Results 31 to 40 of about 33,884 (245)

Machine Learning for Green Solvents: Assessment, Selection and Substitution

Advanced Science, EarlyView.
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, Janhavi Nistane, Abhishek Sose, Harikrishna Sahu, Rampi Ramprasad   +4 more
wiley   +1 more source

Dorsal Raphe VIP Neurons Are Critical for Survival‐Oriented Vigilance

Advanced Science, EarlyView.
DRNVIP neurons in mice and primates are strategically positioned to influence the central extended amygdala via feedback loops. They regulate the excitability of PKC‐δ neurons in the ovBNST and CeA through glutamate release. Their ablation heightens activity in these regions, disrupts active‐phase sleep architecture, enhances risk assessment behaviors ...
Adriane Guillaumin, Emma Perrot, Thibault Dhellemmes, Laura Boi, Daniel De Castro Medeiros, Christelle Glangetas, Sylvie Dumas, Sandra Dovero, Nathalie Biendon, Elodie Ladeveze, Maëlle Hardel, Marc Landry, Erwan Bezard, Jérôme Baufreton, Gilberto Fisone, François Georges   +15 more
wiley   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

From Microscale to Nanoscale Shadow Electrochemiluminescence Microscopy

Angewandte Chemie, EarlyView.
In this research we report on the label‐free shadow electrochemiluminescence (shadow ECL) microscopy of microscale and nanoscale objects. By systematically investigating various influencing factors—including optical configuration, electrode activity, frame averaging, exposure time, and particle arrangement—we further confirm the nano‐imaging potential ...
Xiaodan Gou, Hanna Manko, Jasmina Vidic, Laurent Cognet, Jun‐Jie Zhu, Neso Sojic   +5 more
wiley   +2 more sources

Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review

Advanced Intelligent Discovery, EarlyView.
Feature selection combined with machine learning and high‐throughput experimentation enables efficient handling of high‐dimensional datasets in emerging photovoltaics. This approach accelerates material discovery, improves process optimization, and strengthens stability prediction, while overcoming challenges in data quality and model scalability to ...
Jiyun Zhang, Jiayi Tan, Qizhen Song, Tian Du, Jens Hauch, Christoph J. Brabec   +5 more
wiley   +1 more source

Iron Catalyzed Aryl–Aryl Kumada Cross‐Coupling: A Mechanistic and Computational Investigation

Angewandte Chemie, EarlyView.
Despite intense interest, the mechanism of iron‐catalyzed aryl–aryl cross‐coupling remains poorly understood. Combining Mössbauer spectroscopy, kinetics analysis, and DFT computations these studies reveal a novel Fe(I)/Fe(II)/Fe(III) catalytic for aryl–aryl cross‐coupling mediated by an Fe(II) PCNHCP Pincer complex. These findings close a key knowledge
Jatin Panda, Magali Gimeno, Amrita Gogoi, Zeqing Chen, Subhash Garhwal, Laura Levy, Alexander Kaushansky, Natalia Fridman, Jos Briggs‐Pritchard, Renana Gershoni‐Poranne, Michael L. Neidig, Graham de Ruiter   +11 more
wiley   +2 more sources

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Simulation of subsurface imaging for remote sensing and buried object detection from airborne platforms

European Journal of Remote Sensing, 2019
This paper addresses the problem of efficient simulations of the signal backscattered from buried structures. The proposed modelling of subsurface soil structure is based on a geologically – and hydrologically-motivated approach by using of real ...
R Kedzierawski, J-M Le Caillec, W Czarnecki   +2 more
doaj   +1 more source

Comparison of the Pavement Layers Thickness Measured by Georadar and Conventional Methods – Examples From Croatia

The Baltic Journal of Road and Bridge Engineering, 2012
Thickness of the pavement structure layers represents an important data for the implementation into the database of roads already constructed, during the pavement strengthening design, particularly if the appraisal of the bearing capacity of the pavement
Marko Ožbolt, Tatjana Rukavina, Josipa Domitrović   +2 more
doaj   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source