Short-Time Magnetron Sputtering for the Development of Carbon–Palladium Nanocomposites
Bibliographic record
Abstract
In recent nanomaterials research, combining nanoporous carbons with metallic nanoparticles, like palladium (Pd), has emerged as a focus due to their potential in energy, environmental and biomedical fields. This study presents a novel approach for synthesizing Pd-decorated carbons using magnetron sputter deposition. This method allows for the functionalization of nanoporous carbon surfaces with Pd nano-sized islands, creating metal-carbon nanocomposites through brief deposition times of up to 15 s. The present research utilized direct current magnetron sputtering to deposit Pd islands on a flexible activated carbon cloth substrate. The surface chemistry, microstructure, morphology and pore structure were analyzed using a variety of material characterization techniques, including X-ray photoelectron spectroscopy, X-ray diffraction, Raman spectroscopy, gas sorption analysis and scanning electron microscopy. The results showed Pd islands of varying sizes distributed across the cloth's carbon fibers, achieving high-purity surface modifications without the use of chemicals. The synthesis method preserves the nanoporous structure of the carbon cloth substrate while adding functional Pd islands, which could be potentially useful in emerging fields like hydrogen storage, fuel cells and biosensors. This approach demonstrates the possibility of creating high-quality metal-carbon composites using a simple, clean and economical method, expanding the possibilities for future nanomaterial-based applications.
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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".