Bibliographic record
Abstract
Abstract In recent years, many countries have made plans for the development of electric vehicles. In 2021, the EU announced a plan to completely stop the sale of fuel cars by 2035 and replace all fuel cars with pure electric vehicles, reducing the carbon emissions to 100%. This is the most radical emissions reduction plan in history, and it means that the era of pure electric cars has officially arrived. The limited energy density of lithium-ion batteries currently used in cars has hampered the development of electric vehicle mileage. To meet the demand for electric vehicles, the development and research of high energy density batteries are urgent. Based on a review of the current literature, this paper summarizes the development history, working principles, current challenges and solutions of the solid-state battery, lithium-air batteries and nuclear batteries. The current dilemma for solid-state batteries is the lack of a suitable solid electrolyte, which is needed to possess high ionic conductivity of above 10 (mS/cm) at room temperature and negligible electronic conductivity with a high ionic transference number wide electrochemical stability windows. Lithium-air batteries have low power density, battery energy attenuation, and high safety performance. The research and application of nuclear batteries are more difficult, including low energy conversion rate and health problem. The result provides some guidance to researchers initially involved in the high energy density battery industry.
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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".