Effect of Quench Tempering on Hydrogen Embrittlement and Corrosion Behavior of X100 Pipeline Steel
Bibliographic record
Abstract
In this study, the hydrogen embrittlement and corrosion behavior of X100 pipeline steel (Ref) was investigated after various heat treatments, including one-step austenitizing at 880 °C (HT3), 830 °C (HT2), and 780 °C (HT1) for 90 min, oil quenching to room temperature, tempering at 600 °C for 30 min, and air cooling to room temperature. Potentiodynamic polarisation was performed to assess the electrochemical corrosion behavior, while the Charpy impact test and Vickers microhardness measurement were performed to assess the hydrogen embrittlement susceptibility before and after hydrogen charging. SEM, EBSD, and EDS were used to further characterize the fractured surface and crystallographic texture of specimens, while XRD was used to evaluate the macro-texture and corrosion products. The results of the Charpy impact and hardness tests showed that the high hardness and low impact energy values in the Reference and HT3 specimens were linked to a higher susceptibility to hydrogen embrittlement, indicating that the hardness values and Charpy impact energy, respectively, increased and decreased with a decrease in the hydrogen embrittlement resistance. The micro-texture results from the EBSD analysis showed that the HT3 and Ref. specimens had higher Kernel average misorientation (KAM) values and higher deformed grains fractions than those of the HT2 and HT1 specimens, resulting in lower corrosion resistance. The HT2 specimen had an optimal combination of beneficial ({110}, {111}, {332}) and harmful texture components ({100}), showing that corrosion resistance can be improved.
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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) | 0.000 | 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".