Results 181 to 190 of about 1,595,436 (304)

Stretching the Printability Metric in Direct‐Ink Writing with Highly Extensible Yield‐Stress Fluids

Advanced Functional Materials, EarlyView.
This study introduces “drawability” as a new metric for assessing printability in direct‐ink writing, focusing on gap‐spanning performance and speed robustness. By designing yield‐stress fluids with high extensibility, we demonstrate that extensional strain‐to‐break significantly enhances printability.
Chaimongkol Saengow, Samya Sen, Joaquin Yus, Eliza E. Lovrich, Amanda G. Hoika, Kelly M. Chang, Arielle A. Pfeil, Nellie Haug, Amy J. Wagoner Johnson, Randy H. Ewoldt   +9 more
wiley   +1 more source

Large Language Model Adaptation Strategies in Speech-Based Cognitive Screening: Systematic Evaluation. [PDF]

JMIR AI
Taherinezhad F, Momeni Nezhad MJ, Karimi S, Rashidi S, Zolnour A, Dadkhah M, Haghbin Y, Azadmaleki H, Zolnoori M.   +8 more
europepmc   +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

A Condition-Aware Shading Domain-Adaptive Framework for Robust Chlorophyll Inversion Across Shade Managements in <i>Hopea hainanensis</i>. [PDF]

Plants (Basel)
Chen L, Yang X, Dong X, Lin L, Shi M, Chen F, Huang C, Yu H, Yuan Y, Han M.   +9 more
europepmc   +1 more source

Modulating Two‐Photon Absorption in a Pyrene‐Based MOF Series: An In‐Depth Investigation of Structure–Property Relationships

Advanced Functional Materials, EarlyView.
This study investigates H4TBAPy‐based metal–organic frameworks (MOFs) ‐ NU‐1000, NU‐901, SrTBAPy, and BaTBAPy ‐ for multiphoton absorption (MPA) performance. It observes topology‐dependent variations in the 2PA cross‐section, with BaTBAPy exhibiting the highest activity.
Simon N. Deger, Helmy Pacheco Hernandez, Yang Cui, Hongxing Hao, Vanessa Ramm, David C. Mayer, Khai‐Nghi Truong, Roland A. Fischer, Jürgen Hauer, Mariana Kozlowska, Alexander Pöthig   +10 more
wiley   +1 more source

Efficient and Scalable Tuning of Continuous Impedance Control for Powered Knee Prostheses. [PDF]

IEEE Robot Autom Lett
Hong W, Naseri A, Huang HH.
europepmc   +1 more source

Demonstration of an All‐Optical AND Gate Mediated by Photochromic Molecules

Advanced Functional Materials, EarlyView.
A logic AND gate that runs on photons is demonstrated. It relies on two spatially separated photochromic molecules that work in tandem. Abstract The realization of a photonic logic AND gate, i.e. a logic AND gate that runs on photons rather than electrons, and where all steps are controlled by light, is demonstrated. In a proof‐of‐principle experiment,
Heyou Zhang, Max Gießübel, Pankaj Dharpure, Andrea Albert, Vira Niestierkina, Paul Mulvaney, Mukundan Thelakkat, Jürgen Köhler   +7 more
wiley   +1 more source

A Novel Convolutional Neural Network for Explainable Diabetic Retinopathy Detection and Grade Identification. [PDF]

Sensors (Basel)
Correra S, Sorgente V, Cesarelli M, Martinelli F, Santone A, Mercaldo F.   +5 more
europepmc   +1 more source

Selective Benzene Capture by Metal‐Organic Frameworks

Advanced Functional Materials, EarlyView.
Metal‐organic frameworks (MOFs) hold significant potential for capturing benzene from air emissions and hydrocarbon mixtures in liquid phases. This capability stems from their precisely engineered structures, versatile chemistries, and diverse binding interactions.
Zongsu Han, Ankit Mondal, Jiatong Huo, Yihao Yang, Hong‐Cai Zhou   +4 more
wiley   +1 more source

Fine-Tuning Unifies Foundational Machine-Learned Interatomic Potential Architectures at <i>ab initio</i> Accuracy. [PDF]

J Phys Chem Lett
Hänseroth J, Flötotto A, Qaisrani MN, Dreßler C.   +3 more
europepmc   +1 more source