Assembling Surface Linker Chemistry with Minimization of Non-Specific Adsorption on Biosensor Materials
Bibliographic record
Abstract
The operation of biosensors requires surfaces that are both highly specific towards the target analyte and that are minimally subject to fouling by species present in a biological fluid. In this work, we further examined the thiosulfonate-based linker in order to construct robust and durable self-assembling monolayers (SAMs) onto hydroxylated surfaces such as silica. These SAMs are capable of the chemoselective immobilization of thiol-containing probes (for analytes) under aqueous conditions in a single, straightforward, reliable, and coupling-free manner. The efficacy of the method was assessed through implementation as a biosensing interface for an ultra-high frequency acoustic wave device dedicated to the detection of avidin via attached biotin. Fouling was assessed via introduction of interfering bovine serum albumin (BSA), IgG antibody, or goat serum. Improvements were investigated systematically through the incorporation of an oligoethylene glycol backbone employed together with a self-assembling diluent without a functional distal group. This work demonstrates that the incorporation of a diluent of relatively short length is crucial for the reduction of fouling. Included in this work is a comparison of the surface attachment of the linker to Si3N4 and AlN, both materials used in sensor technology.
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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) | 0.001 | 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 | 0.000 | 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".