Results 131 to 140 of about 37,180 (284)
Advanced Functional Materials, EarlyView.This study examines how pore shape and manufacturing‐induced deviations affect the mechanical properties of 3D‐printed lattice materials with constant porosity. Combining µ‐CT analysis, FEM, and compression testing, the authors show that structural imperfections reduce stiffness and strength, while bulk material inhomogeneities probably enhance ...
Oliver Walker +5 more
wiley +1 more source
Teaching programming and computational thinking in early childhood education: a case study of content knowledge and pedagogical knowledge. [PDF]
Front Psychol, 2023 Zeng Y, Yang W, Bautista A.
europepmc +1 more source
Reevaluating the Activity of ZIF‐8 Based FeNCs for Electrochemical Ammonia Production
Advanced Functional Materials, EarlyView.Though receiving much attention, the field of electrochemical nitrogen reduction reaction (eNRR) to ammonia is marked by doubts about whether this reaction is possible in aqueous media. This work sheds light on this question for iron single‐atom on N‐doped carbon (FeNC) catalysts—a class of well‐known catalysts that is also worth testing for the sister
Caroline Schneider +6 more
wiley +1 more source
A framework for assessing computational thinking skills in the physics classroom: study on cognitive test development. [PDF]
SN Soc Sci, 2023 Zakwandi R, Istiyono E.
europepmc +1 more source
MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance
Advanced Functional Materials, EarlyView.Metal‐organic frameworks (MOFs) and covalent organic frameworks (COFs) hold promise for advanced electronics. However, discrepancies in reported electrical conductivities highlight the importance of measurement methodologies. This review explores intrinsic charge transport mechanisms and extrinsic factors influencing performance, and critically ...
Jonas F. Pöhls, R. Thomas Weitz
wiley +1 more source
Effect of Reverse Engineering Pedagogy on Primary School Students' Computational Thinking Skills in STEM Learning Activities. [PDF]
J Intell, 2023 Liu X, Wang X, Xu K, Hu X.
europepmc +1 more source
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
Advanced Functional Materials, EarlyView.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
Computational Thinking: A Disciplinary Perspective [PDF]
, 2021 Peter J. Denning, Matti Tedre
openalex +1 more source