An emerging machine learning strategy for the assisted‐design of high-performance supercapacitor materials by mining the relationship between capacitance and structural features of porous carbon

New Engineering Science Insights into the Electrode Materials Pairing of Electrochemical Energy Storage Devices

Pairing the positive and negative electrodes with their individual dynamic characteristics at a realistic cell level is essential to the practical optimal design of electrochemical energy storage devices. However, the complex relationship between the performance data measured for individual electrodes and the two-electrode cells used in practice often makes an optimal pairing experimentally challenging. Taking advantage of the developed tunable graphene-based electrodes with controllable structure, experiments with machine learning are successfully united to generate a large pool of capacitance data for graphene-based electrode materials with varied slit pore sizes, thicknesses, and charging rates and numerically pair them into different combinations for two-electrode cells. The results show that the optimal pairing parameters of positive and negative electrodes vary considerably with the operation rate of the cells and are even influenced by the thickness of inactive components. The best-performing individual electrode does not necessarily result in optimal cell-level performance. The machine learning-assisted pairing approach presents much higher efficiency compared with the traditional trial-and-error approach for the optimal design of supercapacitors. The new engineering science insights observed in this work enable the adoption of artificial intelligence techniques to efficiently translate well-developed high-performance individual electrode materials into real energy storage devices.

Machine Learning-Guided Prediction of Desalination Capacity and Rate of Porous Carbons for Capacitive Deionization

Nowadays, capacitive deionization (CDI) has emerged as a prominent technology in the desalination field, typically utilizing porous carbons as electrodes. However, the precise significance of electrode properties and operational conditions in shaping desalination performance remains blurry, necessitating numerous time-consuming and resource-intensive CDI experiments. Machine learning (ML) presents an emerging solution, offering the prospect of predicting CDI performance with minimal investment in electrode material synthesis and testing. Herein, four ML models are used for predicting the CDI performance of porous carbons. Among them, the gradient boosting model delivers the best performance on test set with low root mean square error values of 2.13 mg g-1 and 0.073 mg g-1 min-1 for predicting desalination capacity and rate, respectively. Furthermore, SHapley Additive exPlanations is introduced to analyze the significance of electrode properties and operational conditions. It highlights that electrolyte concentration and specific surface area exert a substantially more influential role in determining desalination performance compared to other features. Ultimately, experimental validation employing metal-organic frameworks-derived porous carbons and biomass-derived porous carbons as CDI electrodes is conducted to affirm the prediction accuracy of ML models. This study pioneers ML techniques for predicting CDI performance, offering a compelling strategy for advancing CDI technology.

Recent advances in metal-organic framework (MOF)-based agricultural sensors for metal ions: a review

Metal ions have great significance for agricultural development, food safety, and human health. In turn, there exists an imperative need for the development of novel, sensitive, and reliable sensing techniques for various metal ions. Agricultural sensors for the diagnosis of both agricultural safety and nutritional health can establish quality and safety traceability systems of both agro-products and food to guarantee human health, even life safety. Metal-organic frameworks (MOFs) are utilized widely for the design of diversified sensors due to their distinctive structural characteristics and extraordinary optical and electrical properties. To serve agricultural sensors better, this review is dedicated to providing a brief overview of the synthesis of MOFs, the modification of MOFs, the fabrication of MOF-based film electrodes, the applications of MOF-based agricultural sensors for metal ions, which are centered on electrochemical sensors and optical sensors, and current challenges of MOF-based agricultural sensors. In addition, this review also provides potential future opportunities for the development and practical application of agricultural sensors.

Unlocking the Full Potential of Heteroatom-Doped Graphene-Based Supercapacitors through Stacking Models and SHAP-Guided Optimization

Graphene-based supercapacitors have emerged as a promising candidate for energy storage due to their superior capacitive properties. Heteroatom-doping is a method of improving the capacitive properties of graphene-based electrodes, but the optimal doping conditions and electrochemical properties are not yet fully understood due to the synergistic effects that occur. Many parameters, such as doping content, defects, specific surface area (SA), electrolyte, and more, could affect the capacitance (CAP). In this study, we use machine learning to solve these critical issues. We applied many models, such as Light Gradient Boost Machine, Extreme Gradient Boost, Polynomial Regression, Neural Network, Elastic Net, Lasso Regression, Ridge Regression, Random Forest, Support Vector Machine, K-Nearest Neighbors, Gradient Boost, AdaBoost, and Decision Tree, to find a suitable model for CAP prediction. Moreover, we enhance the prediction result by taking advantage of the top candidate model and creating a stacking concept (called "stacking models"). The SHAP value was used to identify the range of properties that affect CAP, and it was discussed in detail. Our results suggest that high-CAP graphene supercapacitors should have a large SA, with 4-5% nitrogen, 10-15% oxygen, high percentages of sulfur, a defect ratio close to 1, with acid electrolyte, and a low current density. These findings, along with the developed model and code, are expected to serve as a valuable computational tool for future electrochemical research from fundamental to applications.

O and N co-doped porous carbon derived from crop waste for a high-stability all-solid-state symmetric supercapacitor

The SPC/700 °C/3-based all-solid-state symmetric supercapacitor exhibits a high specific capacitance as well as excellent long-term cycling stability.