Amalgamated gold-nanoalloys with enhanced catalytic activity for the detection of mercury ions (Hg2+) in seawater samples
Bibliographic record
Abstract
Abstract Mercury (Hg) is extremely toxic, and continues to cause major threats to aquatic life, human health and the environment. Hg2+ mainly derives from seawater as a product of atmospheric deposition, therefore there is great demand for sensing approaches that can detect Hg2+ in seawater samples. Herein, we demonstrate that the peroxidase-mimicking activity of gold nanoparticles (AuNPs) or so-called nanozymes, can be exploited for the detection of Hg2+ ions in various water samples. In a high electrolyte environment, the catalytic activity for the oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB) was significantly diminished due to poor stability of the bare-AuNPs. This activity was reduced by ∼ 73.7% when the NaCl concentration was higher than 1.168%, which is much lower than that of seawater (∼ 3.5%), thus presenting its unsuitability for detecting Hg2+ in harsh water matrices. To overcome this limitation, AuNPs were first functionalized with oligo-ethylene glycol (OEG), of which their colloidal form presented high stability in NaCl concentrations up to 20% and across a wide range of pHs from 1–14. Interestingly, the catalytic activity of OEG-AuNPs for the oxidation of TMB was strongly suppressed by the coating, but enhanced upon formation of Au-Hg amalgamation. This novel finding underlies a straightforward, sensitive, and highly selective detection platform for Hg2+ in water samples. The approach could detect the exposure limit level for Hg2+ in drinking water (i.e., 2 ppb for tap and bottled water) as set by the United States Environmental Protection Agency (EPA) and the World Health Organization (WHO). When Hg2+ was spiked into a 3.5% saline solution and a coastal seawater certified reference material (CRM), the detection limits were found to be 10 and 13 ppb, respectively, which exceed the Hg2+ concentrations commonly found within seawater (~ 60–80 ppb). The whole procedure takes less than 45 min to conduct, providing a highly innovative, rapid and low-cost approach for detecting Hg2+ in complex water matrices.
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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 | 0.000 |
| 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) | 0.000 | 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".