Insights into the structure-property relationships of activated carbon derived from phenolic resin for electrochemical storage of green hydrogen using proton battery
Bibliographic record
Abstract
Electrochemical hydrogen storage in porous activated carbons is a rapidly advancing technology, yet the composition and role of oxygen-containing surface functionalities in hydrogen storage remain underexplored. This study provides a detailed investigation of the surface and bulk properties of porous activated carbon derived from phenolic resin (aC PR) using a comprehensive multi-technique approach, including scanning electron microscopy (SEM), transmission electron microscopy (TEM), Brunauer-Emmett-Teller (BET), X-ray photoelectron spectroscopy (XPS), temperature programmed desorption (TPD), Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy . The aC PR exhibits an exceptional BET surface area of approximately 4400 m 2 /g, with a well-balanced distribution of mesopores , micropores , and ultra-micropores. Quantitative analyses reveal that aC PR is composed of 95.45 % carbon and 4.55 % oxygen, with oxygen functionalities distributed as carboxylic acid (~8 %), anhydride (~28 %), phenol (~17 %), carbonyl and quinones (~21 %), and lactones (~23 %). Post-TPD treatment, the oxygen content reduces to 2.25 %, with minimal impact on the material's hydrogen storage capacity, which remains at ~0.60 ± 0.05 wt% H. H-storage was measured using Proton Battery . In the proton battery, protons are generated by water splitting towards the oxygen side and are stored towards the C side in the negatively charged ac PR electrode. Ab initio molecular dynamics simulations demonstrate that both acidic and basic oxygen-containing groups have similar proton affinities , suggesting that the type of oxygen functional group plays a minimal role in hydrogen storage capacity. This work underscores the critical role of oxygen functionalities in hydrogen storage and offers new insights into the design and optimization of next-generation carbon materials for scalable hydrogen storage technologies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".