Bimetallic Nanoparticles for Arsenic Detection
Bibliographic record
Abstract
Effective and sensitive monitoring of heavy metal ions, particularly arsenic, in drinking water is very important to risk management of public health. Arsenic is one of the most serious natural pollutants in soil and water in more than 70 countries in the world. The need for very sensitive sensors to detect ultralow amounts of arsenic has attracted great research interest. Here, bimetallic FePt, FeAu, FePd, and AuPt nanoparticles (NPs) are electrochemically deposited on the Si(100) substrate, and their electrochemical properties are studied for As(III) detection. We show that trace amounts of As(III) in neutral pH could be determined by using anodic stripping voltammetry. The synergistic effect of alloying with Fe leads to better performance for Fe-noble metal NPs (Au, Pt, and Pd) than pristine noble metal NPs (without Fe alloying). Limit of detection and linear range are obtained for FePt, FeAu, and FePd NPs. The best performance is found for FePt NPs with a limit of detection of 0.8 ppb and a sensitivity of 0.42 μA ppb(-1). The selectivity of the sensor has also been tested in the presence of a large amount of Cu(II), as the most detrimental interferer ion for As detection. The bimetallic NPs therefore promise to be an effective, high-performance electrochemical sensor for the detection of ultratrace quantities of arsenic.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".