Synthesis, properties, applications, 3D printing and machine learning of graphene quantum dots in polymer nanocomposites

Progress in Materials Science, Journal Year: 2024, Volume and Issue: 144, P. 101282 - 101282

Published: March 12, 2024

This comprehensive review discusses the recent progress in synthesis, properties, applications, 3D printing and machine learning of graphene quantum dots (GQDs) polymer composites. It explores various synthesis methods, highlighting size control surface functionalization GQDs. The unique electronic structure, tunable bandgap, optical properties GQDs are examined. Strategies for incorporating into matrices their effects on mechanical, electrical, thermal, discussed. Applications GQD-based composites optoelectronics, energy storage, sensors, biomedical devices also reviewed. challenges future prospects explored, aiming to provide researchers with a understanding further advancements that should be possible related fields. Moreover, this article new developments technology can benefit from promise composite materials loaded dots, promising class wide range potential applications. In addition discussing GQDs, delves emerging role techniques optimising GQD-polymer materials. Furthermore, it how artificial intelligence data-driven approaches revolutionising design characterisation these nanocomposites, enabling navigate vast parameter space efficiently achieve desired properties. overall aim is build up common platform connecting individual subsections GQD nanocomposites together generate readers.

Language: Английский

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Avoiding common machine learning pitfalls

Patterns, Journal Year: 2024, Volume and Issue: 5(10), P. 101046 - 101046

Published: Aug. 28, 2024

The bigger pictureMachine learning has transitioned from a niche pursuit to one with mass appeal. Thanks the accessibility of modern machine tools, it is now very easy get started in learning, yet this ease use masks underlying complexities doing learning. This, coupled relatively inexperienced community practitioners, led flawed practices, which are reflected issues such as poor reproducibility within machine-learning-based studies.This tutorial aims address problem by educating practitioners about many things that can go wrong when applying and providing guidance on how avoid these pitfalls. However, just part longer-term process needed improve practice, will only meet its ambitions if able become robust trusted applied discipline. Other factors have role play include better standardization, regulation.SummaryMistakes practice commonplace result loss confidence findings products This outlines common mistakes occur using what be done them. While should accessible anyone basic understanding techniques, focuses particular concern academic research, need make rigorous comparisons reach valid conclusions. It covers five stages process: do before model building, reliably build models, robustly evaluate compare models fairly, report results.

Language: Английский

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Secondary Ion Mass Spectral Imaging of Metals and Alloys

Materials, Journal Year: 2024, Volume and Issue: 17(2), P. 528 - 528

Published: Jan. 22, 2024

Secondary Ion Mass Spectrometry (SIMS) is an outstanding technique for Spectral Imaging (MSI) due to its notable advantages, including high sensitivity, selectivity, and dynamic range. As a result, SIMS has been employed across many domains of science. In this review, we provide in-depth overview the fundamental principles underlying SIMS, followed by account recent development instruments. The review encompasses various applications specific instruments, notably static with time-of-flight (ToF-SIMS) as widely used platform Nano large geometry successful We particularly focus on utility in microanalysis imaging metals alloys materials interest. Additionally, discuss challenges big data analysis give examples machine leaning (ML) Artificial Intelligence (AI) effective MSI analysis. Finally, recommend outlook development. It anticipated that situ operando potential significantly enhance investigation enabling real-time examinations material surfaces interfaces during transformations.

Language: Английский

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A Dataset for Investigations of Amine-Impregnated Solid Adsorbent for Direct Air Capture

Scientific Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: May 1, 2025

Language: Английский

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ROBERT: Bridging the Gap Between Machine Learning and Chemistry

Wiley Interdisciplinary Reviews Computational Molecular Science, Journal Year: 2024, Volume and Issue: 14(5)

Published: Sept. 1, 2024

ABSTRACT Beyond addressing technological demands, the integration of machine learning (ML) into human societies has also promoted sustainability through adoption digitalized protocols. Despite these advantages and abundance available toolkits, a substantial implementation gap is preventing widespread incorporation ML protocols computational experimental chemistry communities. In this work, we introduce ROBERT, software carefully crafted to make more accessible chemists all programming skill levels, while achieving results comparable those field experts. We conducted benchmarking using six recent studies in containing from 18 4149 entries. Furthermore, demonstrated program's ability initiate workflows directly SMILES strings, which simplifies generation predictors for common problems. To assess ROBERT's practicality real‐life scenarios, employed it discover new luminescent Pd complexes with modest dataset 23 points, frequently encountered scenario studies.

