Machine learning applications for electrospun nanofibers: a review

Journal of Materials Science, Год журнала: 2024, Номер 59(31), С. 14095 - 14140

Опубликована: Июль 30, 2024

Abstract Electrospun nanofibers have gained prominence as a versatile material, with applications spanning tissue engineering, drug delivery, energy storage, filtration, sensors, and textiles. Their unique properties, including high surface area, permeability, tunable porosity, low basic weight, mechanical flexibility, alongside adjustable fiber diameter distribution modifiable wettability, make them highly desirable across diverse fields. However, optimizing the properties of electrospun to meet specific requirements has proven be challenging endeavor. The electrospinning process is inherently complex influenced by numerous variables, applied voltage, polymer concentration, solution flow rate, molecular weight polymer, needle-to-collector distance. This complexity often results in variations nanofibers, making it difficult achieve desired characteristics consistently. Traditional trial-and-error approaches parameter optimization been time-consuming costly, they lack precision necessary address these challenges effectively. In recent years, convergence materials science machine learning (ML) offered transformative approach electrospinning. By harnessing power ML algorithms, scientists researchers can navigate intricate space more efficiently, bypassing need for extensive experimentation. holds potential significantly reduce time resources invested producing wide range applications. Herein, we provide an in-depth analysis current work that leverages obtain target nanofibers. examining work, explore intersection ML, shedding light on advancements, challenges, future directions. comprehensive not only highlights processes but also provides valuable insights into evolving landscape, paving way innovative precisely engineered various Graphical abstract

Язык: Английский

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Polypropylene waste plastic fiber morphology as an influencing factor on the performance and durability of concrete: Experimental investigation, soft-computing modeling, and economic analysis

Construction and Building Materials, Год журнала: 2024, Номер 438, С. 137244 - 137244

Опубликована: Июль 2, 2024

Язык: Английский

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Expediting carbon dots synthesis by the active adaptive method with machine learning and applications in dental diagnosis and treatment

Nano Research, Год журнала: 2024, Номер unknown

Опубликована: Сен. 7, 2024

Язык: Английский

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Data-driven modeling of the quantitative structure-activity relationship between aggregate contact parameters and dynamic modulus in asphalt mixtures

Construction and Building Materials, Год журнала: 2025, Номер 470, С. 140698 - 140698

Опубликована: Март 7, 2025

Язык: Английский

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Development of petroleum-derived polymeric additive to enhance the bituminous properties with the use of a machine-learning model

Sustainable Chemistry for the Environment, Год журнала: 2024, Номер unknown, С. 100186 - 100186

Опубликована: Ноя. 1, 2024

Язык: Английский

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