Results 151 to 160 of about 752,870 (293)

Frequency-based decoupling and finite element model updating in vibration of cable-beam systems. [PDF]

J Vib Control, 2022
Jalali MH, Rideout DG.
europepmc   +1 more source

A Topology Optimization Framework for the Inverse Design of Nonlinear Mechanical Metamaterials

Advanced Engineering Materials, EarlyView.
This work uses topology optimization to design unit cells for mechanical metamaterials with a prescribed nonlinear stress–strain response. The framework adds contact and postbuckling modeling to synthesize microstructures for three highly nonlinear responses, including pseudoductile behavior, monostable with snap‐through buckling, and bistable ...
Charlie Aveline, Matthew Santer, Robert Hewson   +2 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

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 New Stochastic Model Updating Method Based on Improved Cross-Model Cross-Mode Technique. [PDF]

Sensors (Basel), 2021
Chen H, Huang B, Tee KF, Lu B.
europepmc   +1 more source

Switchable Supramolecular Adhesive by Tuning Interfacial Bonding and Modulus

Advanced Functional Materials, EarlyView.
The supramolecular adhesive (HyDiP) shows reversible adhesion and recyclability. In the dehydrated state, it is dense, stiff (E ≈445 MPa), transparent, and provides strong bonding with adhesion strengths up to 4.65 MPa. In the hydrated state, it becomes porous, soft (E ≈0.11 MPa), and detaches easily, enabling sustainable high‐strength applications ...
Rumin Fu, Cairong Xiao, Zihao Du, Changhong Linghu, Yongdong Shi, Chuyang Xiang, Mingxin Wan, Guoxin Tan, Limin Lu, Chengyun Ning, K Jimmy Hsia   +10 more
wiley   +1 more source

Coherent Control of Nitrogen Nuclear Spins via the VB−${\rm V}_B^-$‐Center in Hexagonal Boron Nitride

Advanced Functional Materials, EarlyView.
This study demonstrates coherent control of 15N nuclear spins coupled to VB−${\text{V}}_{\text{B}}^{-}$ centers in isotope‐enriched hexagonal boron nitride. Selective addressing via spin‐state mixing enables Rabi driving, quantum gates, and coherence times exceeding 10 μs$\umu{\rm s}$.
Adalbert Tibiássy, Charlie J. Patrickson, Thomas Poirier, James H. Edgar, Bruno Lopez‐Rodriguez, Viktor Ivády, Isaac J. Luxmoore   +6 more
wiley   +1 more source

Conductance‐Dependent Photoresponse in a Dynamic SrTiO3 Memristor for Biorealistic Computing

Advanced Functional Materials, EarlyView.
A nanoscale SrTiO3 memristor is shown to exhibit dynamic synaptic behavior through the interaction of local electrical and global optical signals. Its photoresponse depends quantitatively on the conductance state, which evolves and decays over tunable timescales, enabling ultralow‐power, biorealistic learning mechanisms for advanced in‐memory and ...
Christoph Weilenmann, Hanglin He, Marko Mladenović, Till Zellweger, Kevin Portner, Klemens Bauer, Guillaume Bellec, Mathieu Luisier, Alexandros Emboras   +8 more
wiley   +1 more source

Updated model

This is an update of D31 incorporating the experiences evaluation and feedback from the first round of implementations It includes both the updated overall evidenceinformed whole school mentoring model as well as general guidance to design pedagogically meaningful practices in technologyenriched settings supporting mechanisms stakeholder engagement and
openaire   +1 more source

From Wafers to Electrodes: Transferring Automatic Optical Inspection (AOI) for Multiscale Characterization of Smart Battery Manufacturing

Advanced Functional Materials, EarlyView.
Automat optical inspection (AOI) techniques in semiconductor fabrication can be leveraged in battery manufacturing, enabling scalable detection and analysis of electrode‐ and cell‐level imperfections through AI‐driven analytics and a digital‐twin framework.
Jianyu Li, Ertao Hu, Wei Wei, Feifei Shi
wiley   +1 more source