Synthesis and electrochemical FTIR study of Pd-based nanostructured catalysts
Bibliographic record
Abstract
Palladium-based nanomaterials with high surface areas have been receiving great attention due to their unique properties, which enable a number of impressive applications in catalysis, fuel cells, hydrogen storage and chemical sensors. Recent studies have shown that the electrocatalytic performance of Pd-based nanomaterials is highly dependent on the composition, morphology and surface conditions of the synthesized materials. In this study, a variety of Pd-based nanostructured materials including nanoporous Pd networks and PdPt nanodendrites with different compositions have been synthesized using the hydrothermal method. The as-fabricated Pd-based nanostructured materials were characterized by scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS. The electrochemical properties of these Pd-based nanomaterials were studied using cyclic voltammetry and in-situ electrochemical infrared spectroscopy. Our studies have shown that the fabricated Pd-based nanostructures possess a very large surface area and high catalytic activity towards the electrochemical oxidation of formic acid. In addition, this work has demonstrated that in-situ electrochemical infrared spectroscopy is a powerful technique for the study of the nanostured interface.
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 | 0.000 | 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".