A systematic neutron reflectometry study on hydrogen absorption in thin Mg<sub>1-</sub><i><sub>x</sub></i>Al<i><sub>x</sub></i> alloy films Special issue on Neutron Scattering in Canada.
Bibliographic record
Abstract
In this article, we show how neutron reflectometry (NR) can provide deep insight into the absorption and desorption properties of commercially promising hydrogen storage materials. NR benefits from the large negative scattering length of hydrogen atoms, which changes the reflectivity curve substantially, so that NR can determine not only the total amount of stored hydrogen but also the hydrogen distribution along the film normal, with nanometer resolution. To use NR, the samples must have smooth surfaces, and the film thickness should range between 10 and 200 nm. We performed a systematic study on thin Mg1–xAlx alloy films (x = 0.2, 0.3, 0.4, 0.67) capped with a Pd catalyst layer. Our NR experiments showed that Mg0.7Al0.3 is the optimum alloy composition with the highest amount of stored hydrogen and the lowest desorption temperature. All the thin films expand by about 20% because of hydrogen absorption, and the hydrogen is stored only in the MgAl layer with no hydrogen content in the Pd layer.
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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) | 0.000 | 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".