Assessment of hydrogen adsorption capacities on low-maturity shales for geological storage applications: a lattice density functional theory approach
Bibliographic record
Abstract
Large-scale underground storage of hydrogen is widely recognized as an important solution for energy transition to mitigate global warming. To assess H 2 storage in low-maturity shale, lacustrine samples from the North Jiangsu Basin in eastern China were characterized. The pore structure was first visualized using scanning electron microscopy, while the quantitative pore size distribution (PSD) was determined through low-temperature argon adsorption analysis. High-pressure hydrogen adsorption experiments (50–100 °C, 0–190 bar) were then conducted, and the results were interpreted using a lattice density functional theory (LDFT) model. By incorporating PSD data into the LDFT framework, the model successfully captures the variation in H 2 adsorption across pore sizes and quantifies the contributions of micropores and mesopores to the overall adsorption capacity. The maximum excess adsorption capacity (n ex ) of hydrogen ranged from 36.03 to 54.94 μmol/g at 50 °C, increasing with total organic carbon content and decreasing with temperature. The limiting heat of adsorption ranges from 11.81 to 12.29 kJ/mol. The PSD-LDFT model accurately reproduces the adsorption isotherms across a range of pore sizes and temperatures, highlighting the dominant contribution of micro- and mesopores to the overall adsorption capacity. Modeling results indicate that macropores contribute negligibly to H 2 adsorption, while micropores significantly affect interaction energies and adsorbed-phase densities. In micropores, adsorption occurs mainly through complete pore filling, underscoring the importance of pore size variations in adsorption evaluation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".