End‐Anchored Polymers: Compression by Different Mechanisms and Interpenetration of Apposing Layers
Bibliographic record
Abstract
Abstract Summary: This paper presents a systematic study of the compression of end‐anchored polymer layers by a variety of mechanisms. We treat layers in both good and Θ solvents, and in the range of polymer densities that is normally encountered in experiments. Our primary technique is numerical self‐consistent field (NSCF) theory. We compare the NSCF results for the different mechanisms with each other, and with those of the analytic SCF theory. For each mechanism, we calculate the density profiles, layer thicknesses, and free energies, all as functions of the degree of polymerization and surface coverage. The free energy and the deformation of each layer depend on the compression mechanism, and they can be very different from the ASCF theory. For example, the energy of compression can be as much as three times greater than the analytical SCF (ASCF) prediction, and it does not reduce to simple, universal functions of the reduced distance between the surfaces. The overall physical picture simplifies if the free energy is expressed in terms of the layer deformation, rather than the reduced surface separation. We also examine and quantify the interpenetration of layers, discuss why ASCF theory applies better to some compression mechanisms than others, and end with comments on the difficulties in extracting quantitative information from surface‐forces experiments. Comparisons of forces of compression in a good solvent for the three different systems, as functions of D/nb. The lower three curves are for σ* = 3, and the upper three are for σ* = 23. image Comparisons of forces of compression in a good solvent for the three different systems, as functions of D/nb. The lower three curves are for σ* = 3, and the upper three are for σ* = 23.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".