Detection of Cu2+ ion with 100-fold improvement using mercaptobenzoic acid-capped Au nanoparticles purified by pH selective precipitation
Bibliographic record
Abstract
Citrate-capped Au nanoparticles (NPs) are of much interest as they can be synthesized with robust control over sizes; also, they can serve as a detection platform for various target species for interest. For example, metal ions can bind to negative carboxylic groups in the citrate caps, resulting in increased NP-NP interactions and color change due to a shift in Au NP plasmon resonance. Here we provide a method for switching the capping group to mercaptobenzoic acid (MBA) and purifying the NPs by using pH selective precipitation (PSP) to remove excess citrate and MBA from solution. We show that such purified MBA-capped NPs enable detection of Cu 2+ ions by-eye at 10 -5 M, a 100-fold increase in sensitivity compared with citrate-capped NP and unpurified NPs. We also study aggregation kinetics and X-ray photoemission spectroscopy (XPS) of Au NP films functionalized with MBA. Using Cu 2+ as a test bed, our results provide important insight into the nature and improved application of binding between metal ions and MBA-capped Au NPs.
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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) | 0.001 | 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 | 0.001 | 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".