Environment-Responsive Membranes for Bioseparations
Bibliographic record
Abstract
Environment-responsive membranes were created by modification of a commerical polyvinylidene fluoride (PVDF) membrane support with a thermo-responsive hydrogel composed of poly N-vinyllactams cross-linked with bisacrylamide. The modified membranes were then characterized by their percentage mass gains as well as by their valve effect in response to changes in salt concentration. One set of membranes, with a large valve effect, was selected for highest retention of intermediately sized proteins was examined for ultrafiltration-based protein separation applications. A batch separation protocol featuring pulsed sample injection technique (PSIT) was then used to sieve single proteins and to fractionate a synthetic binary protein mixture and a synthetic ternary protein mixture with some success, demonstrating the potential of these environment-responsive membranes for use in multi-component separations. A second set of membranes, with a small valve effect, was selected for its ability to alter between hydrophobic and hydrophilic states under different environmental conditions and its potential in hydrophobic interaction membrane chromatography (HIMC) applications was successfully demonstrated by comparing against a benchmark membrane that is used successfully for HIMC applications in prior literature.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".