Transport and optics at the node in a nodal loop semimetal
Bibliographic record
Abstract
We use a Kubo formalism to calculate both AC conductivity and DC transport properties of a dirty nodal loop semimetal. The optical conductivity as a function of photon energy $\mathrm{\ensuremath{\Omega}}$ exhibits an extended flat background ${\ensuremath{\sigma}}^{\mathrm{BG}}$ as in graphene provided the scattering rate $\mathrm{\ensuremath{\Gamma}}$ is small as compared to the radius of the nodal ring $b$ (in energy units). Modifications to the constant background arise for $\mathrm{\ensuremath{\Omega}}\ensuremath{\le}\mathrm{\ensuremath{\Gamma}}$ and the minimum DC conductivity ${\ensuremath{\sigma}}^{\mathrm{DC}}$, which is approached as ${\mathrm{\ensuremath{\Omega}}}^{2}/{\mathrm{\ensuremath{\Gamma}}}^{2}$ as $\mathrm{\ensuremath{\Omega}}\ensuremath{\rightarrow}0$, is found to be proportional to $\frac{\sqrt{{\mathrm{\ensuremath{\Gamma}}}^{2}+{b}^{2}}}{{v}_{F}}$ with ${v}_{F}$ the Fermi velocity. For $b=0$ we recover the known three-dimensional point node Dirac result ${\ensuremath{\sigma}}^{\mathrm{DC}}\ensuremath{\sim}\frac{\mathrm{\ensuremath{\Gamma}}}{{v}_{F}}$ while for $b>\mathrm{\ensuremath{\Gamma}}, {\ensuremath{\sigma}}^{\mathrm{DC}}$ becomes independent of $\mathrm{\ensuremath{\Gamma}}$ (universal) and the ratio $\frac{{\ensuremath{\sigma}}^{\mathrm{DC}}}{{\ensuremath{\sigma}}^{\mathrm{BG}}}=\frac{8}{{\ensuremath{\pi}}^{2}}$ where all reference to material parameters has dropped out. As $b$ is reduced and becomes of the order $\mathrm{\ensuremath{\Gamma}}$, the flat background is lost as the optical response evolves towards that of a three-dimensional point node Dirac semimetal which is linear in $\mathrm{\ensuremath{\Omega}}$ for the clean limit. For finite $\mathrm{\ensuremath{\Gamma}}$ there are modifications from linearity in the photon region $\mathrm{\ensuremath{\Omega}}\ensuremath{\le}\mathrm{\ensuremath{\Gamma}}$. When the chemical potential $\ensuremath{\mu}$ (temperature $T$) is nonzero the DC conductivity increases as ${\ensuremath{\mu}}^{2}/{\mathrm{\ensuremath{\Gamma}}}^{2}$(${T}^{2}/{\mathrm{\ensuremath{\Gamma}}}^{2}$) for $\frac{\ensuremath{\mu}}{\mathrm{\ensuremath{\Gamma}}} \left(\frac{T}{\mathrm{\ensuremath{\Gamma}}}\right)\ensuremath{\le}1$. Such laws apply as well for thermal conductivity and thermopower with coefficients of the quadratic law only slightly modified from their value in the three-dimensional point node Dirac case. However in the $\ensuremath{\mu}=T=0$ limit both have the same proportionality factor of $\sqrt{{\mathrm{\ensuremath{\Gamma}}}^{2}+{b}^{2}}$ as does ${\ensuremath{\sigma}}^{\mathrm{DC}}$. Consequently the Lorentz number is largely unmodified. For larger values of $\ensuremath{\mu}>\mathrm{\ensuremath{\Gamma}}$ away from the nodal region the conductivity shows a Drude-like contribution about $\mathrm{\ensuremath{\Omega}}\ensuremath{\approxeq}0$ which is followed by a dip in the Pauli blocked region $\mathrm{\ensuremath{\Omega}}\ensuremath{\le}2\ensuremath{\mu}$ after which it increases to merge with the flat background (two-dimensional graphene like) for $\ensuremath{\mu}b$.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".