1 more source

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Advanced Intelligent Systems, EarlyView.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source

Graphdiyne as a Hole‐Transport Channel in Carbon Nitride Heterojunctions for Synergistic CO2 Reduction and γ‐Butyrolactone Synthesis

Angewandte Chemie, EarlyView.
A fully metal‐free heterojunction constructed from graphdiyne (GDY) and polymeric carbon nitride (PCN) enables efficient coupling of CO2 reduction and tetrahydrofuran oxidation. GDY serves as a hole‐transport channel, establishing a built‐in electric field that drives spatial charge separation. The optimized catalyst achieves near‐unity selectivity for
Xuan Zhang, Junqi Chai, Chong Wang, Wei Lin, Jing Tang, Oleksandr Savateev, Jimmy C. Yu, Jiajia Cheng   +7 more
wiley   +2 more sources

State of health estimation for lithium-ion batteries based on differential thermal voltammetry features and particle swarm optimization-gated recurrent unit. [PDF]

PLoS One
Han B, Sun X, Wang Y, Song Y, Hou E, Wang J, Xu Y.   +6 more
europepmc   +1 more source

Microfluidic Valve‐Integrated Garment for Smooth Sequential Gradient Mechanotherapy

Advanced Intelligent Systems, EarlyView.
We present a soft wearable sleeve that delivers smooth, gap‐free compression using overlapping air‐filled actuators and tiny microfluidic valves. The system reduces bulk, lowers power needs, and uses a smartphone‐sized control box. It can provide sequential gradient compression, gradient pressure holding, and fast deflation, supporting more portable ...
Run Ze Gao, Chen Xi Huang, Drew M. Werbowski, Jacqueline Mary Kormylo, Clark R. Dickerson, Carolyn L. Ren   +5 more
wiley   +1 more source

Photocatalytic Transfer Hydrogenation Using Plastic Hydrolysates as Hydrogen Donor

Angewandte Chemie, EarlyView.
Plastic waste is transformed into functional amines via solar‐driven transfer hydrogenation. Soluble monomers from acid hydrolysis of waste polymers serve as a hydrogen (electron/proton)donors in the selective reduction of nitroarenes using a visible light active photocatalyst consisting of cobalt promoted molybdenum disulfide integrated in cyanamide ...
Papa K. Kwarteng, Afreen H. Naceruddin, Erwin Reisner   +2 more
wiley   +2 more sources

Materials design for thermally improved safety in lithium-ion batteries. [PDF]

Chem Sci
Nan S, Gao G, Yu W, Ding S, Ding D.
europepmc   +1 more source

Disentangling Aleatoric and Epistemic Uncertainty in Physics‐Informed Neural Networks: Application to Insulation Material Degradation Prognostics

Advanced Intelligent Systems, EarlyView.
Physics‐Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains constrained by the limited uncertainty quantification (UQ) capabilities.
Ibai Ramirez, Jokin Alcibar, Joel Pino, Mikel Sanz, Jose I. Aizpurua   +4 more
wiley   +1 more source