Synthesis and characterization of novel glycosurfaces by ATRP
Bibliographic record
Abstract
The direct synthesis of well defined sugar methacrylate-based homopolymer brushes with high grafting densities based on D-gluconamidoethyl methacrylate (GAMA) and 2-lactobionamidoethyl methacrylate (LAMA) from functionalized gold substrates was carried out using surface-initiated atom transfer radical polymerization (ATRP). A good control of the polymerization leading to an increase in the dry film thickness with reaction time was achieved in mixed methanol/water solvent media. However, grafted glycopolymer films of low thicknesses, which did not increase further with longer polymerization times were synthesized in water, suggesting the premature termination of the polymerization in the aqueous solvent. Attenuated total reflectance (ATR)-FTIR spectroscopy confirmed the successful grafting of the glycopolymer brushes on the modified gold substrates, while atomic force microscopy (AFM) verified that the anchored film covered the substrate surface completely and homogeneously. The surface roughness found by AFM was below 1 nm suggesting the preparation of very smooth glycopolymer films. The grafting of the glycopolymer chains onto the gold substrates afforded an increase in the surface hydrophilicity as confirmed by contact angle measurements. The synthesized glycopolymer films exhibited strong binding interactions with specific lectins via the “glycocluster” effect.
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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 | 0.000 |
| 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 | 0.001 |
| 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".