Selective and Solvent-Free Extraction of Medium-Chain Carboxylic Acids with Poly(dimethylsiloxane) Membranes

Microbial production of medium-chain carboxylic acids (MCCAs) through anaerobic digestion of organic wastes has great potential as a method for sustainable chemical production, owing to the high economic value of MCCAs, which are used in cosmetics, animal feeds, and pharmaceuticals. However, a stable, low-cost, and energy-efficient method of separating MCCAs from other microbial products, including alcohols and short-chain carboxylic acids (SCCAs), remains a challenge. The main proposed methods rely on organic solvents to extract MCCAs, leading to toxicity, cost, and processing challenges. In this work, we explore the use of polydimethylsiloxane (PDMS) membranes for robust and selective solvent-free extraction of MCCAs. We first performed fundamental transport experiments with model PDMS films, finding that PDMS membranes have high MCCA permeabilities and high selectivity of MCCAs over SCCAs (e.g., a selectivity of 233 ± 59 for octanoic acid over acetic acid), comparable to those of solvent-based extractions. As PDMS can easily be formed as thin selective layers on porous supports, we then modeled the performance of PDMS-based selective layers of various thicknesses. A preliminary technoeconomic assessment suggested favorable economics for PDMS membranes (e.g., 80% decrease in operating cost compared to supported liquid membranes and pertraction) because of PDMS stability, simple implementation, and solvent-free nature. Commercial PDMS hollow-fiber modules were then tested with synthetic MCCA solutions, showing robust separations with high selectivities matching the model films, albeit with lower-than-expected permeabilities. Last, we discuss scale-up paths and suggest an overall process design that could incorporate PDMS-based extraction. This work demonstrates a robust strategy for selective separation and extraction of MCCAs, using commercially available membrane materials or fabrication techniques that are scalable to the industrial level.