Recent Advances in Nanostructured Thermoelectric Half-Heusler Compounds

Half-Heusler (HH) alloys have attracted considerable interest as promising thermoelectric (TE) materials in the temperature range around 700 K and above, which is close to the temperature range of most industrial waste heat sources. The past few years have seen nanostructuing play an important role in significantly enhancing the TE performance of several HH alloys. In this article, we briefly review the recent progress and advances in these HH nanocomposites. We begin by presenting the structure of HH alloys and the different strategies that have been utilized for improving the TE properties of HH alloys. Next, we review the details of HH nanocomposites as obtained by different techniques. Finally, the review closes by highlighting several promising strategies for further research directions in these very promising TE materials.Comment: 34 pages, 22 figure

Beyond Universal Transformer: block reusing with adaptor in Transformer for automatic speech recognition

Transformer-based models have recently made significant achievements in the application of end-to-end (E2E) automatic speech recognition (ASR). It is possible to deploy the E2E ASR system on smart devices with the help of Transformer-based models. While these models still have the disadvantage of requiring a large number of model parameters. To overcome the drawback of universal Transformer models for the application of ASR on edge devices, we propose a solution that can reuse the block in Transformer models for the occasion of the small footprint ASR system, which meets the objective of accommodating resource limitations without compromising recognition accuracy. Specifically, we design a novel block-reusing strategy for speech Transformer (BRST) to enhance the effectiveness of parameters and propose an adapter module (ADM) that can produce a compact and adaptable model with only a few additional trainable parameters accompanying each reusing block. We conducted an experiment with the proposed method on the public AISHELL-1 corpus, and the results show that the proposed approach achieves the character error rate (CER) of 9.3%/6.63% with only 7.6M/8.3M parameters without and with the ADM, respectively. In addition, we also make a deeper analysis to show the effect of ADM in the general block-reusing method

Scene Matters: Model-based Deep Video Compression

Video compression has always been a popular research area, where many traditional and deep video compression methods have been proposed. These methods typically rely on signal prediction theory to enhance compression performance by designing high efficient intra and inter prediction strategies and compressing video frames one by one. In this paper, we propose a novel model-based video compression (MVC) framework that regards scenes as the fundamental units for video sequences. Our proposed MVC directly models the intensity variation of the entire video sequence in one scene, seeking non-redundant representations instead of reducing redundancy through spatio-temporal predictions. To achieve this, we employ implicit neural representation as our basic modeling architecture. To improve the efficiency of video modeling, we first propose context-related spatial positional embedding and frequency domain supervision in spatial context enhancement. For temporal correlation capturing, we design the scene flow constrain mechanism and temporal contrastive loss. Extensive experimental results demonstrate that our method achieves up to a 20\% bitrate reduction compared to the latest video coding standard H.266 and is more efficient in decoding than existing video coding strategies

Ultra‐Fast One‐Step Fabrication of Cu2Se Thermoelectric Legs With Ni–Al Electrodes by Plasma‐Activated Reactive Sintering Technique

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/133615/1/adem201500548-sup-0001-SupFigs-S1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/133615/2/adem201500548.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/133615/3/adem201500548_am.pd

High performance Bi2Te3 nanocomposites prepared by single-element-melt-spinning spark-plasma sintering

The last decade has witnessed nanocomposites becoming a new paradigm in the field of thermoelectric (TE) research. At its core is to prepare high performance TE nanocomposites, both p- and n-type, in a time and energy efficient way. To this end, we in this article summarize our recent effort and results on both p- and n-type Bi2Te3-based nanocomposites prepared by a unique single-element-melt-spinning spark-plasma sintering procedure. The results of transport measurements, scanning and transmission electronic microscopy, and small angle neutron scattering have proved essential in order to establish the correlation between the nanostructures and the TE performance of the materials. Interestingly, we find that in situ formed nanocrystals with coherent boundaries are the key nanostructures responsible for the significantly improved TE performance of p-type Bi2Te3 nanocomposites whereas similar nanostructures turn out to be less effective for n-type Bi2Te3 nanocomposites. We also discuss the alternative strategies to further improve the TE performance of n-type Bi2Te3 materials via nanostructuring processe

Optimizing the thermoelectric performance of zigzag and chiral carbon nanotubes

Using nonequilibrium molecular dynamics simulations and nonequilibrium Green's function method, we investigate the thermoelectric properties of a series of zigzag and chiral carbon nanotubes which exhibit interesting diameter and chirality dependence. Our calculated results indicate that these carbon nanotubes could have higher ZT values at appropriate carrier concentration and operating temperature. Moreover, their thermoelectric performance can be significantly enhanced via isotope substitution, isoelectronic impurities, and hydrogen adsorption. It is thus reasonable to expect that carbon nanotubes may be promising candidates for high-performance thermoelectric materials

Electrical conductivity adjustment for interface capacitive-like storage in sodium-ion battery

Sodium-ion battery (SIB) is significant for grid-scale energy storage. However, a large radius of Na ions raises the difficulties of ion intercalation, hindering the electrochemical performance during fast charge/discharge. Conventional strategies to promote rate performance focus on the optimization of ion diffusion. Improving interface capacitive-like storage by tuning the electrical conductivity of electrodes is also expected to combine the features of the high energy density of batteries and the high power density of capacitors. Inspired by this concept, an oxide-metal sandwich 3D-ordered macroporous architecture (3DOM) stands out as a superior anode candidate for high-rate SIBs. Taking Ni-TiO2 sandwich 3DOM as a proof-of-concept, anatase TiO2 delivers a reversible capacity of 233.3 mAh g^-1 in half-cells and 210.1 mAh g^-1 in full-cells after 100 cycles at 50 mA g^-1. At the high charge/discharge rate of 5000 mA g^-1, 104.4 mAh g^-1 in half-cells and 68 mAh g^-1 in full-cells can also be obtained with satisfying stability. In-depth analysis of electrochemical kinetics evidence that the dominated interface capacitive-like storage enables ultrafast uptaking and releasing of Na-ions. This understanding between electrical conductivity and rate performance of SIBs is expected to guild future design to realize effective energy storage

The microstructure network and thermoelectric properties of bulk (Bi,Sb)₂Te₃

We report small-angle neutron scattering studies on the microstructure network in bulk (Bi,Sb)(2)Te-3 synthesized by the melt-spinning (MS) and the spark-plasma-sintering (SPS) process. We find that rough interfaces of multiscale microstructures generated by the MS are responsible for the large reduction of both lattice thermal conductivity and electrical conductivity. Our study also finds that subsequent SPS forms a microstructure network of similar to 10 nm thick lamellae and smooth interfaces between them. This nanoscale microstructure network with smooth interfaces increases electrical conductivity while keeping a low thermal conductivity, making it an ideal microstructure for high thermoelectric efficiency