Latest research advances on magnesium and magnesium alloys worldwide
Bibliographic record
Abstract
In the past two years, significant progresses have been achieved in high-performance cast and wrought magnesium and magnesium alloys, magnesium-based composites, advanced cast technologies, advanced processing technologies, and functional magnesium materials, such as Mg ion batteries, hydrogen storage Mg materials, bio-magnesium alloys, etc. Great contributions to the development of new magnesium alloys and their processing technologies have been made by Chongqing University, Shanghai Jiaotong University, Chinese Academy of Sciences, Helmholtz Zentrum Geesthacht, Queensland University, Brunel University, etc. This review paper is aimed to summarize the latest important advances in cast magnesium alloys, wrought magnesium alloys and functional magnesium materials worldwide in 2018–2019, including both the development of new materials and the innovation of their processing technologies. Based on the issues and challenges identified here, some future research directions are suggested, including further development of high-performance magnesium alloys having high strength and superior plasticity together with high corrosion resistance and low cost, and fundamental research on the phase diagram, diffusion, precipitation, etc., as well as the development of advanced welding and joining technology.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".