Results 131 to 140 of about 1,071,418 (268)

Planar Solid‐State Nanopores Toward Scalable Nanofluidic Integration Based on CMOS Technology

Advanced Engineering Materials, EarlyView.
We present a scalable silicon‐based fabrication strategy for planar solid‐state nanopores to enable their integration with complex nanofluidic systems. Prototype devices demonstrate normal voltage‐current characteristics, good noise performance, and appreciable streaming currents. Our CMOS‐compatible fabrication process offers precise geometric control
Ngan Hoang Pham, Chenyu Wen, Mohammad Hadi Khaksaran, Won‐Yong Lee, Javier Cruz, Klas Hjort, Dongping Wu, Shi‐Li Zhang   +7 more
wiley   +1 more source

Transformer-Type Architecture for Predicting Physicochemical Properties of Cosmetic Emulsions. [PDF]

ACS Omega
Solarte S, Porras A, Pradilla D, Alvarez Solano OA.   +3 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

Developing a National eHealth Enterprise Architecture in Botswana: a digital health case study. [PDF]

Oxf Open Digit Health
Masizana A, Mwangi M, Kisanga R, Ndlovu K, Mosesane B, Moghadassi M, Weissglas F, Matengu S, Chebani T.   +8 more
europepmc   +1 more source

Nuclear Architecture: Perceptions of Architectural Technology

, 2016
In this paper we explore the implications of pluralist curricula for architectural technology. This includes the potential effects on strengthening the identity of the architectural technology profession and the academic development of the discipline. This latter relies, arguably, on research being explicit in CIAT’s eight mandatory threshold standards.
Robertson, Frances, Emmitt, S
openaire   +1 more source

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

KATENA: a verifiable governance architecture for encrypted cloud storage systems. [PDF]

Front Big Data
Rodríguez-Aragón JF, Zato C, Pinto-Santos F, Sánchez-Pravos L.   +3 more
europepmc   +1 more source

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

Zynq-Based Hardware-Software Codesign Architecture for an Intelligent Hyperspectral Camera. [PDF]

Sensors (Basel)
Xie L, Zhang L, Yang F, Lin M, Wang J, Cao S, Zhang C, Liu D, Pan M, Yang J.   +9 more
europepmc   +1 more source

Prediction of Surface Topography Parameters in Direct Laser Interference Patterning of Stainless Steel Using Infrared Monitoring and Convolutional Neural Networks

Advanced Engineering Materials, EarlyView.
This study presents an infrared monitoring approach for direct laser interference patterning (DLIP) combined with a convolutional neural network (CNN). Thermal emission data captured during structuring are used to predict surface topography parameters.
Lukas Olawsky, Marcelo Sallese, Leander Kläber, Clemens Kuhn, Keming Du, Andrés Fabián Lasagni   +5 more
wiley   +1 more source