Nonmetal doping of HfS2 monolayers for enhanced adsorption and diffusion of Li and Na ions in anode materials for alkali-ion batteries
Bibliographic record
Abstract
• Formation energy of nonmetal-doped HfS 2 is favorable for experimental synthesis. • Doping significantly improves the adsorption of Li/Na ions over the HfS 2 monolayer. • Na experienced a low diffusion energy barrier of 0.04 eV over N-doped HfS 2 surface. • A high storage capacity of 441.86 mAh/g is achieved for the HfS 2 monolayer. Monolayer hafnium disulfide is emerging as a promising two-dimensional material due to its high theoretical electron mobility and surface current density, making it an excellent candidate for alkali-ion battery (AIBs) applications. This work applies first-principles calculations to investigate adsorption and diffusion characteristics of lithium and sodium ions on HfS 2 monolayers. The low diffusion energy barriers of 0.2 eV for Li and 0.11 eV for Na indicate that these ions possess ultrahigh mobility over HfS 2 surface, attractive for rapid charging and discharging rates in battery applications. Moreover, doping HfS 2 monolayer with nonmetal elements, boron, carbon, nitrogen and phosphorus significantly enhances the binding strength of Li and Na ions on the doped surface. For instance, carbon doping increases the adsorption energy from −3.15 to 4.76 eV for Li and from −2.49 to 3.55 eV for Na. The nitrogen-doped HfS 2 monolayer exhibits the lowest diffusion energy barriers of 0.15 eV for Li and 0.04 eV for Na adsorption. Additionally, high specific storage capacity of 441.86 mAh/g is achieved with four layers of Li adsorption over HfS 2 surface. These findings demonstrate that nonmetal doping of HfS 2 monolayers significantly improves adsorption and diffusion properties of Li and Na ions, highlighting their potential application in AIBs.
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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 | 0.000 | 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 | 0.000 | 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".