Cracking the Code: Harnessing Serpentinization-Driven Micro-Crack Networks for Hydrogen Generation and Subsurface Resource Recovery
Bibliographic record
Abstract
ABSTRACT: This study focuses on simulating volume-increasing processes caused by natural hydrogen-generating serpentinization, a reaction in which olivine and pyroxene typically transform into serpentine minerals. The volume increase leads to crack propagation along grain interfaces and new crack generation within unreacted crystals (aka grains). The interplay between thermo-hydro-mechanical-chemical (THMC) conditions plays a critical role in the initiation, propagation, and coalescence of various fracture surfaces, which enhances permeability and can accelerate serpentinization and hydrogen production rates. In the scenarios considered, chemical reactions between formation brine and reactive minerals result in volumetric expansion, generating stress and promoting the propagation of micro-cracks. Our results show that these micro-cracks can progressively evolve into intricate networks, depending on local stress conditions, material properties, fluid transport, and reaction kinetics. Importantly, if THMC processes can be engineered to optimize these dynamics, they could lead to a commercially viable approach for in-situ hydrogen generation, leveraging naturally reactive systems to create sustainable energy solutions
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".