Recent Progress in Amorphous Carbon‐Based Materials for Anodes of Sodium‐Ion Batteries: Synthesis Strategies, Mechanisms, and Performance
Bibliographic record
Abstract
Sodium-ion batteries (SIBs) are gaining renewed interest as a promising alternative to the already commercialized lithium-ion batteries. The large abundance, low cost, and similar electrochemistry of sodium (compared with lithium) is attracting the attention of the research community for their deployment in energy storage devices. Despite the fact that there are adequate cathode materials, the choice of suitable anodes for SIBs is limited. Graphite, the most versatile anode for LIBs, exhibits poor performance in case of SIBs. Amorphous or disordered carbons (hard and soft carbon) have been the most promising and cost-effective anode materials for SIBs. This Review discusses the recent advances of various forms of amorphous or disordered carbons used in SIBs with emphasis on their synthesis processes and relationship between microstructure, morphology, and performance. A profound understanding of the charge storage mechanisms of sodium in these carbon materials has been deliberated. The performance of these anode materials also depends upon electrolyte optimization, which has been aptly conferred. However, these anodes are often plagued with large voltage loss, low initial coulombic efficiency, and formation of solid electrolyte interphase. In order to overcome these challenges, several mitigation strategies have been put forward in a concise way to offer visions for the deployment of these amorphous carbon materials for the progress and commercial success of SIBs.
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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 | 0.002 | 0.002 |
| 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".