Fast self-assembly kinetics of alkanethiols on gold nanoparticles: simulation and characterization by localized surface plasmon resonance spectroscopy
Bibliographic record
Abstract
This study demonstrates improved kinetics for the formation of self-assembled monolayers (SAMs) of alkanethiols on gold nanoparticle substrates. A computational model was developed to predict SAM growth kinetics. Based on the predictions from the model, SAMs of 11-mercaptoundecanoic acid (11-MUA) and 1-octanethiol (1-OT) were formed by incubation of gold nanoparticle chips in an ethanolic 10 mM solution within 20 min. The performance of this novel rapid SAM formation protocol was compared with a conventional 24 hour incubation protocol. Binding capacity of the alkanethiol SAM was investigated for a 20 min incubation protocol using biotin-streptavidin. For this purpose, the SAM loaded gold nanoparticle chips were modified with 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) to allow attachment of EZ-Link amine PEG3 biotin to the 11-MUA molecules. Binding reactions were monitored in real time using localized surface plasmon resonance (LSPR) spectroscopy. The resulting LSPR absorbance peak shift was comparable to the experimental results for biotin-streptavidin reported in literature. Results of this study suggest that formation of a high quality alkanethiol SAM within 20 min on gold nanoparticles surfaces is possible and could greatly reduce the time and cost compared to conventional 24 h incubation protocols.
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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 | 0.001 | 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".