Synthesis, characterization, and performance assessment of a perovskite‐type nano photocatalyst for degradation of thiamethoxam
Bibliographic record
Abstract
Abstract It is essential to remove thiamethoxam (THMX) from surface water as it negatively impacts the ecology and neurological systems of insects. Hence, a study was conducted to degrade THMX with different compositions of LaFeO 3 /g‐C 3 N 4 (LFCN) composites. Several methods, including X‐ray powder diffraction (XRD), ultraviolet–visible spectroscopy (UV–Vis), X‐ray photoelectron spectroscopy (XPS), Brunauer–Emmett–Teller (BET), and field emission scanning electron microscopy (FESEM), were applied to characterize the synthesized photocatalyst. The influence of many parameters on degradation, for example, THMX concentration, dosage of catalyst, and pH were studied experimentally. The degradation was highest under both UV‐C and sunlight for the synthesized catalyst, 1% LaFeO 3 /g‐C 3 N 4 (LFCN1), in comparison to graphitic carbon nitride (g‐CN), and bare lanthanum ferrite (LF). The degradation was around 95% for LFCN1 under UV‐C light having an intensity of 15 W/m 2 whereas degradation was 71.8% with LFCN1 photocatalyst under sunlight at neutral pH in 120 min of reaction time. The increased activity of LFCN1 was attributable to an improved surface area and a lower band gap. The study of reaction kinetics indicated second‐order behaviour. Additionally, a probable mechanism for degradation was put forth.
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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 | 0.000 | 0.000 |
| 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".