Effect of amine ligands on stabilization of pt nanoparticles as electrode materials for electro-oxidation of methanol
Bibliographic record
Abstract
Pt-stabilized catalysts with platinum crystallites sizes between 1-20 nm were prepared using an organometallic approach and three different amino ligands; tert-butylamine, 1,3-diaminopropane and anthranilic acid. The electrochemical oxidation of methanol was investigated on Pt stabilized nanoparticles in acid solutions and the results were compared with commercial PtBlack to analyze the feasibility of applying a tailored stabilizer to improve the dispersion and electrocatalytic activity. The particle size and the degree of dispersion of the resultant nanoparticles were observed by transmission electron microscopy (TEM) and selected area electron diffraction (SAED) patterns. Dispersion differences, lattice parameters and interplanar distances were caused by the coordination of the functional groups contained in the ligands at the Pt surface. The current density peaks on methanol oxidation reaction (MOR), appearing at different potentials and are increased in the following order PtBlack ≈ PtTBA> PtDAP > PtAA. The different tendency to form aggregates and scattered particles is the result of the divergence in their sterical shapes rather than different acid-base interactions. It has been also found that stabilized Pt nanoparticles using TBA or even DAP exhibit an interesting electrocatalytic activity, and can facilitate the MOR.
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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".