Results 81 to 90 of about 120,723 (210)

Computational Methods as Part of Scientific Research in Cosmetic Sciences—Are We Using the Opportunity?

Cosmetics
In the field of the cosmetic industry, significant efforts are made to develop methods that are not only cost effective and time effective but are also environmentally friendly and cruelty free.
Laura Krumpholz, Sebastian Polak, Barbara Wiśniowska   +2 more
doaj   +1 more source

Quantitative Structure-Property Relationship Analysis for the Prediction of Propylene Adsorption Capacity in Pure Silicon Zeolites at Various Pressure Levels. [PDF]

ACS Omega, 2022
Zhao L, Zhang Q, He C, Chen Q, Zhang BJ.
europepmc   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Quantitative Structure–Property Relationship for High Flash Point of Ester Oil Molecules and Its Experimental Verification

High Voltage
With the increasing voltage of ester oil‐filled transformers and their application in offshore wind power, developing high‐performance, fire‐resistant esters is crucial for enhancing transformer safety.
Jingwen Zhang, Jian Hao, Per‐Olof Åstrand, Hongwei Li, Ruijin Liao   +4 more
doaj   +1 more source

Evaluation of Quantitative Structure Property Relationship Algorithms for Predicting Plasma Protein Binding in Humans. [PDF]

Comput Toxicol, 2021
Yun YE, Tornero-Velez R, Purucker ST, Chang DT, Edginton AN.   +4 more
europepmc   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

Quantitative Structure-Property Relationship Modelling for the Prediction of Singlet Oxygen Generation by Heavy-Atom-Free BODIPY Photosensitizers*. [PDF]

Chemistry, 2021
Buglak AA, Charisiadis A, Sheehan A, Kingsbury CJ, Senge MO, Filatov MA.   +5 more
europepmc   +1 more source

Electrochemical Evaluation of Compressed Selective Laser Melted AlSi7Mg and AlSi10Mg Alloys in Chloride Environment

Advanced Engineering Materials, EarlyView.
The corrosion performance of AlSi7Mg and AlSi10Mg alloys produced through selective laser melting (SLM) was examined under compressive stress in a chloride environment. Electrochemical analyses, including open‐circuit potential (OCP), potentiodynamic polarization (CPP), and electrochemical impedance spectroscopy (EIS), were complemented by scanning ...
Femi John Akinfolarin, Samson Dare Oguntuyi, Peter Apata Olubambi   +2 more
wiley   +1 more source

Artificial neural network prediction of the psychometric activities of phenylalkylamines using DFT-calculated molecular descriptors [PDF]

Journal of the Serbian Chemical Society, 2010
In the present work, a quantitative structure–activity relationship (QSAR) method was used to predict the psychometric activity values (as mescaline unit, log MU) of 48 phenylalkylamine derivatives from their density functional theory (DFT) calculated ...
MINA HAGHDADI, MOHAMMAD H. FATEMI
doaj  

NFDI MatWerk Ontology (MWO): A BFO‐Compliant Ontology for Research Data Management in Materials Science and Engineering

Advanced Engineering Materials, EarlyView.
This article presents the NFDI‐MatWerk Ontology (MWO), a Basic Formal Ontology‐based framework for interoperable research data management in materials science and engineering (MSE). Covering consortium structures, research data management resources, services, and instruments, MWO enables semantic integration, Findable, Accessible, Interoperable, and ...
Hossein Beygi Nasrabadi, Ebrahim Norouzi, Kostiantyn Hubaiev, Jörg Waitelonis, Harald Sack   +4 more
wiley   +1 more source