Effects of alloy elements on the hydrogen adsorption behavior of pipeline steel: A review
Bibliographic record
Abstract
To achieve carbon neutrality and energy transition, hydrogen energy has gained widespread attention as a clean and renewable energy source. Pipeline transportation is the most economical way to achieve large-scale and stable hydrogen energy delivery. However, pipeline steel is potentially susceptible to hydrogen embrittlement (HE) in high-pressure gaseous environments. The initiation of HE is subjected to hydrogen entry into steel. However, the mechanism of hydrogen molecule adsorption and dissociation has remained unclear. This paper systematically reviews the mechanisms of hydrogen adsorption and dissociation on metal surfaces, highlighting the effect of doped alloying elements on the process. Surface metallurgical defects such as grain boundaries or phase boundaries, due to their structural irregularities, serve as preferential sites for element segregation and precipitation, thereby influencing crack initiation and propagation. Additionally, the study provides a comprehensive review of various alloying elements and their physical states in steel (e.g., solid solutions, compound precipitates, segregation), as well as the effects on hydrogen adsorption behavior. The findings confirm that doping alloy elements can modify the electronic structure and energy states of steel, either enhancing or suppressing HE through inhibiting lattice adsorption and preferential hydrogen trapping. It is critical to optimize alloy-doping strategies by balancing the impact on the intrinsic property of materials and their hydrogen resistance. These insights offer a reliable theoretical foundation for improving pipeline steels in hydrogen transportation. In contrast to previous reviews that focus primarily on individual dopants or experimental characterizations, this study integrates multi-scale theoretical insights with a classification framework based on the dopant forms (solid solutions, compounds, and segregation). This approach offers a more comprehensive understanding of the influence of doping configurations on the hydrogen adsorption mechanisms.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".