Sensitive and selective electrochemical detection of chromium(<scp>vi</scp>) based on gold nanoparticle-decorated titania nanotube arrays
Bibliographic record
Abstract
Owing to the severe toxicity and mobility of Cr(VI) in biological and environmental systems, it is of great importance to develop convenient and reliable methods for its detection. Here we report on a facile and effective electrochemical technique for monitoring Cr(VI) concentrations based on the utilization of Au nanoparticle-decorated titania nanotubes (TiO2NTs) grown on a titanium substrate. It was found that the electrochemical reduction of Cr(VI) at the Ti/TiO2NT/Au electrode exhibited an almost 23 fold improvement in activity as compared to a polycrystalline gold electrode, due to its nanoparticle/nanotubular heterojunction infrastructure. As a result, the Ti/TiO2NT/Au electrode demonstrated a wide linear concentration range from 0.10 μM to 105 μM, a low detection limit of 0.03 μM, and a high sensitivity of 6.91 μA μM(-1) Cr(VI) via amperometry, satisfying the detection requirements of the World Health Organization (WHO). Moreover, the Ti/TiO2NT/Au electrode exhibited good resistance against interference from coexisting Cr(III) and other metal ions, and excellent recovery for Cr(VI) detection in both tap and lake water samples. These attributes suggest that this hybrid sensor has strong potential in applications for the selective detection of Cr(VI).
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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 | 0.001 | 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".