Bayesian inference for peak feature extraction and prediction of material property in X-ray diffraction data DOI Creative Commons
Ryo Murakami, Taisuke Sasaki, Hideki Yoshikawa

et al.

Science and Technology of Advanced Materials Methods, Journal Year: 2024, Volume and Issue: 4(1)

Published: Aug. 5, 2024

To advance the development of materials through data-driven scientific methods, appropriate methods for building machine learning (ML)-ready feature tables from measured and computed data must be established. In development, X-ray diffraction (XRD) is an effective technique analysing crystal structures other microstructural features that have information can explain material properties. Therefore, fully automated extraction peak XRD without bias analyst a significant challenge. This study aimed to establish efficient robust approach constructing follow ML standards (ML-ready) data. We challenge in situation where only function profile known priori, knowledge measurement or structure factor. utilized Bayesian estimation extract subsequently performed regression analysis with selection predict property. The proposed method focused on tops peaks within localized regions interest (ROIs) extracted quickly accurately. process facilitated rapid extracting major construction ML-ready table. then applied linear maximum energy product (BH)max, using as explanatory variable. outcomes yielded reasonable results. Thus, findings this indicated 004 height area were important predicting (BH)max.

Language: Английский

A review of chalcogenide-based perovskites as the next novel materials: Solar cell and optoelectronic applications, catalysis and future perspectives DOI
George G. Njema, Joshua K. Kibet

Next Nanotechnology, Journal Year: 2024, Volume and Issue: 7, P. 100102 - 100102

Published: Sept. 11, 2024

Language: Английский

Citations

22
A review on the recovery of cellulose, lignin, and hemicellulose biopolymers from the same source of lignocellulosic biomass – Methodology, characterization and applications DOI

Alusani Manyatshe,

Linda Lunga Sibali

Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 70, P. 107037 - 107037

Published: Jan. 23, 2025

Language: Английский

Citations

4
Optimization of ultrasound-assisted extraction of faba bean protein isolate: Structural, functional, and thermal properties. Part 2/2 DOI Creative Commons
Abraham Badjona, Robert Bradshaw, Caroline Millman

et al.

Ultrasonics Sonochemistry, Journal Year: 2024, Volume and Issue: 110, P. 107030 - 107030

Published: Aug. 15, 2024

Environmental concerns linked to animal-based protein production have intensified interest in sustainable alternatives, with a focus on underutilized plant proteins. Faba beans, primarily used for animal feed, offer high-quality source promising bioactive compounds food applications. This study explores the efficacy of ultrasound-assisted extraction under optimal conditions (123 W power, 1:15 g/mL solute/solvent ratio, 41 min sonication, 623 mL total volume) isolate faba bean (U-FBPI). The method achieved yield 19.75 % and content 92.87 %, outperforming control method's 16.41 89.88 %. Electrophoretic analysis confirmed no significant changes primary structure U-FBPI compared control. However, Fourier-transform infrared spectroscopy revealed modifications secondary due ultrasound treatment. demonstrated superior water oil holding capacities isolate, although its foaming capacity was reduced by ultrasound. Thermal indicated minimal impact protein's thermal properties applied conditions. research highlights potential improving functional isolates, presenting viable approach advancing plant-based contributing consumption.

Language: Английский

Citations

12
Plant-Based Synthesis, characterization Approaches, Applications and Toxicity of Silver Nanoparticles: A Comprehensive Review DOI

Shijith Thomas,

Richard Gonsalves,

Jomy Jose

et al.

Journal of Biotechnology, Journal Year: 2024, Volume and Issue: 394, P. 135 - 149

Published: Aug. 17, 2024

Language: Английский

Citations

11
Crystal Structure Assignment for Unknown Compounds from X-ray Diffraction Patterns with Deep Learning DOI
Litao Chen, Bingxu Wang, Wentao Zhang

et al.

Journal of the American Chemical Society, Journal Year: 2024, Volume and Issue: 146(12), P. 8098 - 8109

Published: March 13, 2024

Determining the structures of previously unseen compounds from experimental characterizations is a crucial part materials science. It requires step searching for structure type that conforms to lattice unknown compound, which enables pattern matching process characterization data, such as X-ray diffraction (XRD) patterns. However, this procedure typically places high demand on domain expertise, thus creating an obstacle computer-driven automation. Here, we address challenge by leveraging deep-learning model composed union convolutional residual neural networks. The accuracy demonstrated dataset over 60,000 different 100 types, and additional categories can be integrated without need retrain existing We also unravel operation black box highlight way in resemblance between compound quantified based both local global characteristics XRD This computational tool opens new avenues automating analysis unearthed high-throughput experimentation.

