Modulate the direct-current and alternating-current transport properties of magnetic γ-graphyne heterojunctions by chemical modification
Bibliographic record
Abstract
Using density functional theory and the non-equilibrium Green's function method, we theoretically investigated the direct-current (DC) and alternating-current (AC) quantum transport properties of magnetic γ-graphyne heterojunctions. For the DC case, we found that the γ-graphyne heterojunction has rich transport properties such as spin-filtering and magnetoresistance effects. As the marginal H atoms of the heterojunction are replaced by O atoms, an outstanding dual spin-filtering phenomenon appears and the magnetoresistance is enhanced. Meanwhile, after chemical modification, the heterojunction exhibits a noticeable rectification effect. For the AC case, depending on the frequency, the total and spin AC conductances can be capacitive, inductive, or resistive. At some given frequencies, the signs of the imaginary parts of the AC conductances for two different spins are opposite; thus, the two spin currents have opposite AC responses. A significant photon-assisted tunneling effect was found in the heterojunctions at high frequency range. More interestingly, after chemical modification in a wide frequency range, the imaginary part of the AC conductance changes the sign, indicating that the AC transport properties of the γ-graphyne heterojunction can be effectively modulated by chemical methods.
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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) | 0.000 | 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 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".