Comparative hydrogen storage performance assessment of organic waste materials for sustainable energy applications
Bibliographic record
Abstract
This paper investigates the electrochemical hydrogen storage performance of activated carbons derived from tea waste (TW), coffee ground (CG), maple leaves (ML), and pinecone (PC). The samples undergo magnetic activation with FeSO 4 .7H 2 O followed by carbonization. Their performance is evaluated using linear sweep voltammetry (LSV), cyclic voltammetry (CV), and galvanostatic charge-discharge (GCD). LSV shows that all samples promote hydrogen evolution with increasing current density in the negative potential region. CV demonstrates that pinecone and tea-derived carbons exhibit higher reversibility and capacitive behavior. GCD results indicate that commercial activated carbon delivers 5.79 μAh/g (0.0214 wt.%), while pinecone and tea carbons reach 3.89 μAh/g (0.0144 wt.%) and 3.25 μAh/g (0.012 wt.%). At 0 °C and 4 kPa, pinecone carbon records 0.389 wt.% compared to 0.0214 wt.% for CAC. SEM and EDS confirm that porosity and Fe dispersion support this performance. The study concludes that waste-derived carbon represents sustainable, low-cost hydrogen storage materials. • Magnetically activated carbons were derived from four organic waste materials. • Electrochemical tests showed promising hydrogen storage capacities at room temp. • Pinecone and tea waste carbons showed superior stability and reversibility in CV. • SEM-EDS results revealed porous morphology correlating with electrochemical output. • Study promotes eco-friendly, low-cost biochar use for hydrogen energy storage.
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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 | 0.001 | 0.001 |
| 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".