Development of Graphene-Based Nanocomposites for Hydrogen Storage
Bibliographic record
Abstract
Hydrogen economy is an envisioned approach with potential to addressing the increasing global energy consumption and emission issues. However, as one of the fundamental elements of the hydrogen economy, storage of hydrogen in a lightweight, compact, and safe manner remains the most serious bottleneck. Gaseous and liquid hydrogen storage methods continue to dominate for commercial usage; however, they have been found to be unsafe and expensive. Consequently, solid-state hydrogen storage has been attracting considerable interests, where hydrogen may be stored via physisorption (carbon-based materials and metal organic frameworks) or chemisorption (metal and complex hydrides) [1-3]. In this presentation, recent advances in graphene-based materials with a high surface area and enhanced storage capacity for solid-state hydrogen storage are highlighted. Specifically, this presentation focuses on the development of uniformly dispersed metal nanoparticles (NPs), for example Pd NPs, on a heteroatom-doped reduced graphene oxide (rGO). Our study has shown that the doping of the heteroatoms (such as boron, nitrogen etc.) effectively suppresses possible metal cluster formation, resulting in uniform distribution of the Pd NPs on the modified rGO. The effect of the doping of different heteroatoms and the decoration with Pd NPs on the hydrogen uptake and release is discussed. [1] S. K. Konda, and A. Chen, Mater. Today, 19 , 100 (2016). [2] E. Boateng, and A. Chen, Mater. Today Adv ., 6 , 100022 (2020). [3] E. Boateng, J. S. Dondapati, A. R. Thiruppathi, and A. Chen, Int. J. Hydrogen Energy , 45 , 28951 (2020).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".