Language: Английский

Citations

9
Fabrication of chitosan-coated calcium tungstate (CaWO4/Chitosan) and its Antioxidant, antimicrobial and phocatalytic activity DOI
T. Gomathi,

V. Priyadharshini,

Mohammed Mujahid Alam

et al.

Inorganic Chemistry Communications, Journal Year: 2024, Volume and Issue: 163, P. 112300 - 112300

Published: March 13, 2024

Language: Английский

Citations

9
A mini review on the applications of artificial intelligence (AI) in surface chemistry and catalysis DOI

Faisal Al-Akayleh,

Ahmed S.A. Ali Agha,

Rami A. Abdel Rahem

et al.

Tenside Surfactants Detergents, Journal Year: 2024, Volume and Issue: 61(4), P. 285 - 296

Published: April 29, 2024

Abstract This review critically analyzes the incorporation of artificial intelligence (AI) in surface chemistry and catalysis to emphasize revolutionary impact AI techniques this field. The current examines various studies that using techniques, including machine learning (ML), deep (DL), neural networks (NNs), catalysis. It reviews literature on application models predicting adsorption behaviours, analyzing spectroscopic data, improving catalyst screening processes. combines both theoretical empirical provide a comprehensive synthesis findings. demonstrates applications have made remarkable progress properties nanostructured catalysts, discovering new materials for energy conversion, developing efficient bimetallic catalysts CO 2 reduction. AI-based analyses, particularly advanced NNs, provided significant insights into mechanisms dynamics catalytic reactions. will be shown plays crucial role by significantly accelerating discovery enhancing process optimization, resulting enhanced efficiency selectivity. mini-review highlights challenges data quality, model interpretability, scalability, ethical, environmental concerns AI-driven research. importance continued methodological advancements responsible implementation

Language: Английский

Citations

8
Synthesis, crystal structure, DFT studies, molecular docking, of 2-amino-6-methoxy-4-(4-nitrophenyl)-4H-benzo[h]chromene-3-carbonitrile as tyrosinase inhibitor DOI
Al-Anood M. Al-Dies,

Ashraf Hassan Fekry Abd El‐Wahab,

Abdullah Alamri

et al.

Journal of Molecular Structure, Journal Year: 2024, Volume and Issue: unknown, P. 140289 - 140289

Published: Oct. 1, 2024

Language: Английский

Citations

4
Mechanistic Insights and Technical Challenges in Sulfur-Based Batteries: A Comprehensive In Situ/Operando Monitoring Toolbox DOI Creative Commons
Jing Yu,

Ivan Pinto‐Huguet,

Chaoyue Zhang

et al.

ACS Energy Letters, Journal Year: 2024, Volume and Issue: 9(12), P. 6178 - 6214

Published: Dec. 4, 2024

Batteries based on sulfur cathodes offer a promising energy storage solution due to their potential for high performance, cost-effectiveness, and sustainability. However, commercial viability is challenged by issues such as polysulfide migration, volume changes, uneven phase nucleation, limited ion transport, sluggish redox kinetics. Addressing these challenges requires insights into the structural, morphological, chemical evolution of phases, associated changes internal stresses, diffusion within battery. Such can only be obtained through real-time reaction monitoring battery's operational environment, supported molecular dynamics simulations advanced artificial intelligence-driven data analysis. This review provides an overview

Language: Английский

Citations

4
DiffractGPT: Atomic Structure Determination from X-ray Diffraction Patterns Using a Generative Pretrained Transformer DOI Creative Commons
Kamal Choudhary

The Journal of Physical Chemistry Letters, Journal Year: 2025, Volume and Issue: unknown, P. 2110 - 2119

Published: Feb. 20, 2025

Crystal structure determination from powder diffraction patterns is a complex challenge in materials science, often requiring extensive expertise and computational resources. This study introduces DiffractGPT, generative pretrained transformer model designed to predict atomic structures directly X-ray (XRD) patterns. By capturing the intricate relationships between crystal structures, DiffractGPT enables fast accurate inverse design. Trained on thousands of their simulated XRD JARVIS-DFT data set, we evaluate across three scenarios: (1) without chemical information, (2) with list elements, (3) an explicit formula. The results demonstrate that incorporating information significantly enhances prediction accuracy. Additionally, training process straightforward fast, bridging gaps computational, experimental communities. work represents significant advancement automating determination, offering robust tool for data-driven discovery

Language: Английский

Citations

0