Selecting polymers for two‐phase partitioning bioreactors ( <scp>TPPB</scp> s): Consideration of thermodynamic affinity, crystallinity, and glass transition temperature
Bibliographic record
Abstract
Two-phase partitioning bioreactor technology involves the use of a secondary immiscible phase to lower the concentration of cytotoxic solutes in the fermentation broth to subinhibitory levels. Although polymeric absorbents have attracted recent interest due to their low cost and biocompatibility, material selection requires the consideration of properties beyond those of small molecule absorbents (i.e., immiscible organic solvents). These include a polymer's (1) thermodynamic affinity for the target compound, (2) degree of crystallinity (wc ), and (3) glass transition temperature (Tg ). We have examined the capability of three thermodynamic models to predict the partition coefficient (PC) for n-butyric acid, a fermentation product, in 15 polymers. Whereas PC predictions for amorphous materials had an average absolute deviation (AAD) of ≥16%, predictions for semicrystalline polymers were less accurate (AAD ≥ 30%). Prediction errors were associated with uncertainties in determining the degree of crystallinity within a polymer and the effect of absorbed water on n-butyric acid partitioning. Further complications were found to arise for semicrystalline polymers, wherein strongly interacting solutes increased the polymer's absorptive capacity by actually dissolving the crystalline fraction. Finally, we determined that diffusion limitations may occur for polymers operating near their Tg , and that the Tg can be reduced by plasticization by water and/or solute. This study has demonstrated the impact of basic material properties that affects the performance of polymers as sequestering phases in TPPBs, and reflects the additional complexity of polymers that must be taken into account in material selection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".