Unconventional thermoelectric behaviors and enhancement of figure of merit in Rashba spintronic systems

Thermoelectric transport in strongly spin-orbit coupled two-dimensional Rashba system is studied using the exact solution of the linearized Boltzmann equation. Some unusual transport behaviors are revealed. We show that the electrical conductivity takes a Drude form when the Fermi energy $E_{F}$ is above the band crossing point, but a non-Drude form which is a quadratic function of $E_{F}$ when $E_{F}$ lies below the band crossing point. The Mott relation breaks down when $E_{F}$ lies in the vicinity of the band crossing point. It is shown that the thermopower and thermoelectric figure of merit are strongly enhanced when $E_{F}$ downs below the band crossing point. This enhancement is attributed to not only the one-dimensional-like density of state but also the unconventional intraband elastic scattering below the band crossing point. The differences between these results and those obtained by the relaxation time approximation are discussed in detail.Comment: 9 pages, 5 figure

Thermoelectric response of spin polarization in Rashba spintronic systems

Motivated by recent discovery of strongly spin-orbit coupled two-dimensional (2D) electron gas near the surface of Rashba semiconductors BiTeX (X=Cl, Br, I), we calculate thermoelectric responses of spin polarization in 2D Rashba model using an exact solution of the linearized Boltzmann equation for elastic scattering. When the Fermi energy $E_{F}$ lies below the band crossing point we find a non-Edelstein electric-field induced spin polarization which is linear in $E_{F}$. We show that the Mott-like relation between spin polarizations induced by the temperature gradient and electric field breaks down significantly when $E_{F}$ lies in the vicinity of the band crossing point. As the temperature tends to zero, the temperature-gradient induced spin polarization vanishes. These results differ from previous ones obtained by relaxation time approximations.Comment: 9 pages, 3 figure

Scaling Law for Time-Reversal-Odd Nonlinear Transport

Time-reversal-odd ($\mathcal{T}$-odd) nonlinear current response has been theoretically proposed and experimentally confirmed recently. However, the role of disorder scattering in the response, especially whether it contributes to the $\sigma_{xx}$-independent term, has not been clarified. In this work, we derive a general scaling law for this effect, which accounts for multiple scattering sources. We show that the nonlinear conductivity is generally a quartic function in $\sigma_{xx}$. Besides intrinsic contribution, extrinsic contributions from scattering also enter the zeroth order term, and their values can be comparable to or even larger than the intrinsic one. Terms beyond zeroth order are all extrinsic. Cubic and quartic terms must involve skew scattering and they signal competition between at least two scattering sources. The behavior of zeroth order extrinsic terms is explicitly demonstrated in a Dirac model. Our finding reveals the significant role of disorder scattering in $\mathcal{T}$-odd nonlinear transport, and establishes a foundation for analyzing experimental result.Comment: 5 pages, 1 figur

Uncertainty-Aware Bootstrap Learning for Joint Extraction on Distantly-Supervised Data

Jointly extracting entity pairs and their relations is challenging when working on distantly-supervised data with ambiguous or noisy labels. To mitigate such impact, we propose uncertainty-aware bootstrap learning, which is motivated by the intuition that the higher uncertainty of an instance, the more likely the model confidence is inconsistent with the ground truths. Specifically, we first explore instance-level data uncertainty to create an initial high-confident examples. Such subset serves as filtering noisy instances and facilitating the model to converge fast at the early stage. During bootstrap learning, we propose self-ensembling as a regularizer to alleviate inter-model uncertainty produced by noisy labels. We further define probability variance of joint tagging probabilities to estimate inner-model parametric uncertainty, which is used to select and build up new reliable training instances for the next iteration. Experimental results on two large datasets reveal that our approach outperforms existing strong baselines and related methods.Comment: ACL 2023 main conference short pape

Synthesis and structure of the inclusion complex {NdQ[5]K@Q[10](H₂O)4}·4NO₃·20H₂O

Heating a mixture of Nd(NO₃)₃·6H₂O, KCl, Q[10] and Q[5] in HCl for 10 min affords the inclusion complex {NdQ[5]K@Q[10](H₂O)₄}·4NO₃·20H₂O. The structure of the inclusion complex has been investigated by single crystal X-ray diffraction and by X-ray Photoelectron spectroscopy (XPS)