Transforming Waste into Wealth: Advanced Carbon-Based Electrodes Derived from Refinery and Coal By-Products for Next-Generation Energy Storage.

This comprehensive review addresses the need for sustainable and efficient energy storage technologies against escalating global energy demand and environmental concerns. It explores the innovative utilization of waste materials from oil refineries and coal processing industries as precursors for carbon-based electrodes in next-generation energy storage systems, including batteries and supercapacitors. These waste-derived carbon materials, such as semi-coke, coal gasification fine ash, coal tar pitch, petroleum coke, and petroleum vacuum residue, offer a promising alternative to conventional electrode materials. They present an optimal balance of high carbon content and enhanced electrochemical properties while promoting environmental sustainability through effectively repurposing waste materials from coal and hydrocarbon industries. This review systematically examines recent advancements in fabricating and applying waste-derived carbon-based electrodes. It delves into the methodologies for converting industrial by-products into high-quality carbon electrodes, with a particular emphasis on carbonization and activation processes tailored to enhance the electrochemical performance of the derived materials. Key findings indicate that while higher carbonization temperatures may impede the development of a porous structure, using KOH as an activating agent has proven effective in developing mesoporous structures conducive to ion transport and storage. Moreover, incorporating heteroatom doping (with elements such as sulfur, potassium, and nitrogen) has shown promise in enhancing surface interactions and facilitating the diffusion process through increased availability of active sites, thereby demonstrating the potential for improved storage capabilities. The electrochemical performance of these waste-derived carbon materials is evaluated across various configurations and electrolytes. Challenges and future directions are identified, highlighting the need for a deeper understanding of the microstructural characteristics that influence electrochemical performance and advocating for interdisciplinary research to achieve precise control over material properties. This review contributes to advancing electrode material technology and promotes environmental sustainability by repurposing industrial waste into valuable resources for energy storage. It underscores the potential of waste-derived carbon materials in sustainably meeting global energy storage demands.

Multi-trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea.

Multi-trait genomic selection (MT-GS) has the potential to improve predictive ability by maximizing the use of information across related genotypes and genetically correlated traits. In this study, we extended the use of sparse phenotyping method into the MT-GS framework by split testing of entries to maximize borrowing of information across genotypes and predict missing phenotypes for targeted traits without additional phenotyping expenditure. Using 300 advanced breeding lines from North Dakota State University (NDSU) pulse breeding program and ∼200 USDA accessions that were evaluated for 10 nutritional traits, our results show that the proposed sparse phenotyping aided MT-GS can further improve predictive ability by >12% across traits compared with univariate (UNI) genomic selection. The proposed strategy departed from the previous reports that weak genetic correlation is a limitation to the advantage of MT-GS over UNI genomic selection, which was evident in the partially balanced phenotyping-enabled MT-GS. Our results point to heritability and genetic correlation between traits as possible metrics to optimize and further improve the estimation of model parameters, and ultimately, prediction performance. Overall, our study offers a new approach to optimize the prediction performance using the MT-GS and further highlight strategy to maximize the efficiency of GS in a plant breeding program. The sparse-testing-aided MT-GS proposed in this study can be further extended to multi-environment, multi-trait GS to improve prediction performance and further reduce the cost of phenotyping and time-consuming data collection process.

We extended the use of sparse phenotyping into the multi-trait genomic selection (MT-GS) framework by split testing of entries. The sparse-phenotyping-aided MT-GS can increase predictive ability by >12% across traits. Heritability and genetic correlation are possible metrics to optimize and further improve prediction performance of MT-GS. The sparse-testing-aided MT-GS can be further extended to multi-environment, multi-trait GS framework.