Quantification of Primary Amine Groups Available for Subsequent Biofunctionalization of Polymer Surfaces
Bibliographic record
Abstract
Biocompatible polymers are commonly functionalized with specific moieties such as amino groups to modify their surface properties and/or to attach bioactive compounds. A reliable method is usually required to characterize amino group surface densities. In this study, aminated polyethylene terephthalate (PET) films were generated via an aminolysis reaction involving either ethylenediamine molecules (EtDA), in order to vary easily the amino group density on PET surfaces, or 25 kDa polyvinylamine (PVAm) as an alternative reagent preventing bulk damages resulting from the aminolysis reaction. Among commonly used dyes for amino group quantification, Orange II and Coomassie Brillant Blue (CBB) were selected to quantify the extent of amine grafting resulting from these derivatization procedures. Rapid and convenient colorimetric assays were compared to surface atomic compositions obtained from X-ray photoelectron spectroscopy (XPS) measurements. Orange II was found to be the most appropriate dye for quantifying primary amine groups in a reliable and specific way. Due to its unique negative charge and low steric hindrance compared to CBB, the Orange II dye was very sensitive and provided reliable quantification over a wide range of amino group surface densities (ca. 5 to at least 200 pmol/mm(2)). In order to further validate the use of the Orange II dye for amino group quantification, a heterobifunctional linker reacting with amino groups was then grafted on modified PET surfaces. Interestingly, the good correlation between the densities of adsorbed Orange II and covalently grafted linkers suggests that the Orange II method is a relevant, reliable, easy, and inexpensive method to predict the amount of amino groups available for subsequent functionalization of polymer surfaces.
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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 | 0.000 |
| 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 | 0.001 | 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".