Results 131 to 140 of about 2,891,657 (252)

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

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

Quantum Emitters in Hexagonal Boron Nitride: Principles, Engineering and Applications

Advanced Functional Materials, EarlyView.
Quantum emitters in hexagonal boron nitride have emerged as a promising candidate for quantum information science. This review examines the fundamentals of these quantum emitters, including their level structures, defect engineering, and their possible chemical structures.
Thi Ngoc Anh Mai, Md Shakhawath Hossain, Nhat Minh Nguyen, Yongliang Chen, Chaohao Chen, Xiaoxue Xu, Quang Thang Trinh, Toan Dinh, Toan Trong Tran   +8 more
wiley   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

Flux‐Regulated Crystallization of Perovskites Using Machine Learning‐Predicted Solvent Evaporation Rates for X‐Ray Detectors

Advanced Functional Materials, EarlyView.
By integrating machine learning into flux‐regulated crystallization (FRC), accurate prediction of solvent evaporation rates in real time, improving crystallization control and reducing crystal growth variability by over threefold, is achieved. This enhances the reproducibility and quality of perovskite single crystals, leading to reproducible ...
Tatiane Pretto, Sergey Dayneko, Antoine Pavesic Junior, Nicholas Sandor, Alexander Hart, Yuki Haruta, Magdalena Bazalova‐Carter, Marcos J. L. Santos, Makhsud I. Saidaminov   +8 more
wiley   +1 more source

Patterning the Void: Combining L‐Systems with Archimedean Tessellations as a Perspective for Tissue Engineering Scaffolds

Advanced Functional Materials, EarlyView.
This study introduces a novel multi‐scale scaffold design using L‐fractals arranged in Archimedean tessellations for tissue regeneration. Despite similar porosity, tiles display vastly different tensile responses (1–100 MPa) and deformation modes. In vitro experiments with hMSCs show geometry‐dependent growth and activity. Over 55 000 tile combinations
Maria Kalogeropoulou, Kenny A. van Kampen, Pierpaolo Fucile, Carlos Mota, Lorenzo Moroni   +4 more
wiley   +1 more source

Miniature Nanomesh Mechano‐Acoustic Sensor with Wide Linear Dynamic Range, Broad Bandwidth, and Flat Frequency Response

Advanced Functional Materials, EarlyView.
A miniaturized mechano‐acoustic sensor is developed using an electrospun PVDF nanomesh as the diaphragm in a capacitive sensor structure. Unlike conventional nanomesh‐based sensors, it achieves high linear sensitivity, a broad and flat frequency response, and a compact form factor.
Jeng‐Hun Lee, Kang Hyuk Cho, Seojin Yun, Siyoung Lee, Woongji Kim, Yunsik Kim, Wonkyu Moon, Yoonyoung Chung, Kilwon Cho   +8 more
wiley   +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, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

A systematic review of statistical learning in autism spectrum disorder. [PDF]

Mol Autism
Bell RR, Thomas HR, Saffran JR, Eigsti IM.   +3 more
europepmc   +1 more source

Complex Cryptographic and User‐Centric Physically Unclonable Functions Enabled by Strain‐Sensitive Nanocrystals via Selective Ligand Exchange

Advanced Functional Materials, EarlyView.
This study investigates electromechanical PUFs that improve on traditional electric PUFs. The electron transport materials are coated randomly through selective ligand exchange. It produces multiple keys and a key with motion dependent on percolation and strain, and approaches almost ideal inter‐ and intra‐hamming distances.
Seungshin Lim, Hanseok Seo, Taesung Park, Byung Ku Jung, Junhyeok Park, Young Kyun Choi, Dae Yang Oh, Soong Ju Oh   +7 more
wiley   +1 more source