Role of Interchain Hopping in the Magnetic Susceptibility of Quasi-One-Dimensional Electron Systems
Bibliographic record
Abstract
The role of interchain hopping in quasi-one-dimensional (Q-1D) electron systems is investigated by extending the Kadanoff-Wilson renormalization group of one-dimensional (1D) systems to Q-1D systems. This scheme is applied to the extended Hubbard model to calculate the temperature ($T$) dependence of the magnetic susceptibility, $\chi (T)$. The calculation is performed by taking into account not only the logarithmic Cooper and Peierls channels, but also the non-logarithmic Landau and finite momentum Cooper channels, which give relevant contributions to the uniform response at finite temperatures. It is shown that the interchain hopping, $t_\perp$, reduces $\chi (T)$ at low temperatures, while it enhances $\chi(T)$ at high temperatures. This notable $t_\perp$ dependence is ascribed to the fact that $t_\perp$ enhances the antiferromagnetic spin fluctuation at low temperatures, while it suppresses the 1D fluctuation at high temperatures. The result is at variance with the random-phase-approximation approach, which predicts an enhancement of $\chi (T)$ by $t_\perp$ over the whole temperature range. The influence of both the long-range repulsion and the nesting deviations on $\chi (T)$ is further investigated. We discuss the present results in connection with the data of $\chi (T)$ in the (TMTTF)$_2X$ and (TMTSF)$_2X$ series of Q-1D organic conductors, and propose a theoretical prediction for the effect of pressure on magnetic susceptibility.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".