Herein we report the fabrication of cationic functionalized cellulose nanofibers (c-CNF) having 0.13 mmol.g -1 ammonium content and its ionic crosslinking via the pad-batch process. The overall chemical modifications were justified through infrared spectroscopy. It is revealed that the tensile strength of ionic crosslinked c-CNF (z c -CNF) improved from 3.8 MPa to 5.4 MPa over c-CNF. The adsorption capacity of z c- -CNF was found to be 158 mg.g -1 followed by the Thomas model. Further, the experimental data were used to train and test a series of machine learning (ML) models. A total of 23 various classical ML models (as a benchmark) were compared simultaneously using Pycaret which helped reduce the programming complexity. However, shallow, and deep neural networks are used that outperformed the classic machine learning models. The best classical-tuned ML model using Random Forests regression had an accuracy of 92.6 %. The deep neural network made effective by early stopping and dropout regularization techniques, with 20 × 6 (Neurons x Layers) configuration, showed an appreciable prediction accuracy of 96 %.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Muhammad Muqeet reports equipment, drugs, or supplies was provided by Mehran University of Engineering & Technology. This manuscript contains only original data, which has not been published elsewhere, nor is submitted, in the press or under consideration simultaneously for publication elsewhere. All authors fully participated in the preparation of the manuscript and accept responsibility for the results being presented. The authors further declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. We declare no conflict of interest.
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