Bibliographic record
Abstract
The major advantage of Mg batteries relies on their promise of employing an Mg metal negative electrode, which offers much higher energy density compared to graphitic carbon. However, the strong coulombic interaction of Mg 2+ ions with anions leads to their sluggish diffusion in the solid state, which along with a high desolvation energy, hinders the development of positive electrode materials. To circumvent this limitation, Mg metal negative electrodes can be used in hybrid systems by coupling an Li + insertion cathode through a dual salt electrolyte. Two “high voltage” Prussian blue analogues (average 2.3 V vs Mg/Mg 2+ ; 3.0 V vs Li/Li + ) are investigated as cathode materials and the influence of structural water is shown. Their electrochemical profiles, presenting two voltage plateaus, are explained based on the two unique Fe bonding environments. Structural water has a beneficial impact on the cell voltage. Capacities of 125 mAh g −1 are obtained at a current density of 10 mA g −1 (≈C/10), while stable performance up to 300 cycles is demonstrated at 200 mA g −1 (≈2C). The hybrid cell design is a step toward building a safe and high density energy storage system.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".