Lattice constant-dependent anchoring effect of MXenes for lithium–sulfur (Li–S) batteries: a DFT study
Bibliographic record
Abstract
The anchoring effect of the cathode plays a significant role in improving the performance of lithium-sulfur (Li-S) batteries. MXenes, a new class of two-dimensional materials, have been reported to be effective sulfur hosts for Li-S batteries. However, previous studies mainly focused on Ti-based MXenes, while other potential transition metal MXenes have not been systematically explored. In the present work, we thoroughly investigated the interactions between lithium polysulfides (LiPSs) and a Ti2CO2 substrate, as well as six other M3C2O2 (M = Cr, V, Ti, Nb, Hf and Zr) MXenes using density functional theory (DFT) calculations. It is found that all six M3C2O2 systems possess trapping ability towards soluble LiPSs, largely attributed to the strong Li-O interactions between the LiPSs and the surface of the M3C2O2. Among them, Cr3C2O2 exhibited the strongest anchoring effect with the largest Eb. More importantly, a monotonical relationship between the binding energies and the lattice constants of M3C2O2 was identified, which indicated that M3C2O2 MXenes with a smaller lattice constant tend to exhibit a stronger anchoring effect. Furthermore, all six M3C2O2 MXenes showed metallic properties during the whole process. Our results shed light on the future rational selection and design of MXenes acting as sulfur hosts in Li-S batteries and on the potential to improve host-guest interactions in other energy storage systems.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".