Photosynthetic Biohybrid Batteries: A Spectral Energy Transfer Framework

The document discusses the design and engineering of artificial photosynthetic systems for energy storage, aiming to create high-efficiency biohybrid batteries. It covers the following key aspects: * Theoretical Foundation: The light-to-charge conversion in photosystems is modeled using a Förster-Dexter operator, with a focus on exciton transport and eigenmodes. The dominant mode facilitates coherent exciton delocalization, while the loss mode involves self-trapping at defects. * Biohybrid Battery Design: The proposed battery utilizes a cytochrome c_6-coated graphene anode and a Mn_4CaO_5 cluster cathode, with a proton-conducting polymer electrolyte. The design incorporates spectral matching to solar flux to maximize efficiency, with a theoretical maximum of approximately 35%. * Degradation Dynamics and Control: The document addresses degradation modes, such as photobleaching, and proposes model predictive control (MPC) strategies to stabilize the system using sensors and adjusting incident photon flux. * Performance Metrics: The biohybrid battery is projected to offer advantages over lithium-ion batteries, including higher energy density (500 Wh/kg), faster charging rate (10C), longer cycle life (10^6 cycles), and lower cost ($20/kWh). * Experimental Validation: The document includes spectroscopic proof and prototype performance data, showing promising results for current density and stability. * Challenges and Solutions: It identifies challenges like exciton recombination, proton leakage, and scalability, and proposes solutions such as plasmonic AuNPs, graphene oxide proton membranes, and roll-to-roll chloroplast deposition. * Stability and Scalability Solutions: This section details strategies for stabilizing photosystems outside cells, enhancing bio-abiotic interfaces, preserving quantum coherence, and enabling scalable manufacturing. Techniques include CRISPR-guided mutagenesis, protein encapsulation, graphene quantum dots, and DNA origami scaffolds. * Proton-Coupled Electron Transfer (PCET) Optimization: The document explores maximizing energy efficiency by controlling proton-electron synergy, leveraging quantum coherence and operator-theoretic stability analysis. * Multi-Proton Tunneling: It discusses leveraging the Coherence Evolution Model (CEM) to exploit collective proton tunneling for ultra-efficient energy conversion.