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一种用于三维电纺泡沫的原位表征方法。

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

作者信息

Almpanidis Kyriakos, Howard Chloe J, Stolojan Vlad

机构信息

Advanced Technology Institute, University of Surrey, Guildford GU2 7XH, UK.

出版信息

Nanomaterials (Basel). 2025 Feb 22;15(5):339. doi: 10.3390/nano15050339.

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 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 for each run r. The analysis shows that both parameters detected the same experiment run as the optimal one (with an adjusted R = 0.84) for polystyrene electrospun foams for two solution concentrations: 15%wv (run 3 with mean = 54.49 and mean = 0.248) and 20%wv (mean = 2.49 and = 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.

摘要

三维电纺泡沫正在多种应用中崭露头角。然而,对它们的表征涉及计算纤维直径和孔隙率的程序,这需要相当长的时间。因此,本文基于接地电压的信号特征提出了一种原位表征方法。这些特征被组合成每次运行r的原位评估参数。采用L9田口方法来最小化实验的总数。此外,为了证明该方法的准确性,进行了传统的制备后分析,并为每次运行r获取了制备后评估参数。分析表明,对于两种溶液浓度的聚苯乙烯电纺泡沫:15%wv(运行3,平均值 = 54.49,平均值 = 0.248)和20%wv(平均值 = 2.49, = 0.248),这两个参数都检测到相同的实验运行作为最优运行(调整后的R = 0.84)。而且,统计分析表明最优和接近最优运行的标准偏差较低,证明了该方法的可重复性。此外,基于电纺射流的麦克斯韦等效电路方法为选择信号特征提供了理论解释。最后,这种快速的原位评估方法可以取代制备后耗时的方法。它可以用作预测最佳泡沫形成的智能人工智能工具的基础步骤。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed38/11902143/9abb4beb1088/nanomaterials-15-00339-g0A1.jpg

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