# An In Situ Characterisation Method for 3-D Electrospun Foams

## TL;DR

This paper introduces a fast in situ method to evaluate 3-D electrospun foams using grounding voltage signals, reducing the need for time-consuming post-fabrication analysis.

## Contribution

A novel in situ evaluation method for electrospun foams using grounding voltage signal features and the Taguchi method.

## Key findings

- The in situ method identified the same optimal run as post-fabrication analysis with an adjusted R2 of 0.84.
- Low standard deviations for optimal and near-optimal runs confirm the method's repeatability.
- The method is based on a theoretical explanation using the Maxwellian equivalent circuit approach.

## Abstract

Three-dimensional electrospun foams are emerging in a diversity of applications. However, their characterisation involves procedures to calculate fibre diameter and porosity, which take considerable time. Hence, in this paper, an in situ characterisation method is presented based on signal features of the grounding voltage. These features are combined into the in situ evaluation parameter Sr for each run r. The L9 Taguchi method was utilised to minimise the total number of experiments. Moreover, to prove the accuracy of this method, the traditional post-fabrication analysis was conducted, and the post-fabrication evaluation parameter was retrieved Qr for each run r. The analysis shows that both parameters detected the same experiment run as the optimal one (with an adjusted R2 = 0.84) for polystyrene electrospun foams for two solution concentrations: 15%wv (run 3 with mean S3 = 54.49 and mean Q3 = 0.248) and 20%wv (mean S5 = 2.49 and Q5 = 0.248), respectively. Also, the statistical analysis shows low standard deviations for the optimal and near-optimal runs, proving the method’s repeatability. Furthermore, a theoretical explanation is provided for selecting signal features based on the Maxwellian equivalent circuit approach for the electrospun jet. Finally, this fast in situ evaluation method can replace the post-fabrication time-consuming one. It can be used as a fundamental step for an intelligent artificial intelligence tool that predicts optimal foam formation.