Photon-assisted thermoelectric properties of noncollinear spin valves
Bibliographic record
Abstract
We report theoretical analysis of thermal-spin and thermoelectric properties of noncollinear spin valves driven by a high-frequency ac voltage bias. The spin valve consists of two ferromagnetic contacts sandwiching a single-level or multilevel quantum dot (QD). A general formulation for the time-averaged thermal-spin and thermoelectric properties of spin valves is derived within the nonequilibrium Green's function theory, which provides a starting point for further numerical calculations of these properties. Numerical results of a spin valve having a spin-degenerate single-level QD are given as an example. The ac bias induces various photon-assisted transmission peaks which can greatly enhance the Seebeck coefficients and the figures of merit, and offer a new possibility to tune both the spin-dependent and normal thermoelectric properties of the spin valve. Details of these properties and how they depend on the noncollinearity of the spin valve, magnetic polarization, ac frequency, ac bias, and other control parameters are reported. A particularly interesting result is the opposite dependency of the thermoelectric properties on the magnetic polarization and noncollinearity for contacts with or without spin accumulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".