Microstructure Analysis and Hydrolysis Mechanism of AlLi Alloys Activated by Metal Additives for Hydrogen Generation
Bibliographic record
Abstract
Microstructure analysis and hydrolytic mechanism of activated Al-Li alloys including low melting point metal additives X (X:indicated as Bi, Sn, In and Ga) are explained for their good hydrogen generation performance in this paper. It is demonstrated that the presence of these metals has a double effect. The metals are helpful to reduce the grain size of Al-Li alloys due to the formation of new intermetallic compounds such as BiLi3, Sn5Li13, AlLiIn2, etc., preventing the connection of Al-Al and Li-Li atoms. The metals strongly improved hydrogen generation performance because the metals deposited on the surface of Al and Li metals act as cathodic centers for hydrogen generation. There were dual micro galvantic cells between Al (Li) and metal additives created in the hydrolysis process which stimulated the electrochemical corrosion of Al and Li. The LiX alloy acts as the initial hydrolysis centers due to its low standard potential and its hydrolysis byproduct LiOH further accelerates the micro galvantic cell between Al and X. Therefore, hydrogen generation performance is linked to standard potential of metal additives, increased Li and X amounts, uniform distribution of Li and X in the Al matrix in the longer milling time. Our results show that the potential good hydrogen generation performance can be obtained via the design and preparation technology of Al alloys.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".