Interfacial thermal transport between electrodes and polymer electrolytes can play a crucial role in the thermal management of solid-state lithium-ion batteries (SLIBs). Modifying the electrode surface with functional molecules can effectively increa
se the interfacial thermal conductance (ITC) between electrodes and polymers (e.g., electrolytes, separators); however, how they influence the interfacial thermal transport in SLIBs during charge/discharge remains unknown. In this work, we conduct molecular dynamics (MD) simulations to investigate the ITC between charged electrodes and solid-state polymer electrolytes (SPEs) mixed with ionic liquids (ILs). We find that ILs could self assemble at the electrode surface and act as non-covalent functional molecules that could significantly enhance the interfacial thermal transport during charge/discharge because of the formation of a densely packed cationic or anionic layer at the interface. While the electrostatic interactions between the charged electrode and the IL ions are responsible for forming these dense interfacial layers, the enhancement of ITC is mainly contributed by the increased Lennard-Jones (LJ) interactions between the charged electrodes and ILs. This work may provide useful insights into the understanding of interfacial thermal transport between electrodes and electrolytes of SLIBs during charge/discharge.
Recent experimental breakthrough in magnetic Weyl semimetals have inspired exploration on the novel effects of various magnetic structures in these materials. Here we focus on a domain wall structure which connects two uniform domains with different
magnetization directions. We study the topological superconducting state in presence of an s-wave superconducting pairing potential. By tuning the chemical potential, we can reach a topological state, where a chiral Majorana mode or zero-energy Majorana bound state is localized at the edges of the domain walls. This property allows a convenient braiding operation of Majorana modes by controlling the dynamics of domain walls.
The Higgs mode associated with amplitude fluctuations of the superconducting gap in uniform superconductors usually is heavy, which makes its excitation and detection difficult. We report on the existence of a gapless Higgs mode in the Fulde-Ferrell-
Larkin-Ovchinnikov states. This feature is originated from the Goldstone mode associated with the translation symmetry breaking. The existence of the gapless Higgs mode is demonstrated by using both a phenomenological model and microscopic Bardeen-Cooper-Schrieffer (BCS) theory. The gapless Higgs mode can avoid the decay into other low energy excitations, which renders it stable and detectable.
In this work, we investigate the radiation-induced segregation (RIS) resulting from the coupling between the atomic and point defect (PD) fluxes towards the structural defects of the microstructure. This flux coupling depends on the migration mechani
sms of PDs and atoms, including thermal diffusion mechanisms and forced atomic relocations (FAR) occurring in displacement cascades. We derive an analytic model of the PD and solute RIS profiles accounting for PD production and mutual recombination, the FAR mechanism, and the overall sink strength of the microstructure controlling the elimination of PDs at structural defects. From this model, we present a parametric investigation of diffusion and RIS properties in dilute Fe-$B$ ($B$ = P, Mn, Cr, Si, Ni, and Cu) binary alloys, in the form of quantitative temperature/radiation flux/sink strength maps. As in previous works, we distinguish three kinetic domains for the diffusion and RIS properties: the recombination domain, the sink domain, and the thermal domain. Both our analytical approach and numerical applications demonstrate that the diffusion and RIS behaviors of PDs and solute atoms largely differ from one kinetic domain to another. Moreover, at high radiation flux, low temperature, and large sink strength, FARs tend to destroy the solute RIS profiles and therefore reduce the overall amount of RIS by forcing the mixing of solute and host atoms, especially close to PD sinks. Finally, we provide quantitative criteria to emulate in-reactor RIS behaviors by ion irradiation.
