Facile Tantalum Doped Tungsten Oxide Intercalated Carbon Sensor for Surfactant Mediated Rapid Detection of Pesticides: Diuron and Dichlone
Bibliographic record
Abstract
The utilization of doped WO 3 nanostructures as an active component in electrochemical sensors is a promising strategy for delivering highly sensitive and stable detection schemes. In this work, a nanostructured WO 3 possessing a mixture of nanoparticle and one-dimensional (1-D) architecture, doped with Ta, was synthesized via hydrothermal reaction for electrochemical sensor application. Substituting Ta in W lattice positions yields polymorphic crystals with n-type characteristics. An optimized Ta content of 2.02 at. % in WO 3 was utilized for detailed electrochemical analysis of diuron (DIU) and dichlone (DCN). An electrochemical sensor was fabricated by packing the mechanically intercalated Ta-WO 3 /carbon matrix into a Teflon tube. The developed Ta-WO 3 /CPE could be employed for the selective and sensitive electrocatalytic determination of DIU and DCN in the presence of the cationic surfactant, viz., CTAB. The CTAB-mediated Ta-WO 3 /CPE exhibited good linearity with remarkable detection limits of 5.2 and 0.4 nM for DIU and DCN, respectively. The sensor displayed good sensitivity of 4.8 (for DIU) and 38.7 μA·μM –1 ·cm –2 (for DCN) with selectivity, demonstrating real-time efficiency for DIU and DCN determination in spiked soil and water samples with satisfactory results.
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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 | 0.001 |
| 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".