Coupling Analysis of Thermodynamic Equilibrium of Mass Transfer and Microbial Community Succession in Multiphase System of Dark Tea Solid-State Fermentation

Abstract

During the solid-state fermentation of dark tea, the limited material migration, the dynamic evolution of the thermodynamic equilibrium state, and the highly dynamic succession of the microbial community make it difficult to analyze the quality formation mechanism systematically. Traditional research has focused on microbial diversity or sensory quality analysis, lacking a systematic discussion on the regulatory effects of material migration and changes in thermodynamic conditions on microbial behavior. This paper proposes a "multiphase, thermodynamic, and microecological coupling modeling method" based on Fick's diffusion principle and interfacial resistance theory to simulate the migration behavior of key substances (such as oxygen, water, and metabolites) between the gas, solid, and liquid three-phase interfaces. Gibbs free energy and chemical potential gradient are used to evaluate the thermodynamic feasibility of microbial metabolic reactions and explain the energy-driven mechanism in the fermentation process. Combining high-throughput sequencing and network analysis technology, the changes in microbial community structure and functional evolution are dynamically monitored. Through multi-scale data integration and system modeling, this paper constructs the coupling equations between material transfer, energy change, and microecological succession in the solid-state fermentation process of dark tea. It proposes a "metabolic potential field, mass transfer gradient" synergistic driving theoretical paradigm. The model has a fitting result R² greater than 0.9 for Gibbs free energy transformation, chemical potential gradient, oxygen and water mass transfer flux, and reaction enthalpy change indicators in the solid-state fermentation process of dark tea, based on a dataset comprising 45 independent fermentation samples collected across five time points with three biological replicates per time point, with the goodness-of-fit standard errors ranging from 0.02 to 0.05 across indicators. The model demonstrates high-precision prediction capability for the dynamic changes of microbial communities, with cross-validation accuracy exceeding 79% evaluated through five-fold cross-validation on the same dataset. This study suggests that the thermodynamic equilibrium state in dark tea fermentation is associated with the allocation of microbial metabolic resources through the Gibbs free energy gradient, supporting an interpretation of ecological selection pressure characterized by the relative enrichment of high-energy-efficiency bacteria. This inferred energy cascade regulation links macroscopic mass transfer to microscopic bacterial community structure, providing a quantifiable thermodynamic design paradigm for the fermentation process.