Polymer-Based Biocompatible Surface Coatings
Bibliographic record
Abstract
The performance of a material intended for biomedical applications depends on its interfacial properties and reactions that occur when come in contact with biological fluids. Non-specific protein adsorption at the biomaterial interface is the first and critical event that initializes a cascade of host responses, including platelet activation, blood coagulation, and complement activation.1,2 Many approaches have been used to prevent such non-specific interactions.3−6 Coating or immobilization of surfaces with biomacromolecules such as albumin7−12 and anticoagulants like heparin13−18 have been widely studied towards this purpose. Another approach to overcome this problem is to coat the surface with synthetic hydrophilic polymers4,19 and this method has been used as a anti-fouling treatment for a number of applications including biosensors20 and drug delivery systems.21 It was demonstrated that such coatings frequently extend the life span of biomedical devices22−23 and the circulation half life of drug delivery systems.24−25 Several factors that affect the protein-repelling properties of polymer thin films on surface include the similarity of interfacial free energies of the polymer with that of water, interaction of proteinswith polymers through hydrophobic or charge interactions and environmental factors such as temperature and pH.26−28 In the case of neutral hydrophilic polymer grafted surfaces, the steric barrier due to high conformational entropy of anchored chains is one of the contributing factor towards protein repulsion.29−35 Other factors include, the structure of the polymer on the surface (linear vs. branched), chemistry of the polymers and molecular weight of the grafted chains.
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 | 0.000 | 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) | 0.006 | 0.005 |
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".