Tuning the Carbon Crystallinity for Highly Stable Li-O<sub>2</sub> Batteries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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–O 2 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–O 2 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–O 2 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 ( 13 C)-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 CO 2 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
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it