Results 151 to 160 of about 527,552 (354)
Machine Learning for Green Solvents: Assessment, Selection and Substitution
Environmental regulations have intensified demand for green solvents, but discovery is limited by Solvent Selection Guides (SSGs) that quantify solvent sustainability. Training a machine learning model on GlaxoSmithKline SSG, a database of sustainability metrics for 10,189 solvents, GreenSolventDB is developed. Integrated with Hansen solubility metrics,
Rohan Datta +4 more
wiley +1 more source
Itch‐induced tick removal (IITR): An acquired neuroimmune mechanism, itch‐induced tick removal, develops after repeated tick exposure, mobilizing T cells and macrophages at the tick bite site to trigger a rapid scratching response that facilitates timely tick removal within a critical window that precedes the transmission of many tick‐borne pathogens ...
Johannes S. P. Doehl +27 more
wiley +1 more source
This review explores inorganic metal oxides and metal salt nanoparticles templated porous carbons, highlighting their synthesis, structural features, and performance in energy and environmental applications. It critically compares template types, porosity control, and functional outcomes across recent literature.
Gurwinder Singh +8 more
wiley +1 more source
Quantification of chemical contaminants in the paper and board fractions of municipal solid waste.
K. Pivnenko +4 more
semanticscholar +1 more source
Delivery of Pleckstrin‐Homology Domains Suppresses PI3K/Akt Signaling and Breast Cancer Metastasis
Current therapies curb tumor growth but not metastasis. Obscurin, a giant metastasis suppressor lost in breast cancer, restrains PI3K/Akt signaling but is impractical to restore. We deploy a mini‐obscurin, comprising the obscurin‐PH‐domain, which sequesters PI3K‐p85, potently suppressing invasion and metastasis.
Matthew Eason +12 more
wiley +1 more source
The Green Cath Lab Challenge: Environmental Sustainability in Interventional Cardiology. [PDF]
Pappa D +4 more
europepmc +1 more source
An Integrated NLP‐ML Framework for Property Prediction and Design of Steels
This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju +5 more
wiley +1 more source
Determinants of waste generation in operating rooms. [PDF]
Steinmeier A +5 more
europepmc +1 more source
Chemical composition of material fractions in Danish household waste.
Christian Riber +2 more
semanticscholar +1 more source

