Structural Analysis of Platinum Nanoparticles on Carbon Nanotube Surface as Electrocatalyst System
Bibliographic record
Abstract
Synthesis of Pt/CNT composite by using sol gel method has been performed which the composition of CNT on the composite are vary, (x = 20, 40, 60 and 80 wt%). Performance of composite was characterized by Transmission Electron Microscope (TEM) and X-Ray Diffraction (XRD), respectively. In the refinement results of X-ray diffraction pattern, the composite consists of two phases, namely, carbon and platinum phases. Carbon phase has a structure hexagonal (P 63 m c) with lattice parameters a = b = 2.451(2) Å and c = 6.89(1) Å, α = β = 90° and γ = 120°, the unit cell volume of V = 35.8(1) A3, and the atomic density of ρ = 2.224 g.cm-3. While platinum phase has the structure of cubic (F m -3 m) with lattice parameters a = b = c = 3.921(2) Å, α = β = γ = 90°, the unit cell volume of V = 60.3(1) A3, and the atomic density of ρ = 21.487 g.cm-3.According to the image of TEM, the average particle size for Pt nano particle is estimated to range from 4.1-4.3 nm. While the cavity diameter average of CNT is estimated to range from 5.9-7.5 nm. Based on the calculation, the crystallite size of the Pt particle was around 4.31 nm. The optimum value of dispersed Pt into CNT occurred at 60 wt% CNT with the best composition of Pt in the unit cell of cystal structure. We concluded that this study successfully dispersed Pt nanoparticles onto CNT formed Pt/CNT composite. This was a great opportunity that the composite can be applied as electrocatalyst system on fuel cell application.
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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".