Results 91 to 100 of about 35,563 (212)

Empowering Carbon Fibers With Ti3C2Tx MXene: A Paradigm Shift Toward Integrated Structure‐Function Composites

open access: yesAdvanced Science, EarlyView.
This review comprehensively outlines how Ti3C2Tx MXene transforms carbon fiber from a structural component into a multifunctional platform. We systematically detail cutting‐edge modification strategies and showcase exceptional performance in EMI shielding, energy storage, smart sensing, and beyond.
Hongshuo Cao   +6 more
wiley   +1 more source

Rational design and synthesis of new acetamide-indole-benzo[d]imidazole-carboxylic acid hybrids as dual PTP1B/α-glucosidase inhibitors. [PDF]

open access: yesRSC Adv
Dastyafteh N   +11 more
europepmc   +1 more source

Accelerated Screening of Halide Double Perovskites via Hybrid Density Functional Theory and Machine Learning for Thermoelectric Energy Conversion

open access: yesAdvanced Energy and Sustainability Research, EarlyView.
This study integrates hybrid density functional theory, Boltzmann transport theory, and machine learning to accelerate the discovery of lead‐free halide double perovskites for thermoelectric energy conversion. By screening 102 compounds, the authors identify high‐performing candidates such as Rb2GeI6 and Cs2SnBr6, offering a sustainable pathway toward ...
Souraya Goumri‐Said   +2 more
wiley   +1 more source

Topological Properties of International Commodity Market: How Uncertainty Affects the Linkages?

open access: yesAgribusiness, EarlyView.
ABSTRACT The study aims to explore the network topology of the international commodity market by examining the interconnections among 21 commodity futures across various categories, including energy, precious and industrial metals, and agriculture. We analyze the market structure of these commodity futures under both low and high uncertainty conditions
Ibrahim Yagli, Bayram Deviren
wiley   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

open access: yesAdvanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian   +12 more
wiley   +1 more source

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