MXenes with entropy-derived mitigation of shuttle effect in sodium-sulfur batteries
Bibliographic record
Abstract
• DFT analysis of high-entropy Ti 2 CO 2 MXenes for mitigating the shuttle effect in NaSBs. • Reduced Cr concentration enhances stability in high-entropy MXenes at standard conditions. • Improved chemisorption supports shuttle effect suppression during battery discharge. • High-entropy MXenes exhibit increased electronic states, enabling faster electrochemical activity. • Moderate energy barrier promotes efficient Na + restoration during battery charging. Room-temperature sodium-sulfur batteries (NaSBs) hold great promise for large-scale energy storage due to their high energy density and resource abundance. However, the notorious shuttle effect, caused by the dissolution of sodium polysulfides (Na 2 S n ; n = 1–8) in electrolyte, significantly undermines the performance and lifespan of NaSBs. To address this, we investigate high-entropy (HE) Ti 2 CO 2 MXenes, where Ti is partially substituted with Mn, V, and Cr, as cathode additives. The configurational entropy stabilizes crystal structure, enhances electronic properties, and enables strong adsorption of sodium polysulfides. Ab-initio molecular dynamics confirm the stability of HE-MXenes with reduced Cr, e.g ., (Ti 0.25 Mo 0.5 V 0.125 Cr 0.125 ) 2 CO 2 , which exhibit significantly higher binding energies for polysulfides (−1.672 to −3.99 eV) compared to pristine MXene (−0.987 to −1.43 eV) and common electrolytes like DOL and DME (−0.18 to −0.98 eV). These systems reduce Na 2 S dissociation energy barrier (∼ 0.92 eV), promoting efficient Na + ion diffusion. HE-MXenes also show enhanced density of electronic states at the Fermi level, facilitating faster electrochemical processes. The combination of strong polysulfide binding, low dissociation barriers, and stable electronic conductivity effectively mitigates the shuttle effect while improving overall electrochemical performance of NaSBs. HE-MXenes present a robust strategy for advancing NaSB technology, offering superior cycling stability and longer operational lifespans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".