Results 191 to 200 of about 22,138,923 (333)

Decoding Tattoo and Permanent Makeup Pigments: Linking Physicochemical Properties to Absorption, Distribution, Metabolism, and Elimination Profiles Using Quantitative Structure–Activity Relationship (QSAR)‐Based New Approach Methodologies (NAMs)

open access: yesAdvanced Intelligent Discovery, EarlyView.
This study applies QSAR‐based new approach methodologies to 90 synthetic tattoo and permanent makeup pigments, revealing systemic links between their physicochemical properties and absorption, distribution, metabolism, and elimination profiles. The correlation‐driven analysis using SwissADME, ChemBCPP, and principal component analysis uncovers insights
Girija Bansod   +10 more
wiley   +1 more source

Bayesian Optimization Guiding the Experimental Mapping of the Pareto Front of Mechanical and Flame‐Retardant Properties in Polyamide Nanocomposites

open access: yesAdvanced Intelligent Discovery, EarlyView.
Bayesian optimization enabled the design of PA56 system with just 8 wt% additives, achieving limiting oxygen index 30.5%, tensile strength 80.9 MPa, and UL‐94 V‐0 rating. Without prior knowledge, the algorithm uncovered synergistic effects between aluminum diethyl‐phosphinate and nanoclay.
Burcu Ozdemir   +4 more
wiley   +1 more source

Insights into the Controlled Formation of Zr‐Based Metal–Organic Gels: Linking Macroscopic Properties with Molecular Information from Solution State NMR

open access: yesAngewandte Chemie, EarlyView.
Real time STD/SDTD NMR unveils water structuring during UiO‐66 gelation under mild, acid‐free conditions compatible with biomolecule encapsulation. This approach bridges molecular‐scale solvent ordering with macroscopic gel properties, unlocking mechanistic insight for the rational design of MOF gels.
Juan C. Muñoz‐García   +5 more
wiley   +2 more sources

Aldehyde cool-flame chemistry explains a missing source of organic acids. [PDF]

open access: yesNat Commun
Liu B   +10 more
europepmc   +1 more source

A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation

open access: yesAdvanced Intelligent Discovery, EarlyView.
This study introduces an iterative supervised machine learning framework for metal‐organic framework (MOF) discovery. The approach identifies over 97% of the best performing candidates while using less than 10% of available data. It generalizes across diverse MOF databases and gas separation scenarios.
Varad Daoo, Jayant K. Singh
wiley   +1 more source

Metal‐Mediated Nitrogen Doping of Carbon Supports Boosts Hydrogen Production from Ammonia

open access: yesAngewandte Chemie, EarlyView.
A virtuous catalytic cycle is established as the carbon support simultaneously tunes electron density and stores atomic nitrogen formed via Ru–N species during ammonia decomposition, resulting in self‐enhancing catalysis. Abstract Ammonia is an attractive hydrogen carrier, yet its practical use is limited by the need for efficient catalytic ...
Thomas J. Liddy   +20 more
wiley   +2 more sources

ChatCFD: A Large Language Model‐Driven Agent for End‐to‐End Computational Fluid Dynamics Automation with Structured Knowledge and Reasoning

open access: yesAdvanced Intelligent Discovery, EarlyView.
Chat computational fluid dynamics (CFD) introduces an large language model (LLM)‐driven agent that automates OpenFOAM simulations end‐to‐end, attaining 82.1% execution success and 68.12% physical fidelity across 315 benchmarks—far surpassing prior systems.
E Fan   +8 more
wiley   +1 more source

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