Electrochemical sensor development for detecting organophosphate pesticide using <scp>CuO</scp> nanograss electrode
Bibliographic record
Abstract
Abstract In this work, copper oxide (CuO) nanograss (NGs) were synthesized on copper foil as electrodes through anodization and annealing processes. After the successful synthesis of the CuO NG electrode, it was characterized using field emission scanning electron microscopy (FESEM) and X‐ray diffraction (XRD) analysis. Anodization duration was optimized for NG synthesis and was found to be 20 min with the help of FESEM analysis. The synthesized electrodes were used to analyze the organophosphate pesticides (OPPs), namely ‘malathion’ and ‘chlorpyrifos’, in the absence and presence of interfering molecules using differential pulse voltammetry (DPV). The proposed sensor functions based on the current inhibition ratio. The parameters like pH, accumulation time, and ionic strength of supporting electrolyte were optimized to be 7 pH, 9 min, and 0.1 M potassium chloride (KCl), respectively, for determining the current inhibition ratio (ΔI/I 0 ). The developed sensor was sensitive and selective, with limit of detection (LOD) as low as 1 ppb for both pesticides. The limit of quantification (LOQ) was 1 ppb for chlorpyrifos and 10 ppb for malathion. The sensor's selectivity was also studied by adding Pb(NO 3 ) 2 , Zn(NO 3 ) 2 , NiCl 2 , and carbendazim to a fixed malathion and chlorpyrifos concentration, and minimal interferences were observed in the detection of malathion and chlorpyrifos. The sensor's functionality was validated using an unknown concentration of malathion and chlorpyrifos in water and food samples with an average recovery of 95% when analyzed with the electrochemical method and high performance liquid chromatography (HPLC). The sensitivity of the electrochemical sensor for chlorpyrifos detection was found to be 0.6678 μA/ppb, and for malathion detection, it was found to be 1.139 μA/ppb.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".