Controlling the magnetic properties of two-dimensional carbon-based Kagome polymers
Bibliographic record
Abstract
With the help of first principles calculations, we have explored a promising route to control the magnetic properties of two-dimensional organic polymers based on all-carbon triangulene monomers. Similar to small triangulene nanostructures, the Kagome-organized triangulene polymer exhibits an antiferromagnetic ground state, but behaves as a Mott-insulator with relatively poor carrier mobilities. The doping of triangulenes with boron or nitrogen atoms contributes to switch the ground state of the polymer into a stable ferromagnetic phase, well separated in energy from the antiferromagnetic phase. The existence of a stable ferromagnetic phase is a direct consequence of electron confinement within B/N-rich triangulenes, where the D3h symmetry of the monomers in the Kagome pattern plays a major role on the resulting electronic structure properties. In addition, the two-dimensional Kagome lattice arrangement of B-rich triangulene polymer leads to highly dispersed spin-polarized semiconducting bands and high carrier mobilities that largely exceed known values for pure silicon. In contrast, the ferromagnetic phase of N-rich polymer shows half-metallic behaviour with lower mobilities than B-rich cases. Our results suggest that triangulene-based polymers could be used in diverse sectors from spin-based logic devices to quantum 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 | 0.000 |
| 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".