Results 191 to 200 of about 14,778,763 (297)

A global decline in atmospheric dust during the 21st century. [PDF]

Sci Adv
Gupta AK, Kok JF, Amiridis V, Gkikas A, Rizos K, Proestakis E, Logothetis SA, Marinou E, García-Pando CP.   +8 more
europepmc   +1 more source

Adaptive Foam 3D Printing of Ultralight and Multifunctional Materials

Advanced Engineering Materials, EarlyView.
Adaptive foam 3D printing, enabled by expandable microspheres, imparts cellular structures to thermoplastic and thermosetting polymers, manufactured through a variety of processes including fused filament fabrication, direct ink writing, digital light processing, and inkjet printing.
Nariman Rajabifar, Amir Ameli
wiley   +1 more source

Efficient Automatic Design of a 2D TMD FET via Machine Learning-Assisted TCAD Simulation. [PDF]

Micromachines (Basel)
Shi N, Wei ZJ, Wu T.
europepmc   +1 more source

Discrepancies Between Micro Versus Macroscale Viscoelastic Properties in a Microplastic‐Tissue Composite Model

Advanced Engineering Materials, EarlyView.
This study explores the discrepancies between bulk mechanics and spatial mapping in a tissue‐mimetic hydrogel model. Microplastic particles create localized stiff mechanical microenvironments that are detectable by cellular‐scale nanoindentation but leave bulk properties unchanged as measured by rheology.
Ahron T. Verschleisser, Baptiste Le Roi, Omri Leshem, Joshua M. Grolman   +3 more
wiley   +1 more source

Data-driven machine learning optimisation of silver nanoparticle synthesis. [PDF]

Nanoscale Adv
Carver J, Chew YMJ, Perera S, Chatterjee S.   +3 more
europepmc   +1 more source

Entropy‐Driven Design of Low‐Melting‐Point Alloys via Compositionally Complex Strategy

Advanced Engineering Materials, EarlyView.
Conventional low‐melting‐point alloys (LMPAs) are limited by a narrow compositional space and inherent property trade‐offs. This review presents an entropy‐driven design strategy that overcomes these limitations, ushering in a new class of low‐melting‐point compositionally complex alloys (LMCCAs).
Yinghui Shang, Jianxiang Zhao, Zhaoqi Chen, Hao Gong, Yinsen Yan, Junlei Tao, Yong Yang   +6 more
wiley   +1 more source

Deep Learning to Automate Parameter Extraction and Model Fitting of Two-Dimensional Transistors. [PDF]

Research (Wash D C)
Bennett RKA, Uslu JL, Gault HF, Khan AI, Hoang L, Peña T, Neilson K, Song YS, Zhang Z, Mannix AJ, Pop E.   +10 more
europepmc   +1 more source

Simulation‐Based Analysis of Insert Pull‐Out in Nickel‐Polyurethane Hybrid Foams Using CT‐Derived Geometries

Advanced Engineering Materials, EarlyView.
CT‐based finite element simulations combined with in situ X‐ray computed tomography are used to analyze insert pull‐out in nickel‐coated polymer foams. Despite variations in material parameters, deformation consistently concentrates within a narrow annular region around the insert.
Yannik Bautz, Jonas Fell, Hendrik Jost, Stefan Diebels, Hans‐Georg Herrmann   +4 more
wiley   +1 more source

A Bioinspired Soft Manipulator Based on Wave Spring Structure: Design and Control. [PDF]

Biomimetics (Basel)
Sui D, Zhang Z, Wang T, Zhao S, Zhang Y, Wang X.   +5 more
europepmc   +1 more source

Leveraging Symbolic Artificial Intelligence and Fuzzy Logic for Materials Science: A Review of Methods, Challenges, and Applications to Scarce and Imperfect Experimental Data

Advanced Engineering Materials, EarlyView.
This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani, Mohamed‐Ali Belloum, Laurence Boudet, Valentin Fouillard, Edwin Friedmann, Guillaume Lacharme, Ali Mahmoud, Jean‐Philippe Poli   +7 more
wiley   +1 more source