Intercalation chemistry of graphite: alkali metal ions and beyond
Bibliographic record
Abstract
Reversibly intercalating ions into host materials for electrochemical energy storage is the essence of the working principle of rocking-chair type batteries. The most relevant example is the graphite anode for rechargeable Li-ion batteries which has been commercialized in 1991 and still represents the benchmark anode in Li-ion batteries 30 years later. Learning from past lessons on alkali metal intercalation in graphite, recent breakthroughs in sodium and potassium intercalation in graphite have been demonstrated for Na-ion batteries and K-ion batteries. Interestingly, some significant differences proved to exist for the intercalation of Na+ and K+ into graphite compared with the Li+ case. Such different host-guest interactions are unique depending on the host materials and electrolytes, which greatly contribute to a deeper understanding of intercalation-type electrode materials for next generation alkali metal ion batteries. This review summarizes significant advances from both experimental and theoretical calculations with a focus on comparing the intercalation of three alkali metal ions (Li+, Na+, K+) into graphite and aims to clarify the intimate host-guest relationships and the underlying mechanisms. New approaches developed to achieve favorable intercalation coupled with the challenges in this field are also discussed. We also extrapolate alkali metal ion intercalation in graphite to mono-/multi-valent ions in layered electrode materials, which will deepen the understanding of intercalation chemistry and provide guidance to explore new guests and hosts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".