Polymer Brushes
Bibliographic record
Abstract
Abstract Polymer brushes are monolayers of one‐end tethered polymer chains at high graft density on a surface. Owing to the steric interactions between the tethered polymer chains within the brush regime, this class of polymer thin films has unique properties compared to the conventional polymer thin films. Polymer brushes provide an elegant route for surface modification owing to their excellent mechanical stability as well as functional versatility. Among the polymer brush synthesis methods, surface‐initiated polymerization (SIP) offers a unique opportunity for the generation of high graft density polymer brushes. Combined with controlled/living polymerization, the SIP method revolutionized the polymer brush synthesis and allowed the tailoring of surface properties of these molecular thin films. The control of surface properties provided new ways to change wettability, lubrication, and the interaction of biological macromolecules/cells to surfaces and created new opportunities in the design of responsive surfaces, smart materials, biocompatible surfaces, and various biotechnology and nanotechnology applications. This field is rapidly emerging with wide range of applications in multiple research fields and is promoting cross‐disciplinary research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.012 |
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".