CHEMICAL AND TOPOGRAPHICAL SURFACE MODIFICATION OF POLYMERS FOR PROTEIN IMMOBILIZATION
Bibliographic record
Abstract
When materials contact biological fluids including blood, protein adsorption occurs rapidly and the proteins present at the interface can subsequently influence the biological response, potentially causing undesired reactions and the failure of medical devices. This thesis explores the impact of surface properties on protein attachment to materials, providing crucial insights into material functionality and the response in biological conditions. To address the lack of control of proteins at interfaces, several surface modification strategies were developed including topographical, chemical and biological methods. These approaches aim to enhance the immobilization of specific proteins on polymer surfaces and obtain an improved understanding of the interactions. Bovine serum albumin (BSA), fibrinogen (Fg), fetuin-A (Fet-A) and immunoglobulin G (IgG) served as models to investigate the protein adsorption, competitive surface affinity and immobilization efficiency on modified surfaces. These desired biomolecules were immobilized on polymers using polydopamine (PDA) and newly synthesized diazirine molecules as linkers for surface conjugation with detailed characterization performed. The results indicated that micropatterned surfaces increased protein immobilization on polydimethylsiloxane (PDMS) by providing greater surface area. PDA-modified PDMS exhibited enhanced protein capacity along with good stability and combining micropatterns with PDA improved levels further. Multiple proteins were immobilized and the amounts could be controlled through either simultaneous or sequential methods. The strength of attachment of the proteins was influenced by the surrounding biological environment based on the concentrations of proteins. For diazirine-modified surfaces, activation through thermal and ultraviolet (UV) methods significantly improved both the quantity and stability of proteins immobilized on PDMS and polyurethane (PU). The diazirine conjugation approach is applicable to other substrates and can provide benefits for intricate devices and implants. Overall, this thesis contributes new knowledge to understanding protein-material interactions and provides novel and promising strategies for modifying polymer surfaces to achieve functionalized biomaterials for various medical applications.
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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 | 0.000 |
| 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".