Nitrogen Doped Reduced Graphene Oxide Based Pt–TiO<sub>2</sub> Nanocomposites for Enhanced Hydrogen Evolution
Bibliographic record
Abstract
Electrochemical hydrogen production from water is an attractive clean energy generation process that has enormous potential for sustainable development. However, noble metal catalysts are most commonly used for such electrochemical hydrogen evolution making the process cost ineffective. Thereby design of hybrid catalysts with minimal use of noble metals using a suitable support material is a prime requirement for the electrolysis of water. Herein, we demonstrate the superior hydrogen evolution reaction (HER) activity of the platinum nanoparticles (Pt NPs) supported on faceted titanium dioxide (TiO 2 ) nanocrystals (Pt–TiO 2 ) and nitrogen doped reduced graphene oxide (N-rGO) based TiO 2 nanocomposite (Pt–TiO 2 –N-rGO). The ternary Pt–TiO 2 –N-rGO nanocomposite exhibits a superior HER activity with a small Tafel slope (∼32 mV·dec –1 ), exchange current density (∼0.22 mA·cm –2 ), and excellent mass activity (∼3116 mA·mg pt –1 ) at 300 mV overpotential. These values are better/higher than that of several support materials investigated so far. The excellent HER activity of the ternary Pt–TiO 2 –N-rGO nanocomposite is ascribed to the presence of Ti(III) states and enhanced charge transportation properties of N-rGO. The present study is a step toward reliable electrochemical hydrogen production using faceted TiO 2 nanocrystals as support material.
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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".