Advances in Ionic Thermoelectrics: From Materials to Devices

Advanced Energy Materials, Journal Year: 2023, Volume and Issue: 13(9)

Published: Jan. 20, 2023

Abstract As an extended member of the thermoelectric family, ionic thermoelectrics (i‐TEs) exhibit exceptional Seebeck coefficients and applicable power factors, as a result have triggered intensive interest promising energy conversion technique to harvest exploit low‐grade waste heat (<130 °C). The last decade has witnessed great progress in i‐TE materials devices; however, there are ongoing disputes about inherent fundamentals working mechanisms i‐TEs, comprehensive overview this field is required urgently. In review, prominent effects, which set ground for materials, or more precisely, thermo‐electrochemical systems, first elaborated. Then, TE performance, capacitance capability, mechanical properties such system‐based followed by critical discussion on how manipulate these factors toward higher figure‐of‐merit, examined. After that, prevalent molding methods assembling into devices summarized. To conclude, several evaluation criteria proposed quantitatively illustrate promise practical applications. It therefore clarified if recent trend developing i‐TEs can continue, recycling landscape will be significantly altered.

Language: Английский

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Convergence of nanotechnology and artificial intelligence in the fight against liver cancer: a comprehensive review

Discover Oncology, Journal Year: 2025, Volume and Issue: 16(1)

Published: Jan. 22, 2025

Abstract Liver cancer is one of the most challenging malignancies, often associated with poor prognosis and limited treatment options. Recent advancements in nanotechnology artificial intelligence (AI) have opened new frontiers fight against this disease. Nanotechnology enables precise, targeted drug delivery, enhancing efficacy therapeutics while minimizing off-target effects. Simultaneously, AI contributes to improved diagnostic accuracy, predictive modeling, development personalized strategies. This review explores convergence liver treatment, evaluating current progress, identifying existing research gaps, discussing future directions. We highlight how AI-powered algorithms can optimize nanocarrier design, facilitate real-time monitoring efficacy, enhance clinical decision-making. By integrating nanotechnology, clinicians achieve more accurate patient stratification personalization, ultimately improving outcomes. holds significant promise for transforming therapy into a individualized, efficient process. However, data privacy, regulatory hurdles, need large-scale validation remain. Addressing these issues will be essential fully realizing potential technologies oncology.

Language: Английский

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Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm

SmartMat, Journal Year: 2025, Volume and Issue: 6(1)

Published: Jan. 9, 2025

ABSTRACT Machine learning (ML), material genome, and big data approaches are highly overlapped in their strategies, algorithms, models. They can target various definitions, distributions, correlations of concerned physical parameters given polymer systems, have expanding applications as a new paradigm indispensable to conventional ones. Their inherent advantages building quantitative multivariate largely enhanced the capability scientific understanding discoveries, thus facilitating mechanism exploration, prediction, high‐throughput screening, optimization, rational inverse designs. This article summarizes representative progress recent two decades focusing on design, preparation, application, sustainable development materials based exploration key composition–process–structure–property–performance relationship. The integration both data‐driven insights through ML deepen fundamental discover novel is categorically presented. Despite construction application robust models, strategies algorithms deal with variant tasks science still rapid growth. challenges prospects then We believe that innovation will thrive along approaches, from efficient design applications.

Language: Английский

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Application of machine learning in adsorption energy storage using metal organic frameworks: A review

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 111, P. 115363 - 115363

Published: Jan. 13, 2025

Language: Английский

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Anomalous thermal transport under high pressure in boron arsenide

Nature, Journal Year: 2022, Volume and Issue: 612(7940), P. 459 - 464

Published: Nov. 23, 2022

Language: Английский

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Enhancing precision in PANI/Gr nanocomposite design: robust machine learning models, outlier resilience, and molecular input insights for superior electrical conductivity and gas sensing performance

Journal of Materials Chemistry A, Journal Year: 2023, Volume and Issue: 12(4), P. 2209 - 2236

Published: Dec. 11, 2023

This study employs various machine learning algorithms to model the electrical conductivity and gas sensing responses of polyaniline/graphene (PANI/Gr) nanocomposites based on a comprehensive dataset gathered from over 100 references.

Language: Английский

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Triboelectric Nanogenerators with Machine Learning for Internet of Things

Advanced Materials Technologies, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 3, 2024

Abstract The development of the Internet Things (IoT) indicates that humankind has entered a new intelligent era “Internet Everything”. Thanks to characteristics low‐cost, diverse structure, and high energy conversion efficiency, self‐powered sensing systems, which are based on Triboelectric Nanogenerator (TENG), demonstrate great potential in field IoT. In order solve challenges TENG signal processing, such as noise nonlinear relations, Machine Learning (ML), is an efficient mature data processing tool, widely applied for efficiently large complex output generated by system. This review summarizes analyzes adaptation different algorithms their advantages disadvantages at beginning, provides reference selection TENG. More importantly, application introduced multiple scenarios, including health monitoring, fault detection, human‐computer interaction. Finally, limitations trend integration ML proposed classification promote future IoT era.

Language: Английский

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Ballistic Transport from Propagating Vibrational Modes in Amorphous Silicon Dioxide: Thermal Experiments and Atomistic-Machine Learning Modeling

Materials Today Physics, Journal Year: 2025, Volume and Issue: unknown, P. 101659 - 101659

Published: Jan. 1, 2025

Language: Английский

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Advancing Thermal Management Technology for Power Semiconductors through Materials and Interface Engineering

Accounts of Materials Research, Journal Year: 2025, Volume and Issue: unknown

Published: April 8, 2025

Language: Английский

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Molecular perspective and engineering of thermal transport and thermoelectricity in polymers

Journal of Materials Chemistry A, Journal Year: 2024, Volume and Issue: 12(18), P. 10614 - 10658

Published: Jan. 1, 2024

This review highlights molecular and nanoscale engineering of electrically insulating semiconducting polymers for improved heat transport thermoelectricity.

Language: Английский

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