Tunable Ultrathin Membranes with Nonvolatile Pore Shape Memory
Bibliographic record
Abstract
The concept of a responsive nanoporous thin-film gel membranes whose pores could be tuned to a desired size by a specific "molecular signal" and whose pore geometry becomes "memorized" by the gel is reported. The ∼100 nm thick membranes were prepared by dip-coating from a solution mixture of a random copolymer comprising responsive and photo-cross-linkable units and monodisperse latex nanoparticles used as a sacrificial colloidal template. After stabilization of the films by photo-cross-linking the latex template was removed, yielding nanoporous structures with a narrow pore size distribution and a high porosity. The thin-film membranes could be transferred onto porous supports to serve as tunable size-selective barriers in various colloids separation applications. The pore dimensions and hence the membrane's colloidal-particle-size cutoff were reversibly regulated by swelling-shrinking of the polymer network with a specially selected low-molar-mass compound. The attained pore shape was "memorized" in aqueous media and "erased" by treatment in special solvents reverting the membrane to the original state.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".