Hierarchical mesoporous plasmonic Pd-Fe <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e806" altimg="si99.svg"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> </mml:msub> </mml:math> O <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e814" altimg="si100.svg"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:math> /NiFe-LDH composites: Characterization, and kinetic study of a photodegradation catalyst for aqueous metoclopramide
Bibliographic record
Abstract
With the primary objective to develop efficient, environmentally benign, visible-light-driven heterogeneous catalysts for the degradation of metoclopramide (MCP), a waste-water pollutant, a newly prepared heterogeneous composite Pd-Fe3O4/NiFe-LDH catalyst is reported. This material has been characterized with a wide range of analysis methods (i.e. XRD, SEM-EDS, BET, FTIR, TG-DTG, DTA, DRUV and TEM analysis). When applied to aqueous solutions of MCP, Pd-Fe3O4/NiFe-LDH displayed a photocatalytic degradation of MCP with an efficiency of 95.2% in 80 min. The photocatalytic degradation rate for this composite material was higher than that of pure NiFe-LDH or Fe3O4/NiFe-LDH. The best photocatalytic activity was obtained at pH 6.5, with 0.4 g/L of the catalyst. Application of the Arrhenius equation yielded an activation energy for this process of 13.4 kJ/mol. A negative activation ΔS‡ (-0.25 kJ/mol) with the positive ΔH‡ and ΔG‡ values were obtained for this MCP photodegradation.
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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) | 0.001 | 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".