Results 131 to 140 of about 306,538 (247)
A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science
We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann +8 more
wiley +1 more source
Report from MDE practice: An interview-based evaluation of model-driven engineering uses. [PDF]
Alfraihi H, Lano K.
europepmc +1 more source
In this study, the interplay of dipolar dynamics and ionic charge transport in MOF compounds is investigated. Synthesizing the novel structure CFA‐25 with integrated freely rotating dipolar groups, local and macroscopic effects, including interactions with Cs cations are explored.
Ralph Freund +6 more
wiley +1 more source
Advantages of two quantum programming platforms in quantum computing and quantum chemistry. [PDF]
Wang PH +4 more
europepmc +1 more source
Laser‐Induced Graphene from Waste Almond Shells
Almond shells, an abundant agricultural by‐product, are repurposed to create a fully bioderived almond shell/chitosan composite (ASC) degradable in soil. ASC is converted into laser‐induced graphene (LIG) by laser scribing and proposed as a substrate for transient electronics.
Yulia Steksova +9 more
wiley +1 more source
IGAR: Indonesian government applications review for sentiment analysis dataset. [PDF]
Isnan M, Pardamean B.
europepmc +1 more source
Electroactive Metal–Organic Frameworks for Electrocatalysis
Electrocatalysis is crucial in sustainable energy conversion as it enables efficient chemical transformations. The review discusses how metal–organic frameworks can revolutionize this field by offering tailorable structures and active site tunability, enabling efficient and selective electrocatalytic processes.
Irena Senkovska +7 more
wiley +1 more source
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source
SI‐bioATRP in Mesoporous Silica for Size‐Exclusion Driven Local Polymer Placement
An enzyme‐catalyzed surface‐initiated atom transfer radical polymerization (SI‐bioATRP) of an anionic monomer within mesoporous silica particles, using hemoglobin as a catalyst, allows for controlling the location of the formed polymer via size‐exclusion effects between the nanopores and the biomacromolecules, thereby opening routes to functional ...
Oleksandr Wondra +8 more
wiley +1 more source

