Interactions between Caffeine, Theophylline and Derivatives with Gold Nanoparticles and Implications for Aptamer‐Based Label‐Free Colorimetric Detection
Bibliographic record
Abstract
Caffeine, theophylline, and other methylxanthines have interesting biological activities and are consumed in high quantities globally, causing health and environmental concerns. Gold nanoparticles (AuNPs) have excellent optical properties for biosensor development, although little is known about the adsorption of these xanthine derivatives to AuNPs. In this work, interactions of these compounds with AuNPs were studied. Caffeine, theophylline and theobromine are adsorbed in a manner that affords protection against salt-induced aggregation, whereas xanthine and paraxanthine are adsorbed to destabilize and thus aggregate the AuNPs. Caffeine and theophylline are able to protect AuNPs starting at concentrations as low as 6.3 μM. Xanthine and paraxanthine induce significant AuNP aggregation at 5 μM. Adsorption was also confirmed by surface-enhanced Raman scattering (SERS). Using two recently selected DNA aptamers for caffeine and theophylline, the label-free colorimetric sensing method was tested; our results indicated that due to adsorption of these target molecules, this method cannot be directly used for their detection. The adsorption of these compounds to AuNPs may enable various detection methods such as SERS, but at the same time, it may complicate other detection methods.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".