Carbonaceous Materials as Anodes for Lithium-Ion and Sodium-Ion Batteries
Bibliographic record
Abstract
The increasing global population and, thus, energy demand have made research into renewable energy sources more critical. Lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs) have been recognized as the most promising technologies for storing energy and effectively addressing this demand. Carbonaceous materials are the most widespread anode material due to their fascinating features, such as high theoretical capacity, high electrical conductivity, and excellent structural stability. Additionally, these materials’ abundance, cost-effectiveness, and environmental friendliness have emphasized the need for further investigation and development. Among these carbon-based materials, graphite (both artificial and natural) stands out as the most ubiquitous anode material due to its layered crystal structure, high mechanical strength, long cycle life, and excellent safety profile, making it ideal for intercalation with lithium and sodium. In recent years, extensive research has been conducted to enhance the efficiency of anodes and, ultimately, the overall performance of batteries. In this review, the role of carbonaceous materials in anodes for lithium-ion and sodium-ion batteries was comprehensively investigated, focusing on advancements in synthesizing and optimizing artificial graphite. Furthermore, the intercalation mechanism and the factors influencing the electrochemical properties of both LIBs and SIBs were extensively discussed. This work also provides a holistic perspective on the differences between these two types of batteries, highlighting their cost, safety applications, and future potential advancement.
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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) | 0.002 | 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".