A DFT Study of Heteroatom Doped-Pyrazine as an Anode in Sodium ion Batteries
Bibliographic record
Abstract
Lithium ion batteries cannot satisfy increasing demand for energy storage. A range of complementary batteries are needed which are environmentally acceptable, of moderate cost and easy to manufacture/recycle. In this case, we have chosen pyrazine to be used in the sodium ion batteries to meet the energy storage requirements of tomorrow. Pyrazine is studied as a possible anode material for bio-batteries, lithium-ion, and sodium ion batteries due to its broad set of useful properties such as ease of synthesis, low cost, ability to be charge-discharge cycled, and stability in the electrolyte. The heteroatom doped-pyrazine with atoms of boron, fluorine, phosphorous, and sulphur as an anode in sodium ion batteries has improved the stability and intercalation of sodium ions at the anode. The longest bond observed between sodium ion and sulphur-doped pyrazine at 2.034 Å. The electronic charge is improved and further enhanced by the presence of highly electronegative atoms such as fluorine and bromine in an already electron-attracting pyrazine compound. The highest adsorption energy is observed for the boron-doped pyrazine at -2.735 eV. The electron-deficient sites present in fluorine and bromine help in improving the electronic storage of the sodium ion batteries. A mismatch is observed between the adsorption energy and bond length in pyrazine doped with fluorine and phosphorus.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".