Enhanced ambient-temperature hydrogen physisorption in porous graphene materials with defect-stabilized atomic potassium
Bibliographic record
Abstract
Theoretically, alkali metals are expected to improve the hydrogen storage performance of graphene and its derivatives as physisorption adsorbents. However, practically achieving this improvement has proven challenging, mainly due to the elusive nature of the adsorption mechanism and the difficulty in exerting alkali metals’ hydrogen adsorption capacity in the synthesized materials. Combining experimental and theoretical efforts, this work unambiguously demonstrates the hydrogen adsorption enhancement effects of atomically decorated potassium on porous graphene materials at room temperature, evidenced by a 3.9-fold increase in maximum hydrogen adsorption capacity. In-depth microstructure and chemical characterization together with sophisticated ab initio density functional theory simulation unveil the mechanisms behind the novel atomic potassium incorporation and the enhancement of hydrogen adsorption, highlighting the role of atomic potassium in creating a nano-environment strongly attracting hydrogen at room temperature. This work provides a new strategy for enhancing hydrogen physisorption by engineering alkali metal-centered nano-attractive environment in adsorbent materials. • 3.9-fold enhancement of room-temperature H 2 storage by decorating K on graphene. • Unraveled mechanism and method of atomic K decoration onto graphene through defects. • Explained enhancement via formation of a highly charged nano-environment around K. • Revealed a new strategy for boosting H 2 physisorption using integrated alkali metal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".