Synthesis and Electrochemical Study of Graphene-Based Nanocomposites for Hydrogen Sorption and Storage
Bibliographic record
Abstract
With impending serious concerns associated with climate change and the depletion of fossil fuels, an envisaged hydrogen economy remains a viable alternative for addressing future energy issues. However, the significant technical challenges from vehicular hydrogen storage systems such as weight, efficiency, safety and cost constraints must be properly resolved before a commercial application is possible. Compared to compressed high-pressure and liquid hydrogen storage systems, storing hydrogen in solid systems via chemisorption and physisorption is emerging as a promising approach for hydrogen storage [1-3]. In this presentation, recent advances in the solid-state hydrogen storage with a high volumetric density are highlighted. In addition, graphene-based nanomaterials have been considered as a promising candidate for hydrogen storage due to its lightweight and high surface area [4]. We have synthesized graphene oxide (GO), reduced graphene oxide (rGO) and boron-doped reduced graphene oxide (B-rGO) and investigated their performance for hydrogen storage. To enhance their capacity for hydrogen storage, the fabricated graphene oxide based nanomaterials were further modified with palladium (Pd) nanoparticles. The morphological features, structure and chemical compositions of the synthesized nanomaterials (GO, rGO and B-rGO) and nanocomposites (Pd/GO, Pd/rGO and Pd/B-rGO) were characterized using field-emission scanning electron microscopy, transmission electron microscopy, X-ray diffraction spectroscopy, X-ray photoelectron spectroscopy and Raman spectroscopy, showing that Pd nanoparticles were uniformly dispersed on the B-rGO surface. Cyclic voltammetry and galvanostatic charging-discharging technique were employed to probe the hydrogen storage capacity of the graphene based nanomaterials and the nanocomposites. The effect of the boron substitution and the Pd nanoparticle decoration on the hydrogen storage are discussed. References: [1] A. Chen, C. Ostrom. Palladium-based nanomaterials: synthesis and electrochemical applications. Chem. Rev. 115 (2015) 11999 - 12044. [2] S. Konda, A. Chen, Palladium based nanomaterials for enhanced hydrogen spillover and storage. Mater. Today 19 (2016) 100 - 108. [3] E. Boateng, A. Chen, Recent advances in nanomaterials-based solid-state hydrogen storage. Mater. Today Adv. (2019) in press. [4] S. K. Konda, A. Chen, One-step synthesis of Pd and reduced graphene oxide nanocomposites for enhanced hydrogen sorption and storage. Electrochem. Commun. 60 (2015) 148–152.
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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".