TiO<sub>2</sub> Nanoparticles Co-Sensitized with Graphene Quantum Dots and Pyrocatechol Violet for Photoelectrochemical Detection of Cr(VI)
Bibliographic record
Abstract
Photoactive electrodes with high photon-to-electron conversion efficiency are key to achieving sensitive photoelectrochemical sensors. Among all the photoactive materials, titanium dioxide (TiO2) nanoparticles have attracted much attention due to their unique electronic and optical properties. However, the large bandgap of TiO2 results in limited photocurrent signal generation under visible irradiation, which is important for its use in many applications including sensing. Herein, we modified TiO2 nanoparticles with both pyrocatechol violet and graphene quantum dots to obtain high photocurrents at visible light excitation while also improving TiO2 nanoparticle dispersion and film-forming properties. This material system enhances photocurrent by 5 times compared to TiO2 nanoparticles that are modified with only pyrocatechol violet and 60 times compared to TiO2 nanoparticles modified with graphene quantum dots. Additionally, the optimized photoelectrodes were used to detect hexavalent chromium (Cr(VI)), which has been reported as a toxic carcinogen. Under visible light irradiation, the fabricated sensor offered a low limit-of-detection of 0.04 μM for Cr(VI), with selectivity against Na, Mg, Cu, and Cr (III) ions, paving the route toward photoelectrochemical Cr(VI) sensing.
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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) | 0.001 | 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".