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 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".