We predict that CeBi in the ferromagnetic state is a Weyl semimetal. Our calculations within density functional theory show the existence of two pairs of Weyl nodes on the momentum path $(0, 0, k_z)$ at $15$ meV} above and $100$ meV below the Fermi l
evel. Two corresponding Fermi arcs are obtained on surfaces of mirror-symmetric (010)-oriented slabs at $E=15$ meV and both arcs are interrupted into three segments due to hybridization with a set of trivial surface bands. By studying the spin texture of surface states, we find the two Fermi arcs are strongly spin-polarized but in opposite directions, which can be detected by spin-polarized ARPES measurements. Our theoretical study of quasiparticle interference (QPI) for a nonmagnetic impurity at the Bi site also reveals several features related to the Fermi arcs. Specifically, we predict that the spin polarization of the Fermi arcs leads to a bifurcation-shaped feature only in the spin-dependent QPI spectrum, serving as a fingerprint of the Weyl nodes.
In this paper, we develop RCC, the first unified and comprehensive RDMA-enabled distributed transaction processing framework supporting six serializable concurrency control protocols: not only the classical protocols NOWAIT, WAITDIE, and OCC, but als
o more advanced MVCC and SUNDIAL, and even CALVIN, the deterministic concurrency control protocol. Our goal is to unbiasedly compare the protocols in a common execution environment with the concurrency control protocol being the only changeable component. We focus on the correct and efficient implementation using key techniques, such as co-routines, outstanding requests, and doorbell batching, with two-sided and one-sided communication primitives. Based on RCC, we get the deep insights that cannot be obtained by any existing systems. Most importantly, we obtain the execution stage latency breakdowns with one-sided and two-sided primitive for each protocol, which are analyzed to develop more efficient hybrid implementations. Our results show that three hybrid designs are indeed better than both one-sided and two-sided implementations by up to 17.8%. We believe that RCC is a significant advance over the state-of-the-art; it can both provide performance insights and be used as the common infrastructure for fast prototyping new implementations.
Understanding the growth dynamics of the microbubbles produced by plasmonic heating can benefit a wide range of applications like microfluidics, catalysis, micro-patterning and photo-thermal energy conversion. Usually, surface plasmonic bubbles are g
enerated on plasmonic structures pre-deposited on the surface subject to laser heating. In this work, we investigate the growth dynamics of surface microbubbles generated in plasmonic nanoparticle (NP) suspension. We observe much faster bubble growth rates compared to those in pure water with surface plasmonic structures. Our analyses show that the volumetric heating effect around the surface bubble due to the existence of NPs in the suspension is the key to explain this difference. Such volumetric heating increases the temperature around the surface bubble more efficiently compared to surface heating which enhances the expelling of dissolved gas. We also find that the bubble growth rates can be tuned in a very wide range by changing the concentration of NPs, besides laser power and dissolved gas concentration.
Josephson radiation is a powerful method to probe Majorana zero modes in topological superconductors. Recently, Josephson radiation with half the Josephson frequency has been experimentally observed in a HgTe-based junction, possibly from Majorana ze
ro modes. However, this radiation vanishes above a critical voltage, sharply contradicting previous theoretical results. In this work, we theoretically obtain a radiation spectrum quantitatively in agreement with the experiment after including the nonlinear dynamics of the Majorana states into the standard resistively shunted junction model. We further predict two new structures of the radiation spectrum for future experimental verification: an interrupted emission line and a chaotic regime. We develop a fixed-point analysis to understand all these features. Our results resolve an apparent discrepancy between theory and experiments, and will inspire reexamination of structures in radiation spectra of various topological Josephson junctions.
Although n-gram language models (LMs) have been outperformed by the state-of-the-art neural LMs, they are still widely used in speech recognition due to its high efficiency in inference. In this paper, we demonstrate that n-gram LM can be improved by
neural LMs through a text generation based data augmentation method. In contrast to previous approaches, we employ a large-scale general domain pre-training followed by in-domain fine-tuning strategy to construct deep Transformer based neural LMs. Large amount of in-domain text data is generated with the well trained deep Transformer to construct new n-gram LMs, which are then interpolated with baseline n-gram systems. Empirical studies on different speech recognition tasks show that the proposed approach can effectively improve recognition accuracy. In particular, our proposed approach brings significant relative word error rate reduction up to 6.0% for domains with limited in-domain data.
