Adsorption of tetracycline antibiotics to gold nanoparticles and feasibility of aptamer-based label-free colorimetric detection
Bibliographic record
Abstract
Tetracyclines are a group of very important antibiotics that are still in use. To extract, detect, and remove tetracyclines from the environment, various nanomaterials have been employed. Although gold nanoparticles (AuNPs) are a commonly cited material for these purposes, a fundamental understanding of these tetracycline-AuNP systems is still limited. In this work, the adsorption of tetracycline, oxytetracycline, and doxycycline to AuNPs was studied. The effect on the colloidal stability of AuNPs, adsorption kinetics, and the resulting adsorption isotherms was measured. While millimolar concentrations of the tetracyclines can cause aggregation of AuNPs, saturated monolayer adsorption was achieved with low micromolar concentrations of the tetracyclines. Adsorption was instantaneous, and adsorption to AuNPs enhanced their intrinsic fluorescence instead of quenching. With the assumption that aptamer/target complexes cannot be easily adsorbed by AuNPs compared to free aptamers, a label-free colorimetric detection method was tested. While the label-free sensor showed target-dependent aggregation of AuNPs, a nonbinding mutant aptamer showed the same trend, suggesting that the color change did not reflect aptamer adsorption but other events such as target adsorption. This study indicates the importance of the fundamental understanding of target/AuNP interactions to correctly design aptamer and AuNP-based label-free biosensors.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".