Rapid and selective lead (II) colorimetric sensor based on azacrown ether-functionalized gold nanoparticles
Bibliographic record
Abstract
A gold nanoparticle (AuNPs)-based simple and fast colorimetric sensor for selective detecting of Pb(II) in aqueous solution has been developed. Monodisperse AuNPs (approx. 2.0 nm diameter) has been prepared facilely and further modified with an alkanethiol-bearing monoazacrown ether terminus. These AuNPs are shown to selectively sense Pb(2+) through color change, which is visually discernible by an appearance of the surface plasmon band (SPB) at 520 nm. The recognition mechanism is attributed to the unique structure of the monoazacrown ether attached to AuNPs and metal sandwich coordination between two azacrown ether moieties that are attached to separate nanoparticles. This inter-particle cross-linking results in an aggregation and apparent color change from brown to purple. Additionally, TEM experiments support the optical absorption data proving the aggregation between azacrown ether-capped gold nanoparticles. This AuNP-based colorimetric assay is a facile and robust method and allows fast detection of Pb(2+) at ambient temperatures. More importantly, the developed technique does not utilize enzymatic reactions, light-sensitive dye molecules, lengthy protocols or sophisticated instrumentation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".