AI-driven optimization of microbial co-cultures and extractive fermentation for next-generation biorefineries

Extractive co-culture fermentation leverages microbial synergies for biofuel and biochemical production, but industrial translation is hindered by the absence of a materials-centered design framework. Separation media including polymer–salt aqueous two-phase systems (ATPS), ionic liquids (ILs), and deep eutectic solvents (DES/NADES) are currently reported as empirical process add-ons rather than designable material systems with quantifiable structure–property–performance relationships. This review reframes extractive co-culture bioprocessing around materials intelligence: the systematic design, evaluation, and optimization of separation media as functional interfaces between microbial metabolism and downstream recovery. We introduce a standardized evaluation matrix comprising ten quantitative metrics: partition coefficient (K), selectivity (α), phase stability, binodal curve/tie-line length, mass transfer coefficient (k L a), biocompatibility (IC 50 ), regenerability, solvent loss, downstream energy intensity, and sustainability score, with associated measurement protocols. To navigate the high-dimensional design space of solvent–microbe–process interactions, we map artificial intelligence (AI) to four task-defined materials challenges: (i) property prediction and high-throughput screening, (ii) structure–property–performance mapping, (iii) multi-objective optimization (efficiency–toxicity–energy trade-offs), and (iv) digital twin-enabled closed-loop control. A dedicated analytical section examines sampling bias, phase carryover, matrix effects, and calibration transfer constraints that shape both material selection and model reliability. We conclude with a materials-first research agenda for next-generation smart biorefineries.