Q. Zhang

Zhang, Q. ., Khetan, A. ., Sorkun, E. ., Niu, F. ., Loss, A. ., Pucher, I. ., & Er, S. . (2022). Data-driven discovery of small electroactive molecules for energy storage in aqueous redox flow batteries. Energy Storage Materials, 47, 167-177. https://doi.org/10.1016/j.ensm.2022.02.013 (Original work published 2022)

Sorkun, E. ., Zhang, Q. ., Khetan, A. ., Sorkun, M. C., & Er, S. . (2022). RedDB, a computational database of electroactive molecules for aqueous redox flow batteries. Nature Scientific Data, 9, 718. https://doi.org/10.1038/s41597-022-01832-2 (Original work published 2022)

Zhang, Q. ., Khetan, A. ., Sorkun, E. ., & Er, S. . (2022). Discovery of aza-aromatic anolytes for aqueous redox flow batteries via high-throughput screening. Journal of Materials Chemistry A, 10(41), 22214-22227. https://doi.org/10.1039/D2TA05674G (Original work published 2022)

Zhang, Q. ., Khetan, A. ., & Er, S. . (2021). A Quantitative Evaluation of Computational Methods to Accelerate the Study of Alloxazine-Derived Electroactive Compounds for Energy Storage. Scientific Reports, 11, 4089. https://doi.org/10.1038/s41598-021-83605-2 (Original work published 2021)

Zhang, Q. . (2021). A computational approach for high-throughput virtual screening of organic electroactive compounds for aqueous redox flow batteries (Eindhoven University of Technology). Eindhoven University of Technology, Eindhoven, Netherlands. Retrieved from https://research.tue.nl/en/publications/a-computational-approach-for-high-throughput-virtual-screening-of (Original work published)

Zhang, Q. ., Khetan, A. ., & Er, S. . (2020). Comparison of computational chemistry methods for the discovery of quinone-based electroactive compounds for energy storage. Scientific Reports, 10, 22149. https://doi.org/10.1038/s41598-020-79153-w (Original work published 2020)