Electrospun nanofiber membrane diameter prediction using a combined response surface methodology and machine learning approach

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Despite the widespread interest in electrospinning technology, very few simulation studies have been conducted. Thus, the current research produced a system for providing a sustainable and effective electrospinning process by combining the design of experiments with machine learning prediction models. Specifically, in order to estimate the diameter of the electrospun nanofiber membrane, we developed a locally weighted kernel partial least squares regression (LW-KPLSR) model based on a response surface methodology (RSM). The accuracy of the model's predictions was evaluated based on its root mean square error ( RMSE), its mean absolute error ( MAE), and its coefficient of determination ( R ²). In addition to principal component regression (PCR), locally weighted partial least squares regression (LW-PLSR), partial least square regression (PLSR), and least square support vector regression model (LSSVR), some of the other types of regression models used to verify and compare the results were fuzzy modelling and least square support vector regression model (LSSVR). According to the results of our research, the LW-KPLSR model performed far better than other competing models when attempting to forecast the membrane's diameter. This is made clear by the much lower RMSE and MAE values of the LW-KPLSR model. In addition, it offered the highest R ² values that could be achieved, reaching 0.9989.

Related collections

Most cited references 54

Record: found
Abstract: found
Article: not found

Electrospun Nanofibers: New Concepts, Materials, and Applications

Jiajia Xue, Jingwei Xie, Wenying Liu … (2017)

Electrospinning is a simple and versatile technique that relies on the electrostatic repulsion between surface charges to continuously draw nanofibers from a viscoelastic fluid. It has been applied to successfully produce nanofibers, with diameters down to tens of nanometers, from a rich variety of materials, including polymers, ceramics, small molecules, and their combinations. In addition to solid nanofibers with a smooth surface, electrospinning has also been adapted to generate nanofibers with a number of secondary structures, including those characterized by a porous, hollow, or core-sheath structure. The surface and/or interior of such nanofibers can be further functionalized with molecular species or nanoparticles during or after an electrospinning process. In addition, electrospun nanofibers can be assembled into ordered arrays or hierarchical structures by manipulation of their alignment, stacking, and/or folding. All of these attributes make electrospun nanofibers well-suited for a broad spectrum of applications, including those related to air filtration, water purification, heterogeneous catalysis, environmental protection, smart textiles, surface coating, energy harvesting/conversion/storage, encapsulation of bioactive species, drug delivery, tissue engineering, and regenerative medicine. Over the past 15 years, our group has extensively explored the use of electrospun nanofibers for a range of applications. Here we mainly focus on two examples: (i) use of ceramic nanofibers as catalytic supports for noble-metal nanoparticles and (ii) exploration of polymeric nanofibers as scaffolding materials for tissue regeneration. Because of their high porosity, high surface area to volume ratio, well-controlled composition, and good thermal stability, nonwoven membranes made of ceramic nanofibers are terrific supports for catalysts based on noble-metal nanoparticles. We have investigated the use of ceramic nanofibers made of various oxides, including SiO2, TiO2, SnO2, CeO2, and ZrO2, as supports for heterogeneous catalysts based on noble metals such as Au, Pt, Pd, and Rh. On the other hand, the diameter, composition, alignment, porosity, and surface properties of polymeric nanofibers can be engineered in a controllable fashion to mimic the hierarchical architecture of an extracellular matrix and help manipulate cell behaviors for tissue engineering and regenerative medicine. To this end, we can mimic the native structure and morphology of the extracellular matrix in tendon using uniaxially aligned nanofibers; we can use radially aligned nanofibers to direct the migration of cells from the periphery to the center in an effort to speed up wound healing; and we can also use uniaxially aligned nanofibers to guide and expedite the extension of neurites for peripheral nerve repair. Furthermore, we can replicate the anatomic structures at the tendon-to-bone insertion using nanofiber scaffolds with graded mineral coatings. In this Account, we aim to demonstrate the unique capabilities of electrospun nanofibers as porous supports for heterogeneous catalysis and as functional scaffolds for tissue regeneration by concentrating on some of the recent results.

