Nano‐hydroxyapatite (<scp>HAp</scp>) and hydroxyapatite/platinum (<scp>HAp</scp>/Pt) core shell nanorods: Development, structural study, and their catalytic activity
Bibliographic record
Abstract
Abstract The development of a new kind of material that is a nanostructured catalytic material with an environmentally benign nature that can be used for alternative energy has acquired significance in recent years. In this context, the use of heterogeneous catalysts for the transesterification of vegetable oils has gained prominence due to their eco‐friendly and reusable nature. Hence in the present study, pure hydroxyapatite (HAp) and hydroxyapatite/platinum (HAp/Pt) nanostructured particles have been prepared successfully through a facile chemical method without templates and surfactants and their catalytic activity investigated for transesterification of natural vegetable oil to bioenergy (biodiesel). The textural and structural features of pure HAp and HAp/Pt were investigated using various characterization techniques such as x‐ray diffraction, Fourier transform infrared (FTIR) and Raman spectroscopy, field emission scanning electron microscopy (FESEM), and transmission electron microscopy (TEM). The elements present in the prepared nanostructures were confirmed through energy dispersive spectroscopy (EDS) and x‐ray photoelectron spectroscopy (XPS) techniques. The XPS analysis also confirms the metallic nature of the platinum in HAp/Pt. The specific surface area and porous nature of the prepared nanostructured catalysts were studied using the N2physisorption Brunauer‐Emmett‐Teller‐Barrett‐Joyner‐Halenda (BET‐BJH) method. The catalytic activity of the pure HAp nanoparticles and HAp/Pt core shell nanorods with theSimarouba glaucaplant seed oil was investigated. The obtained results indicate that the pristine HAp nanoparticles and HAp/Pt core shell nanorods (NRs) show 91.4% and 87.1% fatty acid methyl ester (FAME) conversion, respectively, potentially offering environmental benign biocatalysts for biofuel production from natural feed stock.
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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".