Analysis and Prediction of Electrospun Nanofiber Diameter Based on Artificial Neural Network

Ma, Ming; Zhou, Huimeng; Gao, Shuting; Li, Nan; Guo, Wei; Dai, Zhao

doi:10.3390/polym15132813

Cited by 7 publications

(1 citation statement)

References 33 publications

(43 reference statements)

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“…The average fiber diameter was calculated by taking 100 random nanofibers in the SEM micrographs and these were 32, 40, 56 and 65 nm for 15 wt% (figures 2(a) and (b)), 18 wt% (figures 2(c) and (d)), 20 wt% (figures 2(e) and (f)), and 23 wt% (figures 2(g) and (h)), of Ny 6 samples, respectively. As the polymer concentration increases, the fiber diameter tends to increase as reported in the literature [20][21][22]. The main reason for this phenomenon is the increased viscosity of the polymer solution with higher polymer concentrations.…”

Section: Morphological Structural and Thermal Analyses Of Nylon-6 Nan...supporting

confidence: 56%

Sustainable energy harvesting and breath sensing with electrospun triboelectric nylon-6

Jelmy,

Sunil,

Kandappanthodi

et al. 2024

J. Phys. Energy

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A high-performance triboelectric nanogenerator (TENG) has been developed for breath sensing applications, utilizing tribopositive electrospun nylon-6 nanofibers and tribonegative fluorinated ethylene propylene (FEP). The optimization towards the development of electrospun nylon-6-based TENG includes a range of factors such as the applied force and frequency on tribo responses, the thickness of the fiber mat, the concentration of nylon-6 in the fiber mats, and the selection of the tribonegative material for pairing with nylon-6 nanofiber. Among these parameters, the nanofiber prepared with 18 wt% nylon-6, characterized by a uniform fiber distribution, the highest surface area of 55.69 m2/g, and an optimal thickness of 0.169 mm, demonstrated excellent TENG performance, among others. The TENG module constructed using nanofiber in a 4 cm2 area showed the TENG responses of more than 30 μA short-circuit current, 200 V open-circuit voltage, and 90 nC charge when hand-pressed. It achieved a substantial power density of 890 mW/m2 at 20 MΩ by applying a constant force of 10 N at a 10 Hz frequency. Charging a 1 μF capacitor to approximately 30.1 V in just 30 seconds highlights the potential of electrospun nylon-6 as a promising material for nanogenerator energy harvesting and sensing applications. The TENG device was found to be sufficient to power small, portable electronics such as LEDs and digital watch displays. A wearable belt was fabricated to showcase its breath-sensing capabilities by pairing it with FEP. The microcontroller connected to the TENG in the wearable belt is used to analyze the output produced through breathing patterns, subsequently activating a buzzer and LED by the nature of the breathing.

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Section: Morphological Structural and Thermal Analyses Of Nylon-6 Nan...supporting

confidence: 56%

Sustainable energy harvesting and breath sensing with electrospun triboelectric nylon-6

Jelmy,

Sunil,

Kandappanthodi

et al. 2024

J. Phys. Energy

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Artificial neural networks for the accurate prediction of the thickness of fiber membranes prepared by airflow‐assisted electrostatic spinning

Ma,

Zheng,

Zeng

et al. 2024

J of Applied Polymer Sci

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The practical application of fiber membranes prepared by electrostatic spinning requires a uniform thickness. However, the parameters involved in the spinning process are highly complex, particularly when it comes to precise control over membrane thickness. Hence, achieving a consistent and homogeneous thickness for fiber membranes has posed significant challenges thus far. To accurately predict membrane thickness during the spinning process and ensure consistent outcomes, this study employed polyvinylidene fluoride as the raw material and optimized electrostatic spinning preparation using an innovative airflow‐assisted strategy. The as‐proposed preparation strategy effectively enhances the volatilization of solvents within the jet, also promotes jet stretching through assistance from airflow, resulting in a more stable and homogeneous fiber diameter. Additionally, COMSOL simulations were utilized to provide rational explanations for these findings. By collecting multiple process parameters, including positive voltage, liquid supply speed, winding speed, and solution concentration, this study has successfully developed an artificial neural network (ANN) that exhibits reliable predictive capabilities for membrane thickness. Notably, the model demonstrates a remarkable goodness‐of‐fit with an R2 value of 0.9981. Through the integration of ANN with electrostatic spinning technology, significant advancements have been achieved in accurately predicting and controlling the uniformity of electrostatically spun polymer fiber membranes.

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Machine learning applications for electrospun nanofibers: a review

Subeshan,

Atayo,

Asmatulu

2024

J Mater Sci

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

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Analysis and Prediction of Electrospun Nanofiber Diameter Based on Artificial Neural Network

Cited by 7 publications

References 33 publications

Sustainable energy harvesting and breath sensing with electrospun triboelectric nylon-6

Sustainable energy harvesting and breath sensing with electrospun triboelectric nylon-6

Artificial neural networks for the accurate prediction of the thickness of fiber membranes prepared by airflow‐assisted electrostatic spinning

Machine learning applications for electrospun nanofibers: a review

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