Language: Английский

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Shape memory liquid crystalline polymers: Stimuli‐responsiveness, advanced technologies, and key applications

Polymers for Advanced Technologies, Journal Year: 2024, Volume and Issue: 35(8)

Published: Aug. 1, 2024

Abstract Liquid crystalline polymers (LCPs) represent a distinct class of materials that have garnered significant interest for their utilisation in diverse industrial and engineering applications. A prominent attribute LCPs is stimuli‐responsiveness. These can undergo deformation subsequently recover original shapes when subjected to external stimuli such as heat, light, electromagnetic fields. The molecular structure consists mesogens flexible tails, mirroring the fundamental mechanism found shape memory polymers. This characteristic positions promising article provides comprehensive review LCPs, focusing on various forms In addition, it delves into application additive manufacturing machine learning technologies context LCPs. Finally, concludes by exploring critical applications materials.

Language: Английский

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Quantum Machine Learning in Materials Prediction: A Case Study on ABO₃ Perovskite Structures

The Journal of Physical Chemistry Letters, Journal Year: 2023, Volume and Issue: 14(31), P. 6940 - 6947

Published: July 27, 2023

Quantum machine learning (QML), ML on quantum computers, offers a promising approach for discovering and screening novel materials. This study introduces hybrid classical-quantum method using variational classifier to identify simple perovskite structures within data set of ABO3 compounds. The model is trained 397 known compounds, with 254 perovskites 143 non-perovskite labeled as +1 -1, respectively. By considering feature correlation eliminating less important features, the QML system achieves an optimal accuracy 88% training 87% unseen test data. These results demonstrate potential in materials science classification tasks, even limited data, leveraging intrinsic properties computation enhance investigation In addition, perspectives applications are discussed.

Language: Английский

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Machine learning assisted material development for lithography-based additive manufacturing of porous alumina ceramics

Open Ceramics, Journal Year: 2024, Volume and Issue: 18, P. 100573 - 100573

Published: March 15, 2024

The transition of applying ceramic additive manufacturing (AM) from prototyping to mass production and monolithic multi-material (MM) components can be supported by continual development in materials processes. Lithography-based (LCM) used for MM printing ceramics with high accuracy introducing different approaches that enable discrete/smooth multidirectional material transitions. Adaptation slurries plays important role the successful co-sintering. Especially co-sintering, shrinkage must adapted so no internal residual stresses occur. Machine learning (ML) offers promising opportunities new optimization processes AM relations between input features output responses. In this article, ML algorithms were prediction porosity alumina samples dependent on including material, printing, thermal processing parameters defect-free porous/dense combination achieved.

Language: Английский

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Classification of battery compounds using structure-free Mendeleev encodings

Journal of Cheminformatics, Journal Year: 2024, Volume and Issue: 16(1)

Published: April 26, 2024

Machine learning is a valuable tool that can accelerate the discovery and design of materials occupying combinatorial chemical spaces. However, prerequisite need for vast amounts training data be prohibitive when significant resources are needed to characterize or simulate candidate structures. Recent results have shown structure-free encoding complex materials, based entirely on compositions, overcome this impediment perform well in unsupervised tasks. In study, we extend exploration supervised classification, show how accurately predict classes material compounds battery applications without time consuming measurement bonding networks, lattices densities. SCIENTIFIC CONTRIBUTION: The comprehensive evaluation encodings classification tasks, including binary multi-class separation, inclusive three classifiers different logic function, measured four metrics curves. applied two sets from computational experimental sources, outcomes visualised using 5 approaches confirms suitability superiority Mendeleev encoding. These methods general accessible source software, provide simple, intuitive interpretable informatics design.

Language: Английский

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Data Checking of Asymmetric Catalysis Literature Using a Graph Neural Network Approach

Molecules, Journal Year: 2025, Volume and Issue: 30(2), P. 355 - 355

Published: Jan. 16, 2025

The range of chemical databases available has dramatically increased in recent years, but the reliability and quality their data are often negatively affected by human-error fidelity. size can make manual curation/checking such sets time consuming; thus, automated tools to help this process highly desirable. Herein, we propose use Graph Neural Networks (GNNs) identifying potential stereochemical misassignments primary asymmetric catalysis literature. Our method relies on an ensemble GNN models predict expected stereoselectivity exemplars for a particular reaction. When majority these do not correlate reported outcome, point is labeled as possible misassignment. Such identified cases few number more easily investigated cause. We demonstrate approach spot literature ketone products resulting from catalytic 1,4-addition organoboron nucleophiles Michael acceptors two different databases, each one using family chiral ligands (bisphosphine diene ligands). results that methodology useful curation medium-sized speeding significantly compared complete curation/checking. In datasets investigated, human expert checking was reduced 2.2% 3.5% total exemplars.

Language: Английский

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