Unveiling the potential of 3D TH-graphyne: a porous carbon anode for efficient potassium-ion storage
Bibliographic record
Abstract
Abstract Due to the abundance and low cost of potassium resources, potassium-ion batteries have emerged as promising alternatives to Li-ion batteries, with high capacity and fast ion transport. This study presents a novel three-dimensional triangular-hexagonal graphyne-like (3D TH-GY) carbon allotrope engineered by incorporating acetylenic linkers. Ab initio calculations confirm that the semi-metallic 3D TH-GY is thermally and dynamically stable, having a small bandgap of 0.04 eV. The electrochemical potential of 3D TH-GY, along with a recently proposed two-dimensional porous carbon allotrope (TH-GY), is assessed as an anode material for KIBs via DFT calculations. The study reveals strong K-ion binding, with values of –0.97 eV for 2D TH-GY and –1.53 eV for 3D TH-GY, respectively, facilitating efficient ion intercalation while ensuring favorable desorption kinetics. The calculated diffusion barriers for K-ion migration are exceptionally low (0.036 eV for 2D TH-GY, 0.12 eV for 3D TH-GY), indicating rapid ion transport across the surface. Moreover, the theoretical capacities of 2D TH-GY and 3D TH-GY reach 929.83 and 495.91 mAh g −1 , respectively, positioning them as promising candidates for high-performance charge–discharge cycles in KIBs. The combination of high charge storage capacity, fast ion diffusion, and structural robustness highlights TH-GY-based materials as compelling alternatives for next-generation potassium-ion battery anodes.
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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) | 0.000 | 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) | 0.001 | 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".