Selective and Solvent-Free Extraction of Medium-ChainFatty Acids with Polydimethylsiloxane Membranes
Bibliographic record
Abstract
Microbial production of medium-chain fatty acids (MCFAs) through anaerobic digestion of organic wastes has great potential as a method for sustainable chemical production owing to the high economic value of MCFAs, which are used in cosmetics, animal feeds, and pharmaceuticals. However, a stable, low-cost, and energy-efficient method of separating MCFAs from other microbial products, including alcohols and short-chain fatty acids (SCFAs), remains a challenge. The main proposed methods rely on organic solvents to extract MCFAs, 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 MCFAs. We first performed fundamental transport experiments with model PDMS films, finding that PDMS membranes have high MCFA permeabilities and high selectivity of MCFAs over SCFAs (e.g., a selectivity of 230 ± 46 for octanoic acid over acetic acid), comparable to 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 techno-economic assessment showed favorable economics for PDMS membranes compared to competing extraction technologies because of PDMS stability, simple implementation, and solvent-free nature. Commercial PDMS hollow fiber modules were then tested with synthetic MCFA solutions, showing robust separations with high selectivities matching the model films, albeit with lower than expected permeabilities. Last, we suggest an overall process design that could incorporate PDMS-based extraction. This work demonstrates a new path for the attainable selective separation and extraction of MCFAs, using commercially available membrane materials and/or fabrication techniques that are scalable to the industrial level.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".