The Rise of Multivalent Metal–Sulfur Batteries: Advances, Challenges, and Opportunities
Bibliographic record
Abstract
Abstract As the “star of hope” for the next‐generation high‐energy‐density batteries, lithium–sulfur batteries (Li–S batteries) face severe challenges such as reserves, costs, and safety, which seriously restrict their practical application. Alternatively, research on multivalent metals (e.g., Mg, Ca, Al, Zn, etc.) as anodes, characterized by less reactivity and higher natural abundance, is gaining increasing attention and urgent demand. However, metal–sulfur (M–S) battery technology based on multivalent metal anodes is still in its infancy and not yet mature for practical application. This review provides insights into the challenges and prospects of multivalent M–S batteries, covering fundamental mechanisms, key issues, response strategies, and the latest advancements in flexible/micro energy storage devices. Furthermore, a general perspective and future research directions are also presented in this review. This review aims to explore opportunities for emerging multivalent M–S batteries and support the development of next‐generation high‐energy‐density energy storage systems.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".