Rapid Diffusion and Nanosegregation of Hydrogen in Magnesium Alloys from Exposure to Water
Bibliographic record
Abstract
Abstract Hydrogen gas is formed when Mg corrodes in water; however, the manner and extent to which the hydrogen may also enter the Mg metal is poorly understood. Such knowledge is critical as stress corrosion cracking (SCC)/embrittlement phenomena limit many otherwise promising structural and functional uses of Mg. Here, we report via D2O/D isotopic tracer and H2O exposures with characterization by secondary ion mass spectrometry, inelastic neutron scattering vibrational spectrometry, electron microscopy, and atom probe tomography techniques direct evidence that hydrogen rapidly penetrated tens of micrometers into Mg metal after only 4 h of exposure to water at room temperature. Further, technologically important microalloying additions of <1 wt % Zr and Nd used to improve the manufacturability and mechanical properties of Mg significantly increased the extent of hydrogen ingress, whereas Al additions in the 2–3 wt % range did not. Segregation of hydrogen species was observed at regions of high Mg/Zr/Nd nanoprecipitate density and at Mg(Zr) metastable solid solution microstructural features. We also report evidence that this ingressed hydrogen was unexpectedly present in the alloy as nanoconfined, molecular H2. These new insights provide a basis for strategies to design Mg alloys to resist SCC in aqueous environments as well as potentially impact functional uses such as hydrogen storage where increased hydrogen uptake is desired.
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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 | 0.000 |
| 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".