Electrochemical Mg<sup>2+</sup> Displacement Driven Reversible Copper Extrusion/Intrusion Reactions for High‐Rate Rechargeable Magnesium Batteries
Bibliographic record
Abstract
Abstract Rechargeable magnesium batteries (RMBs) based on metal Mg anodes have shown great potential owing to the abundant natural resources, high volumetric capacity, and low safety hazard. Nevertheless, the development of RMBs is hampered by the sluggish kinetics of Mg 2+ diffusion and the limited cyclability of cathode materials. Herein, nonstoichiometric copper selenide (Cu 2– x Se) are synthesized via a solution‐based method and exploited as a durable cathode material based on ionic displacement mechanism for RMBs. The copper ions in the Se 2− based sub‐lattices are reversibly exchanged by Mg 2+ ions without causing lattice collapse. Owing to the same face‐centered cubic Se 2− sub‐lattices and similar unit cell size of Cu 2– x Se and MgSe, the energy barrier for lattice reconstruction during cycling processes is very low, significantly improving the rate performance, structural stability, and cycle life of the Cu 2– x Se cathode. Moreover, metal Cu is in situ generated during discharging, thus greatly facilitating electron transport. Comprehensive characterizations confirm that the Cu 2– x Se cathode undergoes reversible copper ion extrusion/reinjection during the discharge−charge steps. This work suggests the great potential for exploring high‐performance electrode materials based on ionic displacement mechanism for advanced multivalent‐ion secondary batteries.
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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) | 0.001 | 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".