Tuning the Carbon Crystallinity for Highly Stable Li-O<sub>2</sub> Batteries
Bibliographic record
Abstract
The increasing demands for emerging high-energy-density applications, such as electric vehicles, have prompted considerable efforts to design a new type of innovative, sustainable battery. Li–O2 batteries can deliver much higher energy densities than current Li-ion batteries and have thus attracted much attention; however, their poor cyclic stability remains a major obstacle to their use in high-energy-density applications. The carbon-based cathode materials (CCMs) used for Li–O2 batteries are considered one of the origins of this cycle-life degradation, which has led to the development of several alternative types of cathode materials, such as Au or TiC.1,2,3 However, there is currently no practical substitute for CCMs, which exhibit desirable properties such as high specific surface area, high electrical conductivity, light weight, and chemical stability and involve the use of well-known technologies with low processing and raw material costs. This study provides a new perspective on Li–O2 batteries, for which the cyclic stability can be dramatically increased using well-ordered graphitic CCMs. Through a systematic investigation on the controlled carbon, we demonstrate that the graphitic crystallinity of carbon is an important factor in determining the stability of not only the cathode but also the electrolyte. To discern the degradation factors affecting the cathode from those affecting the electrolyte, we used carbon isotope (13C)-based air electrodes with various degrees of graphitic crystallinity. Furthermore, in situ differential electrochemical mass spectroscopy analysis clearly demonstrates that as the crystallinity of the carbon increases, the CO2 evolution from the cell is reduced, which leads to a three-fold enhancement in the cycle stability of the cell. Reference 1. Thotiyl, M. M. O. et al., J. Am. Chem. Soc. 2012, 135, 494 2. Peng, Z et al., Science 2012, 337, 563 3. Thotiyl, M. M. O. et al., Nat. Mater. 2013, 12, 1050
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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".