A General Approach to Predict and Tailor the Nanoscale Permeability of Comb‐Shaped Polymer Coatings
Bibliographic record
Abstract
Comb-shaped polymers such as poly(oligoethylene glycol monomethyl ether) methacrylate) (pOEGMA) are used to produce molecular sieving coatings on proteins. The mechanisms and phenomena implicated in the experimentally observed sieving properties have been recently characterized by simulation. These result from an interplay between steric hindrance, microenvironmental effects, and modified protein dynamics. Steric hindrance, in particular, is expected to vary considerably as a function of the geometric parameters of the system (protein size, polymer size, grafting density, etc.). In this work, the steric and size-selective permeability characteristics of comb-polymer coatings are systematically explored across a very broad parameter space to gain a universal and predictive view of molecular sieving, for application to systems with different dimensions. All features of the data can be understood when three distinct regimes are considered: i) no-interactions, ii) weak-interactions, and iii) strong-interactions between adjacent polymer chains. Primary and secondary considerations are provided for tuning coating properties to adjust the size threshold of molecular sieving. The corresponding qualitative physical pictures and quantitative analyses give a robust framework to understand molecular sieving that will accelerate the development of future coatings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".