Low‐temperature performance of Na‐ion batteries
Bibliographic record
Abstract
Abstract Sodium‐ion batteries (NIBs) have become an ideal alternative to lithium‐ion batteries in the field of electrochemical energy storage due to their abundant raw materials and cost‐effectiveness. With the progress of human society, the requirements for energy storage systems in extreme environments, such as deep‐sea exploration, aerospace missions, and tunnel operations, have become more stringent. The comprehensive performance of NIBs at low temperatures (LTs) has also become an important consideration. Under LT conditions, challenges such as increased viscosity of electrolyte, abnormal growth of solid electrolyte interface, and poor contact between collector and electrode materials emerge. The aforementioned issues hinder the diffusion kinetics of sodium ions (Na + ) at the electrode/electrolyte interface and cause rapid degradation of battery performance. Consequently, the optimization of electrolyte composition and cathode/anode materials becomes an effective approach to improve LT performance. This review discusses the conduction behavior and limiting factors of Na + in both solid electrodes and liquid electrolytes at LT. Furthermore, it systematically reviews the recent research progress of LT NIBs from three aspects: cathode materials, anode materials, and electrolyte components. This review aims to provide a valuable reference for developing high‐performance LT NIBs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".