Direct Micropatterning of Phase Separation Membranes Using Hydrogel Soft Lithography
Bibliographic record
Abstract
Abstract In this work a novel method is presented to directly apply microscale patterns on the membrane surface using hydrogel facilitated phase separation (HFPS). The hydrogel mold initiates phase separation spontaneously when it contacts the polymer solution and this guarantees that location of the dense skin layer is on the patterned side. In this fashion, the active surface area of a membrane is larger than the equivalent flat surface and subsequently enhances water flux without changing the membrane surface chemistry. The morphological properties of the HFPS membranes show similarity to the nonsolvent induced phase separation ones; however, a pore enlarging is noticed in the HFPS membranes due to the slow demixing rate of the solvent/nonsolvent in the phase separation process. The permeation results show that the HFPS patterned membrane doubles the pure water permeate flux when compared to the HFPS unpatterned membrane. This increase is attributed to the combined effect of enhanced surface area and a slight increase in the average pore size of the membrane. Moreover, fouling experiments with bovine serum albumin solution show a 78% increment in the flux for the patterned membrane after 100 min of operation, demonstrating the advantage of using microstructured membrane for filtration applications.
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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 | 0.000 | 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) | 0.001 | 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".