A framework to predict and experimentally evaluate polymer–solute thermodynamic affinity for two‐phase partitioning bioreactor (<scp>TPPB</scp>) applications
Bibliographic record
Abstract
ABSTRACT BACKGROUND Selection of a polymer for two‐phase partitioning bioreactor (TPPB) applications has previously been limited to heuristic approaches. However, recent interest has focused on first principles' selection methods based on polymer crystallinity, glass transition temperature and polymer–solute thermodynamic affinity. In this work, a framework is proposed to evaluate and predict polymer–solute thermodynamic affinity via the polymer‐phase activity coefficient. RESULTS Polymer screening via thermodynamic affinity was shown to be most effective at very dilute concentrations, where partition coefficients can be estimated using infinite dilution activity coefficients. In the absence of published values, UNIFAC‐vdW‐FV or Flory–Huggins based activity models can provide very good predictions for the polymer‐phase activity coefficient, significantly improving upon previous approaches using Hildebrand and Hansen solubility parameter differences. For non‐dilute systems, however, the activity models failed to consider the full effects of concentration on partition coefficient. Additionally, a reduction in polymer molecular weight resulted in improved partition coefficients, a phenomena well described by the activity models. CONCLUSION Predicting and experimentally quantifying polymer–solute thermodynamic affinity at very dilute concentrations will aid future attempts at TPPB polymer selection. Furthermore, experimental partition coefficient data at a range of operational concentrations will indicate how TPPB effectiveness will change throughout the fermentation course. Finally, reduction of polymer molecular weight to improve solute partitioning should be investigated further for a range of polymers. © 2014 Society of Chemical Industry
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".