Engineering Surfaces for Bioconjugation: Developing Strategies and Quantifying the Extent of the Reactions
Bibliographic record
Abstract
This study presents two-step and multistep reactions for modifying the surface of plasma-functionalized poly(tetrafluoroethylene) (PTFE) surfaces for subsequent conjugation of biologically relevant molecules. First, PTFE films were treated by a radiofrequency glow discharge (RFGD) ammonia plasma to introduce amino groups on the fluoropolymer surface. This plasma treatment is well optimized and allows the incorporation of a relative surface concentration of approximately 2-3.5% of amino groups, as assessed by chemical derivatization followed by X-ray photoelectron spectroscopy (XPS). In a second step, these amino groups were further reacted with various chemical reagents to provide the surface with chemical functionalities such as maleimides, carboxylic acids, acetals, aldehydes, and thiols, that could be used later on to conjugate a wide variety of biologically relevant molecules such as proteins, DNA, drugs, etc. In the present study, glutaric and cis-aconitic anhydrides were evaluated for their capability to provide carboxylic functions to the PTFE plasma-treated surface. Bromoacetaldehyde diethylacetal was reacted with the aminated PTFE surface, providing a diethylacetal function, which is a latent form of aldehyde functionality. Reactions with cross-linkers such as sulfo-succinimidyl derivatives (sulfo-SMCC, sulfo-SMPB) were evaluated to provide a highly reactive maleimide function suitable for further chemical reactions with thiolated molecules. Traut reagent (2-iminothiolane) was also conjugated to introduce a thiol group onto the fluoropolymer surface. PTFE-modified surfaces were analyzed by XPS with a particular attention to quantify the extent of the reactions that occurred on the polymer. Finally, surface immobilization of fibronectin performed using either glutaric anhydride or sulfo-SMPB activators demonstrated the importance of selecting the appropriate conjugation strategy to retain the protein biological activity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".