Biocompatibility of low molecular weight polymers for two‐phase partitioning bioreactors
Bibliographic record
Abstract
Two phase partitioning bioreactors (TPPBs) improve the efficiency of fermentative processes by limiting the exposure of microorganisms to toxic solutes by sequestering them into a non-aqueous phase (NAP). A potential limitation of this technology, when using immiscible organic solvents as the NAP, is the cytoxicity that these materials may exert on the microbes. An improved TPPB configuration is one in which polymeric NAPs are used to replace organic solvents in order to take advantage of their low cost, improved handling qualities, and biocompatibility. A recent study has shown that low molecular weight polymers may confer improved solute uptake relative to high molecular weight polymers (i.e., have higher partition coefficients), but it is unknown whether sufficiently low molecular weight polymers may inhibit cell growth. This study has investigated the biocompatibility of a range of low molecular weight polymers, and compared trends in biocompatibility to the well-established "critical log P" concept. This was achieved by determining the biocompatibility of polypropylene glycol polymers over a molecular weight (MW) range of 425-4,000 to Saccharomyces cerevisiae and Pseudomonas putida, two organisms which have been previously used in TPPB systems. The lower MW polymers were shown to have lower average log P values, and showed more cytotoxicity than polymers of the same structure but with higher molecular weight. Since polymers are generally polydisperse (i.e., polymer samples contain a distribution of MWs), removal of the lower MW fractions via water washing was found to result in improved polymer biocompatibility. These results suggest that the critical log P concept remains useful for describing the toxicity of polymeric substances of different MWs, although it is complicated by the presence of the low MW fractions in the polymers arising from polydispersity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 teacher head, 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".