Empirical model to simulate morphology of electrospun polycaprolactone mats

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

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 (R²). 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 R² values that could be achieved, reaching 0.9989.

Electrospun nanofiber-based glucose sensors for glucose detection

Diabetes is a chronic, systemic metabolic disease that leads to multiple complications, even death. Meanwhile, the number of people with diabetes worldwide is increasing year by year. Sensors play an important role in the development of biomedical devices. The development of efficient, stable, and inexpensive glucose sensors for the continuous monitoring of blood glucose levels has received widespread attention because they can provide reliable data for diabetes prevention and diagnosis. Electrospun nanofibers are new kinds of functional nanocomposites that show incredible capabilities for high-level biosensing. This article reviews glucose sensors based on electrospun nanofibers. The principles of the glucose sensor, the types of glucose measurement, and the glucose detection methods are briefly discussed. The principle of electrospinning and its applications and advantages in glucose sensors are then introduced. This article provides a comprehensive summary of the applications and advantages of polymers and nanomaterials in electrospun nanofiber-based glucose sensors. The relevant applications and comparisons of enzymatic and non-enzymatic nanofiber-based glucose sensors are discussed in detail. The main advantages and disadvantages of glucose sensors based on electrospun nanofibers are evaluated, and some solutions are proposed. Finally, potential commercial development and improved methods for glucose sensors based on electrospinning nanofibers are discussed.

Digital Twin Geometry for Fibrous Air Filtration Media

Computational modeling of air filtration is possible by replicating nonwoven nanofibrous meltblown or electrospun filter media with digital representative geometry. This article presents a methodology to create and modify randomly generated fiber geometry intended as a digital twin replica of fibrous filtration media. Digital twin replicas of meltblown and electrospun filter media are created using Python scripting and Ansys SpaceClaim. The effect of fiber stiffness, represented by a fiber relaxation slope, is analyzed in relation to resulting filter solid volume fraction and thickness. Contemporary air filtration media may also be effectively modeled analytically and tested experimentally in order to yield valuable information on critical characteristics, such as overall resistance to airflow and particle capture efficiency. An application of the Single Fiber Efficiency model is incorporated in this work to illustrate the estimation of performance for the generated media with an analytical model. The resulting digital twin fibrous geometry compares well with SEM imagery of fibrous filter materials. This article concludes by suggesting adaptation of the methodology to replicate digital twins of other nonwoven fiber mesh applications for computational modeling, such as fiber reinforced additive manufacturing and composite materials.

Research progress, models and simulation of electrospinning technology: a review

In recent years, nanomaterials have aroused extensive research interest in the world's material science community. Electrospinning has the advantages of wide range of available raw materials, simple process, small fiber diameter and high porosity. Electrospinning as a nanomaterial preparation technology with obvious advantages has been studied, such as its influencing parameters, physical models and computer simulation. In this review, the influencing parameters, simulation and models of electrospinning technology are summarized. In addition, the progresses in applications of the technology in biomedicine, energy and catalysis are reported. This technology has many applications in many fields, such as electrospun polymers in various aspects of biomedical engineering. The latest achievements in recent years are summarized, and the existing problems and development trends are analyzed and discussed.

Melt Electrospinning of PET and Composite PET-Aerogel Fibers: An Experimental and Modeling Study

Increasingly advanced applications of polymer fibers are driving the demand for new, high-performance fiber types. One way to produce polymer fibers is by electrospinning from polymer solutions and melts. Polymer melt electrospinning produces fibers with small diameters through solvent-free processing and has applications within different fields, ranging from textile and construction, to the biotech and pharmaceutical industries. Modeling of the electrospinning process has been mainly limited to simulations of geometry-dependent electric field distributions. The associated large change in viscosity upon fiber formation and elongation is a key issue governing the electrospinning process, apart from other environmental factors. This paper investigates the melt electrospinning of aerogel-containing fibers and proposes a logistic viscosity model approach with parametric ramping in a finite element method (FEM) simulation. The formation of melt electrospun fibers is studied with regard to the spinning temperature and the distance to the collector. The formation of PET-Aerogel composite fibers by pneumatic transport is demonstrated, and the critical parameter is found to be the temperature of the gas phase. The experimental results form the basis for the electrospinning model, which is shown to reproduce the trend for the fiber diameter, both for polymer as well as polymer-aerogel composites.