Research advances of magnesium and magnesium alloys globally in 2024
Bibliographic record
Abstract
Research on magnesium (Mg) alloys still remains a prominent and expanding field in recent years. The Web of Science Core Collection database documented 4898 published articles on the topic, highlighting a sustained and growing interest. Statistical analysis of the literature reveals a consistent focus on microstructures, mechanical and corrosion properties. Significant progress has also been made in the manufacture of large-scale Mg alloy components. Meanwhile, steady advancements have been achieved in functional magnesium materials, magnesium-based hydrogen storage, and magnesium-ion batteries, with magnesium-based Energy Storage Mater. moving closer to commercial applications. Notably, the year 2024 marks a breakthrough in artificial intelligence, and the integration of big data and artificial intelligence is expected to significantly accelerate the research and development of magnesium alloy materials. Furthermore, the decline in primary magnesium prices in 2024 has triggered a new wave of research and large-scale commercial applications. Concurrently, there is growing interest in their use in emerging industries such as unmanned aerial vehicles and robotics. With continuous improvements and diversification in performance, the applications of magnesium alloys have expanded significantly in 2024, encompassing satellite components, integrated automotive structures, magnesium alloy formwork, and biomedical materials. This paper provides a comprehensive review of the current state of development and key research challenges in the field of Mg alloys as of 2024, and also outlines potential future directions for research and application.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".