Adsorption of a Carboxylated Silane on Gold: Characterization for Its Rational Use in Hybrid Glass/Gold Substrates
Bibliographic record
Abstract
Integrated sensing and biosensing microfluidic systems often require sealing between polysiloxane, glass, and gold interfaces, while maintaining functional support on the gold surface within the cell chamber (e.g., biomolecular interaction analysis). A carboxylated trimethoxysilane (TMS-EDTA) coating has been shown to facilitate the bonding of polydimethylsiloxane (PDMS) to gold and glass slides. In this work, the adsorption of TMS-EDTA onto Au is characterized in order to enable its rational use in hybrid glass/gold substrates. Surface plasmon resonance results suggest that carboxylates are available for streptavidin immobilization. Atomic force microscopy studies indicate that a uniform surface coverage with monolayer thickness is formed. Infrared spectroscopy studies confirm that the carboxyl groups are present. Moreover, there is little evidence of siloxane cross-linking. Electrochemical differential capacitance measurements reveal that the potential-dependent free energies of adsorption are ∼−20 to–30 kJ/mol (for potentials between −0.5 and 0.2 V) in the complex electrolyte solution used. Furthermore, at highly negative potentials (∼−1.1 V), TMS-EDTA desorbs from the Au surface. The fundamental knowledge obtained about TMS-EDTA adsorption on Au can be applied to construct robust PDMS-based systems with hybrid glass/gold substrates.
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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 | 0.000 |
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".