Graphene: Chemistry and Applications for Lithium-Ion Batteries
Bibliographic record
Abstract
In the present era, different allotropes of carbon have been discovered, and graphene is the one among them that has contributed to many breakthroughs in research. It has been considered a promising candidate in the research and academic fields, as well as in industries, over the last decade. It has many properties to be explored, such as an enhanced specific surface area and beneficial thermal and electrical conductivities. Graphene is arranged as a 2D structure by organizing sp2 hybridized C with alternative single and double bonds, providing an extended conjugation combining hexagonal ring structures to form a honeycomb structure. The precious structure and outstanding characteristics are the major reason that modern industry relies heavily on graphene, and it is predominantly applied in electronic devices. Nowadays, lithium-ion batteries (LIBs) foremostly utilize graphene as an anode or a cathode, and are combined with polymers to use them as polymer electrolytes. After three decades of commercialization of the lithium-ion battery, it still leads in consumer electronic society due to its higher energy density, wider operating voltages, low self-discharge, noble high-temperature performance, and fewer maintenance requirements. In this review, we aim to give a brief review of the domination of graphene and its applications in LIBs.
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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) | 0.000 | 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) | 0.003 | 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".