Ergotropy Dynamics in a Dissipative Graphene Quantum Battery

TL;DR

This paper studies how different dissipative environments affect the energy extraction potential of a graphene-based quantum battery, highlighting the roles of coherence and reservoir memory in its performance.

Contribution

It introduces a detailed analysis of ergotropy dynamics in a graphene quantum battery under various dissipative conditions, emphasizing the importance of non-Markovian effects.

Findings

Amplitude damping can stabilize finite ergotropy despite energy loss.

Pure dephasing suppresses coherence and work extraction.

Non-Markovian memory effects slow ergotropy loss and enable partial recovery.

Abstract

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.