Synthesis of Layered Double Hydroxides and TiO<sub>2</sub> Supported Metal Nanoparticles for Electrocatalysis
Bibliographic record
Abstract
Abstract In the present work, solution‐phase synthesis was employed to prepare two sets of catalysts with different transition metals as active sites. One set contained Au or Pd supported on TiO2 (Au−TiO2, Pd−TiO2), whereas the other set contained layered double hydroxides (NiFe‐LDH and CuFe‐LDH). The electrocatalytic performance of these composite materials was investigated by cyclic voltammetry (CV) using a model compound 4‐nitrophenol (4‐NP). Composite materials were characterized by various analytical techniques to gain insight into the catalysts active sites. The morphology and structure of the prepared samples were investigated by X‐ray diffraction, attenuated total reflectance Fourier transform infrared, X‐ray photoelectron spectroscopy, transmission scanning electron microscope, and field emission scanning electron microscope. Metal nanoparticles loading on TiO2 was measured by inductively coupled plasma – optical emission spectrometry. CV measurements were performed in acetonitrile solution containing 0.1 m tetrabutylammonium hexafluorophosphate (TBAPF6) and 1 mm 4‐NP. Among all dioxides (Au−TiO2, Pd−TiO2) and hydroxides (NiFe‐LDH and CuFe‐LDH) studied, Pd−TiO2 shows the lowest onset potential (−0.32 V vs. Ag/AgCl) for the electrocatalytic reduction of 4‐NP. This is the first comparative study of such materials for 4‐NP electrocatalysis in aprotic solvent, thus demonstrating the suitability of dioxide and hydroxide based materials as electrocatalysts.
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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".