0 comments Cited 271 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

An ANN application for water quality forecasting.

Shie-Yui Liong, Pavel Tkalich, Sundarambal Palani (2008)

Rapid urban and coastal developments often witness deterioration of regional seawater quality. As part of the management process, it is important to assess the baseline characteristics of the marine environment so that sustainable development can be pursued. In this study, artificial neural networks (ANNs) were used to predict and forecast quantitative characteristics of water bodies. The true power and advantage of this method lie in its ability to (1) represent both linear and non-linear relationships and (2) learn these relationships directly from the data being modeled. The study focuses on Singapore coastal waters. The ANN model is built for quick assessment and forecasting of selected water quality variables at any location in the domain of interest. Respective variables measured at other locations serve as the input parameters. The variables of interest are salinity, temperature, dissolved oxygen, and chlorophyll-alpha. A time lag up to 2Delta(t) appeared to suffice to yield good simulation results. To validate the performance of the trained ANN, it was applied to an unseen data set from a station in the region. The results show the ANN's great potential to simulate water quality variables. Simulation accuracy, measured in the Nash-Sutcliffe coefficient of efficiency (R(2)), ranged from 0.8 to 0.9 for the training and overfitting test data. Thus, a trained ANN model may potentially provide simulated values for desired locations at which measured data are unavailable yet required for water quality models.

0 comments Cited 57 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: not found
Article: not found

Ultrafine Cellulose Nanofibers as Efficient Adsorbents for Removal of UO22+ in Water

Hongyang Ma, Benjamin Chu, Benjamin S. Hsiao (2011)

0 comments Cited 43 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Yingjie Cai: yingjiecai@wtu.edu.cn

George K. Stylios: g.stylios@hw.ac.uk

Vincenzo Naddeo: vnaddeo@unisa.it

Journal

Journal ID (nlm-ta): Sci Rep

Journal ID (iso-abbrev): Sci Rep

Title: Scientific Reports

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2045-2322

Publication date (Electronic): 15 June 2023

Publication date PMC-release: 15 June 2023

Publication date Collection: 2023

Volume: 13

Electronic Location Identifier: 9679

Affiliations

[1 ]GRID grid.413242.2, ISNI 0000 0004 1765 9039, Hubei Provincial Engineering Laboratory for Clean Production and High Value Utilization of Bio-Based Textile Materials, , Wuhan Textile University, ; Wuhan, 430200 China

[2 ]GRID grid.11780.3f, ISNI 0000 0004 1937 0335, Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, , University of Salerno, ; 84084 Fisciano, Italy

[3 ]GRID grid.448987.e, Department of Chemical and Energy Engineering, Faculty of Engineering and Science, , Curtin University Malaysia, ; CDT 250, 98009 Miri, Sarawak Malaysia

[4 ]GRID grid.443081.a, ISNI 0000 0004 0489 3643, Faculty of Nutrition and Food Science, , Patuakhali Science and Technology University, ; Patuakhali, 8602 Bangladesh

[5 ]GRID grid.411512.2, ISNI 0000 0001 2223 0518, Department of Chemical Engineering, , Bangladesh University of Engineering and Technology, ; Dhaka, 1000 Bangladesh

[6 ]GRID grid.22069.3f, ISNI 0000 0004 0369 6365, Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, , East China Normal University, and Institute of Eco-Chongming, ; Shanghai, 200241 China

[7 ]GRID grid.9531.e, ISNI 0000000106567444, Research Institute for Flexible Materials, School of Textiles and Design, , Heriot-Watt University, ; Galashiels, TD1 3HF UK

Article

Publisher ID: 36431

DOI: 10.1038/s41598-023-36431-7

PMC ID: 10272235

PubMed ID: 37322139

SO-VID: f5d4d493-bb18-4704-a62e-9c1ed314b48d

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.