Exploring anchoring performance of defective <scp>MgO</scp> nanotubes for lithium–sulphur batteries: A density functional theory ( <scp>DFT</scp> ) study
Bibliographic record
Abstract
Abstract In recent years, lithium–sulphur batteries have received great attention due to their high theoretical specific capacity. In the exploration of improving battery performance, developing suitable anchoring materials is one of the ways to suppress the shuttle effect which is one of the main problems of lithium–sulphur batteries. In this work, we investigated the anchoring ability of MgO nanotubes (MgONT) and defective MgO nanotubes (MgONTv) to lithium polysulphides (LiPSs) by density functional theory (DFT). The defect formation energy, the HOMO and LUMO of the defective MgONT, the energy difference (ΔE), and the adsorption energy were calculated. The optimized structures of LiPSs adsorbed on the MgONT and MgONTv were also obtained. The calculation results show that MgONTv1 has a strong adsorption effect on LiPSs, and its adsorption energy ranges from −1.78 to −4.51 eV. The adsorption of LiPSs narrows the bandgap of MgONTv. In other words, the conductivity of MgONTv is better than the pristine one. Our study demonstrates that MgONTv has more robust adsorption performance for LiPSs, which is an effective addictive material for the cathode of lithium–sulphur batteries. It can provide a theoretical basis for exploring the application of new one‐dimensional anchoring materials in the cathode of lithium–sulphur batteries.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".