Computational Design of Hydrogenated Monolayer Pyrite for Enhanced Energy Storage
Bibliographic record
Abstract
Abstract In the search for clean energy technologies, it is crucial to develop low‐cost batteries with enhanced performance, and 2D materials are promising for electrode applications owing to their high surface area where fast ionic diffusion can occur. In this work, density functional theory calculations that demonstrate the great potential of recently synthesized 2D pyrite as a battery electrode are reported. An extensive analysis of its performance toward Li‐ion batteries and post‐lithium technologies (Na, K, Mg, Ca, Zn, Al), as well as how point defects can be leveraged to engineer its electronic properties are reported. First, the results explain that the main drawback of the unmodified material, namely its voltammetric peaks at high voltages, is due to the overly strong adsorption of lithium ions. Second, it is demonstrated that hydrogenation of the material leads to milder open‐circuit voltages without compromising the capacity of the anode, and lowers the diffusion barrier to only 0.06eV for both Li and K ions. With a capacity as high as 1317 mAh g−1 for Al‐ion, hydrogenated monolayer pyrite is demonstrated to be a promising material for energy storage applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".