Plant-derived lignin-based supercapacitors have shown strong potential as light and flexible electronic devices. To design a safe, reliable, and efficient supercapacitor, it is important to predict its electrochemical performance. To date, there is no reliable theoretical model to serve the purpose. In this work, four machine learning algorithms are developed and compared to predict the specific capacitance variation for lignin supercapacitors. Specifically, linear regression (LR), support vector machine (SVM), decision tree (DT), and artificial neural network (ANN) models are analyzed. Specific capacitance variation obtained by cyclic charge−discharge tests is modeled as a function of weight percentages of material constituents (lignin, nickel tungstate nanoparticles, and polyvinylidene fluoride) and the cycle number. The accuracy of the models is ranked as LR < SVM < DT < ANN and validated further using the F-test. The superior model fit of ANN shows the highest accuracy and applicability with excellent robustness. ANN can generalize the learned specific capacitance variation to material ratios excluded in the training set. This work provides an understanding of different machine learning techniques for predicting the specific capacitance variation and retention of lignin-based supercapacitors, demonstrating the strong potential of ANN to be used as a predictive tool to aid in supercapacitor optimization.

Machine learning (ML) has been the focus of many recent studies aiming to improve battery and supercapacitor technology. Its application in materials science research has demonstrated promising results for accelerating...

Due to demands for sustainability, the interest in energy storage devices constructed from green materials has increased immensely. These devices currently have yet to be satisfactory. Issues include high production costs and toxicity, limited dependability, and subpar electrochemical performance. In this research, low-cost, plant-based electroactive Cu3Mo2O9 materials were synthesized via co-precipitation followed by an annealing method using two different structure-directing agents, i.e., the commonly used surfactant cetyltrimethylammonium bromide (CTAB) and the biomolecule deoxyribonucleic acid (DNA) as a greener alternative, and these materials were studied for the first time. Further, the Cu3Mo2O9 nanoparticles developed using CTAB and DNA were integrated into the lignin matrix and studied as flexible electrodes for supercapacitor application. Here, the morphological advantages of the nanorods and nanosheets formed by varying the synthesis methods and their effects during supercapacitor studies were studied in detail. After 1200 cycles, the Al/lig-Cu3Mo2O9@DNA supercapacitor exhibited higher capacitive performance when compared to the Al/lig-Cu3Mo2O9@CTAB supercapacitor. The Al/Lig-Cu3Mo2O9@DNA supercapacitor had an initial specific capacitance of 404.64 mF g−1 with a ~70% retention, while the Al/Lig-Cu3Mo2O9@CTAB supercapacitor had an initial specific capacitance of 309.59 mF g−1 with a ~50% retention. This study offers a new approach to creating scalable, low-cost, green composite CuMoO4-based electrodes for flexible supercapacitors.