Sodium ion batteries: A sustainable alternative to lithium-ion batteries with an overview of market trends, recycling, and battery chemistry
Bibliographic record
Abstract
Sodium-ion batteries (SIBs) are being actively investigated as a potentially viable and more sustainable alternative to lithium-ion batteries (LIBs), driven by concerns over lithium resource scarcity, high production costs, and environmentally challenging extraction methods. While LIBs dominate applications in consumer electronics and electric vehicles due to their superior energy density and maturity, SIBs offer notable advantages, such as using earth-abundant and low-cost elements like sodium and aluminum. Despite current limitations in energy density and cycle life, ongoing research in electrode materials and cell design has yielded encouraging progress in enhancing the electrochemical performance and safety profile of SIBs. In particular, their improved thermal stability offers potential benefits for stationary energy storage applications where safety is critical. The development of SIBs aligns with global Sustainable Development Goals (SDGs), particularly SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action), by promoting safer and potentially lower-cost energy storage technologies. Continued advancements in material innovation, system integration, and end-of-life recycling will be key to the commercial competitiveness of SIBs. This review emphasizes the potential of SIBs as a viable alternative to LIBs by integrating electrochemical, economic, and environmental perspectives amid growing concerns over lithium supply and cost. For sustainable energy solutions and provides valuable insights into the current state of SIB research, offering a roadmap for future developments in this field.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".