Zero‐Order Catalysis in TAML‐Catalyzed Oxidation of Imidacloprid, a Neonicotinoid Pesticide
Bibliographic record
Abstract
Abstract Bis‐sulfonamide bis‐amide TAML activator [Fe{4‐NO 2 C 6 H 3 ‐1,2‐( N COCMe 2 N SO 2 ) 2 CHMe}] − ( 2 ) catalyzes oxidative degradation of the oxidation‐resistant neonicotinoid insecticide, imidacloprid (IMI), by H 2 O 2 at pH 7 and 25 °C, whereas the tetrakis‐amide TAML [Fe{4‐NO 2 C 6 H 3 ‐1,2‐( N COCMe 2 N CO) 2 CF 2 }] − ( 1 ), previously regarded as the most catalytically active TAML, is inactive under the same conditions. At ultra‐low concentrations of both imidacloprid and 2 , 62 % of the insecticide was oxidized in 2 h, at which time the catalyst is inactivated; oxidation resumes on addition of a succeeding aliquot of 2 . Acetate and oxamate were detected by ion chromatography, suggesting deep oxidation of imidacloprid. Explored at concentrations [ 2 ]≥[IMI], the reaction kinetics revealed unusually low kinetic order in 2 (0.164±0.006), which is observed alongside the first order in imidacloprid and an ascending hyperbolic dependence in [H 2 O 2 ]. Actual independence of the reaction rate on the catalyst concentration is accounted for in terms of a reversible noncovalent binding between a substrate and a catalyst, which usually results in substrate inhibition when [catalyst]≪[substrate] but explains the zero order in the catalyst when [ 2 ]>[IMI]. A plausible mechanism of the TAML‐catalyzed oxidations of imidacloprid is briefly discussed. Similar zero‐order catalysis is presented for the oxidation of 3‐methyl‐4‐nitrophenol by H 2 O 2 , catalyzed by the TAML analogue of 1 without a NO 2 ‐group in the aromatic ring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".