Exploring new battery knowledge by advanced characterizing technologies
Bibliographic record
Abstract
Exploration of science and technologies represents human's thirst for new knowledge and new life. Presently, we are in a stage of transferring the use of fossil fuels to renewable energy, which urgently calls for new energy materials and techniques beyond the boundary of human knowledge. On the way of scrutinizing these materials and surmounting the bottleneck of their performances, characterizing technologies are of critical importance in enabling the revealing of materials regarding their structural and chemical information, eventually establishing the correlations between microstructures and properties at the multiscale levels. Regrettably, traditional characterizations are hard to simultaneously probe electrochemistry with these chemical and physical structural evolutions, especially under operando conditions, or offer high-resolution images of materials sensitive to electron-beam irradiation. To this end, various advanced characterizing and diagnosing technologies recently developed, such as transmission X-ray microscopy and cryo-transmission electron microscopy, have demonstrated their benefits in understanding the energy storage behaviors of high-performance energy materials (such as layered transition oxide cathode and Li metal anode). Benefited from new knowledge, the progress of high-capacity electroactive materials is significantly accelerated. Here, we timely review the breakthroughs in emerging techniques and discuss how they guide the design of future battery materials to achieve the ultimate carbon neutrality.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".