Jianjun Guo^1,2, Lingling Jia³, Sandra Senyo Fometu⁴, Qiang Ma⁵, Jinjin Wang¹, Hang Li¹, Lei Jiang⁵, Guohua Wu^1,*

Journal of Renewable Materials, Vol.10, No.12, pp. 3373-3389, 2022, DOI:10.32604/jrm.2022.021087

Abstract Traditionally, silkworm silk has been used to make high-quality textiles. Nevertheless, various wastes from silkworm silk textiles that are no longer used are increasing, which is also causing considerable waste and contamination. This issue is causing widespread concern in countries that use more silk. Regenerated silk fibroin (RSF) fibers have been shown to be fragile and tender, which prohibits RSF from being widely used as a structural component. Therefore, enriching the function of silk and enhancing the RSF mechanical properties are important directions to expand the comprehensive utilization of silk products. In the present research, wet spinning was used to… More >

Taegeon Kil¹, D. I. Jang¹, H. N. Yoon¹, Beomjoo Yang^2,*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4487-4502, 2022, DOI:10.32604/cmc.2022.020940

Abstract A machine learning-based prediction of the self-heating characteristics and the negative temperature coefficient (NTC) effect detection of nanocomposites incorporating carbon nanotube (CNT) and carbon fiber (CF) is proposed. The CNT content was fixed at 4.0 wt.%, and CFs having three different lengths (0.1, 3 and 6 mm) at dosage of 1.0 wt.% were added to fabricate the specimens. The self-heating properties of the specimens were evaluated via self-heating tests. Based on the experiment results, two types of artificial neural network (ANN) models were constructed to predict the surface temperature and electrical resistance, and to detect a severe NTC effect. The… More >