Results 151 to 160 of about 5,220 (217)

Flow characteristics and regime transitions in fluidization systems of Group C+ particles

open access: yesAIChE Journal, EarlyView.
Abstract Fine and ultrafine particles are attractive for fluidized bed reactors because their high surface area benefits catalytic processes, but their cohesive nature complicates regime characterization. Nanoparticle modulation improves the flowability of Group C powders, yet their bubbling to turbulent transition remains unclear.
Yue Song   +3 more
wiley   +1 more source

A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons

open access: yesAdvanced Intelligent Discovery, EarlyView.
We benchmark six large atomistic foundation models on 2429 crystalline materials for phonon transport properties. The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces.
Md Zaibul Anam   +5 more
wiley   +1 more source

Parity Metamaterials and Dynamic Acoustic Mimicry. [PDF]

open access: yesResearch (Wash D C)
Shi J   +6 more
europepmc   +1 more source

Structure and Spectroscopic Characterisation of Phenanthroline‐Based Iodobismuthate(III) Complexes Utilised for Raw Acoustic Signal Classification

open access: yesAdvanced Intelligent Discovery, EarlyView.
Memristors based on trimethylsulfonium (phenanthroline)tetraiodobismuthate have been utilised as a nonlinear node in a delayed feedback reservoir. This system allowed an efficient classification of acoustic signals, namely differentiation of vocalisation of the brushtail possum (Trichosurus vulpecula).
Ewelina Cechosz   +4 more
wiley   +1 more source

Uncovering the acoustic ecology of sympatric coral-dwelling fish with portable audio-video arrays. [PDF]

open access: yesSci Rep
Azofeifa-Solano JC   +6 more
europepmc   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

open access: yesAdvanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong   +5 more
wiley   +1 more source

Ultrasound in medicine from 2014 to 2024: A bibliometric review. [PDF]

open access: yesMedicine (Baltimore)
Chenhao Z   +5 more
europepmc   +1 more source

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