Ti-decorated SiC2 as a high-performance anode material for Li-ion batteries: A DFT-D2 approach
Bibliographic record
Abstract
This study employs dispersion-corrected DFT-D2 calculations to investigate Li adsorption on pristine and Ti-decorated SiC 2 , evaluating their potential as anode materials for Li-ion batteries. Key analyses, including adsorption energy , density of states (DOS), Bader charge, diffusion barrier , and open-circuit voltage (OCV), reveal that the incorporation of titanium (Ti) into SiC 2 significantly enhances the electrochemical performance , stability, and lithium atom diffusion characteristics of the material. Ti increases the adsorption energy, Eads, from −1.422 eV for SiC 2 to −1.641 eV for Ti-decorated SiC 2 , strengthening the bond between lithium ions and the substrate. This stronger interaction improves capacity retention and cycling stability by reducing lithium desorption during cycling. While this increase in adsorption energy may slightly impede lithium diffusion, it contributes to greater structural stability and durability under high-rate charging and discharging conditions. Additionally, OCV is enhanced from 0.340 V in SiC 2 to 0.392 V in Ti-decorated SiC 2 , improving the overall energy output. The lattice constants exhibit a minimal change of only 0.21 %, indicating that lithium intercalation and deintercalation during battery charge and discharge cycles have an insignificant impact on volume variation. With a capacity of 965.25 mAh/g, Ti-decorated SiC 2 achieves a more favorable balance of stability, rate capability, and energy efficiency compared to undoped SiC 2 , making it a promising material for practical, long-term applications in lithium-ion 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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".