Investigation of effect of processing parameters of 3D printed NHS/EDC crosslinked carboxy methyl cellulose/gelatin hydrogels with machine learning techniques

Abstract

This study focuses on the effects of preparing conditions of gelatin/carboxymethyl cellulose (CMC) composites on their mechanical properties of gelatin/carboxymethyl cellulose (CMC) by extreme gradient boosting (XGB) machine learning algorithm. The research involved studying the effect of weight fraction of carboxymethyl cellulose (CMC) and graphene oxide (GO) as well as the concentration of ethyl(dimethylaminopropyl)carbodiimide (EDC)/ N-hydroxysuccinimide (NHS) on modulus, % strain at break and ultimate tensile strength (UTS). It also includes a correlation heatmap, feature importance assessment, model performance evaluation, and the Shapley Additive Explanation (SHAP) technique to analyze the dataset. The relationship between independent parameters and mechanical properties reveals insights into the material’s ductility, flexibility, and modulus. Feature importance demonstrates that NHS/EDC concentration has the highest impact on the mechanical properties. Increase of EDC/NHS concentration is observed to drastically elevate the modulus and UTS, however, reduces the flexibility of the nanocomposites. CMC improves flexibility but reduces UTS and modulus. GO improves % strain at break, UTS and modulus up to 1% GO, however, higher wt% of GO reduces the mechanical performance. With lower concentrations of NHS/EDC, the mechanical properties can be tailored for soft tissue engineering applications. The study highlights the importance of optimizing material compositions for tissue engineering applications.

Keywords

• machine learning
• XGB model
• mechanical properties
• EDS
• gelatin
• carboxy methyl cellulose