Recent progress in electrochemical performance of carbon-based anodes for potassium-ion batteries based on first principles calculations
Bibliographic record
Abstract
Carbonaceous materials and the composite materials of transition metals compounds in carbon matrix were widely used as anode for potassium-ion batteries (PIBs). During the research of these anode materials, first-principles calculations based on adsorption energy, density of states (DOSs) as well as diffusion energy barriers was regarded as an effectively approach to investigate their potassium storage mechanism. The underlying reasons for the improvement of electrochemical performance could be well illustrated via the corresponding calculations. Moreover, first-principles calculations also played a vital role to predict the material properties of electrodes before conducting experimental analysis. Hence, this review is to analyze in-depth the effect mechanism of K-adsorption energy, DOSs as well as diffusion energy barrier and so on for electrochemical performance of carbon-based anode materials. We summarized the corresponding research progress, the challenges of first principles calculations in PIBs, and proposed the corresponding strategies along with future perspectives for further development of carbon-based anode materials. This work not only can provide theoretical guidance for the development of anode materials with excellent physical and chemical properties, but also have reference significance for other energy storage systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".