Mass transfer considerations in solid–liquid two‐phase partitioning bioreactors: a polymer selection guide
Bibliographic record
Abstract
ABSTRACT BACKGROUND Selection of polymers for two‐phase partitioning bioreactors (TPPBs) has been focused primarily on predicting a polymer's affinity for the target molecule. Although the extent to which a polymer absorbs a solute is important, the rate of uptake/release must be sufficiently rapid such that a TPPB is not mass transfer limited. This work focused on developing a guide to identify combinations of polymer diffusivities and diffusional path lengths that will ensure a TPPB is not limited by substrate delivery. RESULTS TPPB systems limited by substrate delivery yielded linear growth, while biologically limited systems exhibited exponential growth. Release rates of phenol from various polymer phases increased as polymer diffusivity increased, or as diffusional path length (polymer bead size) decreased. A polymer selection guide was developed identifying combinations of polymer diffusivity and bead size that will ensure a TPPB is not mass transfer limited, for a desired maximum substrate consumption rate. CONCLUSION In selecting polymers for TPPB applications, solute affinity (extent of uptake) has been relatively well characterized using first principles methods, and the present work has now ‘completed the picture’ by providing a description of polymer transport properties (diffusivity and diffusional path length) to be able to generate a guide for selecting polymers. © 2015 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".