Accelerated Electrosynthesis Development Enabled by High-Throughput Experimentation

Synthesis, Год журнала: 2023, Номер 55(18), С. 2817 - 2832

Опубликована: Апрель 12, 2023

Abstract Electrochemical synthesis has recently emerged as an environmentally benign method for synthesizing value-added fine chemicals. Its unique reactivity attracted significant interests of synthetic chemists to develop new redox chemistries. However, compared conventional chemistry, the increased complexity caused by electrode materials, supporting electrolytes, and setup configurations create obstacles efficient reaction discovery optimization. The recent increasing adoption high-throughput experimentation (HTE) in chemistry significantly expedites development. Considering potential implementing HTE electrosynthesis tackle challenges parameter space, this short review aims at providing advances technology electrosynthesis, including electrocatalysts screening, device miniaturization, electroanalytical methods, artificial intelligence, system integration. discussed contents also cover some topics electrochemistry areas other than hoping spark inspirations readers use interdisciplinary techniques solve electrochemistry. 1 Introduction 2 Parallelized Reaction Screening 3 High-Throughput Electrocatalysts 4 Miniaturization Devices 5 Analytical Methods Electrosynthesis 6 Artificial Intelligence 7 Integrated Systems 8 Conclusion Outlook

Язык: Английский

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Vacancy-induced phonon localization in boron arsenide using a unified neural network interatomic potential

Cell Reports Physical Science, Год журнала: 2023, Номер 5(1), С. 101760 - 101760

Опубликована: Дек. 29, 2023

Boron arsenide, considered an ideal semiconductor, inevitably introduces arsenic defects during crystal growth. Here, we develop a unified neural network interatomic potential with quantum-mechanical precision that accurately describes phonon transport properties in both perfect and defective boron arsenides. Through molecular dynamics simulations, quantitatively explore the degree of localization arsenide caused by vacancies. We confirm this primarily affects vibration modes within frequency range 2.0–4.0 THz, which is challenge for conventional first-principles approaches. In addition, examine fluctuation heat flux autocorrelation function, reveals extent phase disruption resulting from voids lattice anharmonicity more fundamental perspective. Our study highlights applicability simulations conjunction systems, laying theoretical groundwork engineering real semiconductor crystals.

Язык: Английский

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Sound Absorption Performance of Ultralight Honeycomb Sandwich Panels Filled with “Network” Fibers—Juncus effusus

Polymers, Год журнала: 2024, Номер 16(13), С. 1953 - 1953

Опубликована: Июль 8, 2024

This study investigates lightweight and efficient candidates for sound absorption to address the growing demand sustainable eco-friendly materials in noise attenuation. Juncus effusus (JE) is a natural fiber known its unique three-dimensional network, providing viable filler enhanced honeycomb panels. Microperforated-panel (MPP) absorbers incorporating JE fillers were fabricated designed, focusing on optimizing absorber designs by varying densities, geometrical arrangements, MPP parameters. At optimal filling MPP-type structures filled with fibers achieved high reduction coefficients (NRC) of 0.5 0.7 at 20 mm 50 thicknesses, respectively. Using an analytical model artificial neural network (ANN) model, characteristics these successfully predicted. demonstrates potential improving mitigation strategies across different industries, offering more solutions construction transportation.

Язык: Английский

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Advancements and prospects of deep learning in biomaterials evolution

Cell Reports Physical Science, Год журнала: 2024, Номер 5(9), С. 102116 - 102116

Опубликована: Июль 25, 2024

In recent decades, significant strides have been made in advancing biomaterials for biomedical applications. Ideal necessitate suitable mechanical properties, excellent biocompatibility, and specific bioactivities. However, the design preparation of materials with these essential characteristics pose formidable challenges, persisting as issues field. The development optimization high-performance biomaterials, along construction composites hybrids diverse biofunctions, present promising strategies enhancing therapeutic diagnostic procedures. reliance on traditional "trial error" methods acquiring a substantial volume experimental data proves to be laborious, time consuming, unreliable. An emerging approach involves successful application artificial intelligence (AI), specifically deep learning (DL), investigate optimize manufacturing techniques various biomaterials. DL, an automated intelligent tool within AI domain, finds widespread devising, analyzing, optimizing different Through "experiment-AI" technique, DL predicts potential feature information performance showcasing remarkable biomaterial research development. This review comprehensively explores DL-based technologies field, emphasizing cutting-edge advantages providing insights recommendations enhance efficacy such approaches

Язык: Английский

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Inverse Design of Microstructures Using Conditional Continuous Normalizing Flows

Acta Materialia, Год журнала: 2024, Номер unknown, С. 120704 - 120704

Опубликована: Дек. 1, 2024

Язык: Английский

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