Bibliographic record
Abstract
The environmental pollution and energy crisis require the development of large-scale energy storage techniques. Batteries have attracted enormous attention over the past several years due to their relatively long cycle life and high energy density . Lithium-ion batteries as the most widely used energy storage devices still have safety hazards, high cost issues. Zinc-based rechargeable batteries, have gained remarkable attention as a promising energy storage technology, especially in mild/neutral aqueous electrolytes. Aqueous zinc-ion batteries are promising candidates for the next-generation energy storage system. Zinc metal anode exhibits high theoretical capacity (820 mA h g -1 ), low redox potential (-0.76 V vs the standard hydrogen electrode), low cost, and intrinsically high safety. However, practical application limited by severe dendrite formation, corrosion and hydrogen evolution on the anode surface. They can cause low Coulombic efficiency, short cycling life and even puncture of separators or short-circuit in the batteries. One of efficient strategies toward improving reversibility of Zn anode is to use surface engineering strategies such as building an artificial coating protection layer for zinc-electrolyte interface to provide defined and smooth Zn depostion behavior. In this work, we designed and synthesized an inorganic-carbon composite protection layer by complexing zinc oxide with carbon. This composite protection layer not only effectively shields rampant electrolyte diffusion to suppress side reactions but also suppresses Zn dendrites by uniform Zn plating/stripping. Symmetric cells with hybrid coating layer show extended cycle life and small voltage polarization. Full batteries with Mn-based and V-based cathodes can be assembled with increased capacity and stable cycling performance.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".