Determination of manganese‐ and manganese‐containing fungicides with lucigenin–Tween‐20‐enhanced chemiluminescence detection
Bibliographic record
Abstract
A flow-injection (FI) method is reported for the determination of Mn(II), maneb and mancozeb fungicides based on the catalytic effect of Mn(II) on the oxidation of lucigenin and dissolved oxygen in a basic solution. The Tween-20 surfactant has been reported for first time to enhance lucigenin chemiluminescence (CL) intensity in the presence of Mn(II) (53%) and maneb and mancozeb (89%). The calibration graphs were linear in the concentration range of 0.001-1.5 mg L(-1) (R(2) = 0.9982 (n = 11) with a limit of detection (S/N = 3) of 0.1 µg L(-1) for Mn(II) and 0.01-3.0 mg L(-1) [R(2) = 0.9989 and R(2) = 0.9992 (n = 6)] with a limit of detection (S/N =3) of 1.0 µg L(-1) for maneb and mancozeb respectively. Injection throughputs of 90 and 120 h(-1) for Mn(II) and maneb and mancozeb respectively, and relative standard deviations of 1.0-3.4% were obtained in the concentration range studied. The experimental variables, e.g., reagents concentrations, flow rates, sample volume, and photomultiplier tube voltage, were optimized and potential interferences were investigated. The analysis of Mn(II) in river water reference materials (SLRS-4 and SLRS-5) showed good agreement with the certified values incorporating an on-line 8-hydroxyquinoline chelating column in the manifold for removing interfering metal ions. Recoveries for maneb and mancozeb were in the range of 92 ± 5 to 104 ± 3% and 91 ± 2 to 100 ± 4% (n = 3) respectively. The effect of 30 other pesticides (fungicides, herbicides and insecticides) was also examined in the lucigenin-Tween-20 CL system.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".