Carboxymethylated Dextran-Modified <i>N</i>-Heterocyclic Carbene Self-Assembled Monolayers on Gold for Use in Surface Plasmon Resonance Biosensing
Bibliographic record
Abstract
Surface chemistry is a key enabler for various biosensing applications. Biosensors based on surface plasmon resonance routinely employ thiol-based chemistry for the linker layer between gold-coated support surfaces and functional biosensor surfaces. However, there is a growing awareness that such sensor surfaces are prone to oxidation/degradation problems in the presence of oxygen, and previous efforts to improve the stability have shown limited advancements. As an alternative, recent studies employing N-heterocyclic carbene (NHC) self-assembled monolayers (SAMs) deposited on gold have shown significant promise in this area. Here, we describe a sensor surface employing an NHC SAM to couple a modified carboxymethylated dextran onto a gold surface. Such a dextran matrix is also used for affinity chromatography, and it is the most commonly employed matrix for commercial biosensor surfaces today. The performance reliability of the dextran-modified NHC chip to act as an alternative biosensing platform is compared with that of a thiol-based commercial chip in the proof-of-concept tests. The resultant NHC sensor surface shows a higher thermal stability compared to thiol analogues. Moreover, the plasma protein/drug and antibody/antigen interactions were validated on the NHC-based dextran chip and showed similar performance as compared to the thiol-based commercial chip. Ultimately, this study shows the strong potential applicability of chemical modifications to gold surfaces using NHC ligands for biosensing applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".