We investigate ergotropy dynamics in a graphene-based quantum battery modeled as a four-level spin--valley system under different dissipative environments. The battery is charged via a Gaussian pulse and subsequently evolves under amplitude damping, dephasing, and both Markovian and non-Markovian reservoirs. We find that amplitude damping, while inducing energy loss, can stabilize non-passive steady states with finite ergotropy, whereas pure dephasing suppresses coherence and eliminates work extraction. On the other hand, non-Markovian memory slows ergotropy loss and enables partial recovery through information backflow. These results identify coherence and reservoir memory as essential resources for enhancing the long-time performance of graphene quantum batteries.
@article{arxiv.2511.12666,
title = {Ergotropy Dynamics in a Dissipative Graphene Quantum Battery},
author = {Disha Verma and Indrajith VS and R. Sankaranarayanan},
journal= {arXiv preprint arXiv:2511.12666},
year = {2026}
}