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Optimization of an alkali modification protocol on Crotalaria juncea fibre and its characterization for technical textile applications
Industrial Crops and Products ( IF 5.9 ) Pub Date : 2024-02-27 , DOI: 10.1016/j.indcrop.2024.118275
Pavan M , Surabhi Mahajan , Ammayappan Lakshmanan , T. Nageshkumar , Vinay Midha , B.S. Manjunatha , Sumeet Grewal

Sunnhemp () is a valuable lignocellulosic biomass for both fodder and textile fibre. It exhibits an appealing specific tensile strength which make it a desirable reinforcement material for development of the green composites. However, to enhance its compatibility with hydrophobic polymers, a suitable chemical modification was desired. Hence, in the current study an attempted was made to modify the surface of the Sunnhemp fibre by sodium hydroxide and to optimize the operational protocol through hybrid Artificial Neural Network-Multi-Objective Genetic Algorithm (ANN-MOGA). The physical, chemical, mechanical, and surface morphological properties of raw and alkali-modified fibre were evaluated as per standard protocols. The optimized conditions are 5.01 g/L alkali concentration, 30 minutes of treatment time, and 1:20 material-to-liquor ratio. The optimized condition was validated, with less deviation between predicted and actual values, which confirmed the adequacy of the developed model. The alkali modified Sunnhemp fibre showed higher cellulose content, mechanical strength, and thermal stability than the raw fibre. SEM analysis revealed a distinct channel-like patterns in the alkali-treated fibres and affirmed the removal of surface impurities. Notably, the alkali-modified Sunnhemp fibre exhibited desirable antimicrobial properties against bacteria. It was concluded that AI based optimization could offer a sustainable approach for the surface modification of the natural fibre by identifying the effective processing condition with saving energy.

中文翻译:

猪屎豆纤维碱改性方案的优化及其产业用纺织品应用的表征

Sunnhemp () 是一种有价值的木质纤维素生物质,可用于饲料和纺织纤维。它具有吸引人的比拉伸强度,使其成为开发绿色复合材料的理想增强材料。然而,为了增强其与疏水性聚合物的相容性,需要进行适当的化学修饰。因此,在当前的研究中,尝试用氢氧化钠修饰 Sunnhemp 纤维的表面,并通过混合人工神经网络-多目标遗传算法(ANN-MOGA)优化操作协议。根据标准协议评估原纤维和碱改性纤维的物理、化学、机械和表面形态特性。优化工艺条件为碱浓度5.01 g/L、处理时间30 min、料液比1:20。对优化条件进行了验证,预测值与实际值之间的偏差较小,这证实了所开发模型的充分性。碱改性 Sunnhemp 纤维比原纤维表现出更高的纤维素含量、机械强度和热稳定性。SEM 分析揭示了碱处理纤维中明显的通道状图案,并证实了表面杂质的去除。值得注意的是,碱改性 Sunnhemp 纤维表现出理想的抗菌性能。结论是,基于人工智能的优化可以通过确定有效的加工条件并节省能源,为天然纤维的表面改性提供可持续的方法。
更新日期:2024-02-27
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