Thermodynamically‐Driven Phase Engineering and Reconstruction Deduction of Medium‐Entropy Prussian Blue Analogue Nanocrystals DOI
Guangxun Zhang,

Wanchang Feng,

Guangyu Du

и другие.

Advanced Materials, Год журнала: 2025, Номер unknown

Опубликована: Апрель 14, 2025

Abstract Prussian blue analogs (PBAs) are exemplary precursors for the synthesis of a diverse array derivatives.Yet, intricate mechanisms underlying phase transitions in these multifaceted frameworks remain formidable challenge. In this study, machine learning‐guided analysis medium‐entropy PBA system is delineated, utilizing an descriptors that encompass crystallographic phases, structural subtleties, and fluctuations multimetal valence states. By integrating multimodal simulations with experimental validation, thermodynamics‐driven transformation model established accurately predicted critical parameters. A constellation advanced techniques—including atomic force microscopy coupled Kelvin probe individual nanoparticles, X‐ray absorption spectroscopy, operando ultraviolet‐visible situ diffraction, theoretical calculations, multiphysics simulations—substantiated iron oxide@NiCoZnFe‐PBA exhibits both exceptional stability remarkable electrochemical activity. This investigation provides profound insights into transition dynamics polymetallic complexes propels rational design other thermally‐induced derivatives.

Язык: Английский

Leveraging Prompt Engineering in Large Language Models for Accelerating Chemical Research DOI Creative Commons
Feifei Luo, Jinglang Zhang,

Qilong Wang

и другие.

ACS Central Science, Год журнала: 2025, Номер unknown

Опубликована: Апрель 2, 2025

Язык: Английский

Процитировано

0
Spike sorting AI agent DOI Creative Commons
Zuwan Lin, Arnau Marin-Llobet,

Jong‐Min Baek

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Фев. 12, 2025

Spike sorting is a fundamental process for decoding neural activity, involving preprocessing, spike detection, feature extraction, clustering, and validation. However, conventional methods are highly fragmented, labor-intensive, heavily reliant on expert manual curation, limiting their scalability reproducibility. This challenge has become more pressing with advances in recording technology, such as high-density Neuropixels large-scale or flexible electrodes long-term stable over months to years. The volume complexity of these datasets make curation infeasible, requiring an automated scalable solution. Here, we introduce SpikeAgent, multimodal large language model (LLM)-based AI agent that automates standardizes the entire pipeline. Unlike traditional approaches, SpikeAgent integrates multiple LLM backends, coding functions, established algorithms, autonomously performing reasoning-based decision-making real-time interaction intermediate results. It generates interpretable reports, providing transparent justifications each decision, enhancing transparency reliability. We benchmarked against human experts across various demonstrating its versatility ability achieve consistency equal to, even higher than experts. also drastically reduces expertise barrier accelerates validation time by orders magnitude. Moreover, it enables interpretability spiking data, which cannot be achieved any methods. presents paradigm shift processing signals neuroscience brain-computer interfaces, while laying ground agent-augmented science domains.

Язык: Английский

Процитировано

0
Treatment options of nitrogen heterocyclic compounds in industrial wastewater: from fundamental technologies to energy valorization applications and future process design strategies DOI
Chao Ma, Huiqin Zhang, Ziwei Liu

и другие.

Water Research, Год журнала: 2025, Номер unknown, С. 123575 - 123575

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Thermodynamically‐Driven Phase Engineering and Reconstruction Deduction of Medium‐Entropy Prussian Blue Analogue Nanocrystals DOI
Guangxun Zhang,

Wanchang Feng,

Guangyu Du

и другие.

Advanced Materials, Год журнала: 2025, Номер unknown

Опубликована: Апрель 14, 2025

Abstract Prussian blue analogs (PBAs) are exemplary precursors for the synthesis of a diverse array derivatives.Yet, intricate mechanisms underlying phase transitions in these multifaceted frameworks remain formidable challenge. In this study, machine learning‐guided analysis medium‐entropy PBA system is delineated, utilizing an descriptors that encompass crystallographic phases, structural subtleties, and fluctuations multimetal valence states. By integrating multimodal simulations with experimental validation, thermodynamics‐driven transformation model established accurately predicted critical parameters. A constellation advanced techniques—including atomic force microscopy coupled Kelvin probe individual nanoparticles, X‐ray absorption spectroscopy, operando ultraviolet‐visible situ diffraction, theoretical calculations, multiphysics simulations—substantiated iron oxide@NiCoZnFe‐PBA exhibits both exceptional stability remarkable electrochemical activity. This investigation provides profound insights into transition dynamics polymetallic complexes propels rational design other thermally‐induced derivatives.

Язык: Английский

Процитировано

0