Tunable Electrochemical Hydrogen Uptake and Release of Nitrogen-Doped Reduced Graphene Oxide Nanosheets Decorated with Pd Nanoparticles
Bibliographic record
Abstract
Strategic surface modification and doping of reduced graphene oxide (rGO) support materials via heteroatom functionalization and metal decoration have been shown to improve hydrogen storage performance. By utilizing a microwave-assisted hydrothermal method, a series of nitrogen-doped rGO (NrGO) nanomaterials were fabricated with tuned nitrogen contents up to 7.0 at. %. The NrGO nanomaterials were then decorated with palladium nanoparticles (Pd NPs) via a facile chemical reduction method to form Pd/NrGO nanocomposites. The incorporation of an appropriate nitrogen content in rGO significantly improved the hydrogen uptake and release activity. The optimized Pd/NrGO nanocomposite with ∼5 at. % of nitrogen exhibited over sixfold enhancement of hydrogen storage capacity compared to Pd/rGO. Pair distribution function analysis by high-energy X-ray diffraction showed the formation of PdH x NPs only in NrGO materials, suggesting that the nitrogen doping enhances the hydrogen affinity of the Pd NPs. Systematic structural characterization and electrochemical studies reveal that the optimized Pd/NrGO exhibited a uniform distribution of Pd NPs on the NrGO nanosheets and low electron-transfer resistance. These results suggest that nitrogen doping leads to a strong metal-carbon support interaction, higher specific surface area, and larger accessibility for surface diffusion-controlled hydrogen uptake and release processes. Compared to Pd/rGO, the optimized Pd/NrGO nanocomposite attained a consistent hydrogen release charge after 3000 cycles, demonstrating an excellent stability under acidic conditions. This work opens a new avenue for designing advanced and cost-effective hydrogen storage materials by tuning the dopant content to maximize their performance toward a hydrogen economy.
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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".