Promising thermoelectric performance in van der Waals layered SnSe2

SnSe as a lead-free IV–VI semiconductor, has attracted intensive attention for its potential thermoelectric applications, since it is less toxic and much cheaper than conventional PbTe and PbSe thermoelectrics. Here we focus on its sister layered compound SnSe2 in n-type showing a thermoelectric performance to be similarly promising as SnSe in the polycrystalline form. This is enabled by its favorable electronic structure according to first principle calculations, its capability to be effectively doped by bromine on selenium site to optimize the carrier concentration, as well as its intrinsic lattice thermal conductivity as low as 0.4 W/m-K due to the weak van der Waals force between layers. The broad carrier concentration ranging from 0.5 to 6 × 1019 cm−3 realized in this work, further leads to a fundamental understanding on the material parameters determining the thermoelectric transport properties, based on a single parabolic band (SPB) model with acoustic scattering. The layered crystal structure leads to a texture in hot-pressed polycrystalline materials and therefore anisotropic transport properties, which can be well understood by the SPB model. This work not only demonstrates SnSe2 as a promising thermoelectric material but also guides the further improvements particularly by band engineering and texturing approaches

Biofuel Manufacturing from Woody Biomass: Effects of Sieve Size Used in Biomass Size Reduction

Size reduction is the first step for manufacturing biofuels from woody biomass. It is usually performed using milling machines and the particle size is controlled by the size of the sieve installed on a milling machine. There are reported studies about the effects of sieve size on energy consumption in milling of woody biomass. These studies show that energy consumption increased dramatically as sieve size became smaller. However, in these studies, the sugar yield (proportional to biofuel yield) in hydrolysis of the milled woody biomass was not measured. The lack of comprehensive studies about the effects of sieve size on energy consumption in biomass milling and sugar yield in hydrolysis process makes it difficult to decide which sieve size should be selected in order to minimize the energy consumption in size reduction and maximize the sugar yield in hydrolysis. The purpose of this paper is to fill this gap in the literature. In this paper, knife milling of poplar wood was conducted using sieves of three sizes (1, 2, and 4 mm). Results show that, as sieve size increased, energy consumption in knife milling decreased and sugar yield in hydrolysis increased in the tested range of particle sizes

Low-Symmetry Rhombohedral GeTe Thermoelectrics

High-symmetry thermoelectric materials usually have the advantage of very high band degeneracy, while low-symmetry thermoelectrics have the advantage of very low lattice thermal conductivity. If the symmetry breaking of band degeneracy is small, both effects may be realized simultaneously. Here we demonstrate this principle in rhombohedral GeTe alloys, having a slightly reduced symmetry from its cubic structure, to realize a record figure of merit (zT ∼ 2.4) at 600 K. This is enabled by the control of rhombohedral distortion in crystal structure for engineering the split low-symmetry bands to be converged and the resultant compositional complexity for simultaneously reducing the lattice thermal conductivity. Device ZT as high as 1.3 in the rhombohedral phase and 1.5 over the entire working temperature range of GeTe alloys make this material the most efficient thermoelectric to date. This work paves the way for exploring low-symmetry materials as efficient thermoelectrics. Thermoelectric materials enable a heat flow to be directly converted to a flow of charge carriers for generating electricity. The crystal structure symmetry is one of the most fundamental parameters determining the properties of a crystalline material including thermoelectrics. The common belief currently held is that high-symmetry materials are usually good for thermoelectrics, leading to great efforts having historically been focused on GeTe alloys in a high-symmetry cubic structure. Here we show a slight reduction of crystal structure symmetry of GeTe alloys from cubic to rhombohedral, enabling a rearrangement in electronic bands for more transporting channels of charge carriers and many imperfections for more blocking centers of heat-energy carriers (phonons). This leads to the discovery of rhombohedral GeTe alloys as the most efficient thermoelectric materials to date, opening new possibilities for low-symmetry thermoelectric materials. Cubic GeTe thermoelectrics have been historically focused on, while this work utilizes a slight symmetry-breaking strategy to converge the split valence bands, to reduce the lattice thermal conductivity and therefore realize a record thermoelectric performance, all enabled in GeTe in a rhombohedral structure. This not only promotes GeTe alloys as excellent materials for thermoelectric power generation below 800 K, but also expands low-symmetry materials as efficient thermoelectrics

Constitutive relationship of TC4 titanium alloy based on back propagating (BP) neural network (NN)

Using Gleeble-3800 thermal simulation testing machine, the TC4 titanium alloy was subjected to hot compression experiments under the conditions of deformation temperature of 810 – 950 °C, strain rate of 0.001 - 1s-1. The research shows that the flow stress of TC4 titanium alloy is more sensitive to the deformation temperature and strain rate during thermal deformation, and it increases with the decrease of the deformation temperature and the increase of the strain rate. Based on BP neural network, a constitutive model of TC4 titanium alloy α+β two-phase region is established. The correlation coefficient reaches 0,996, which proves that the model can predict the high temperature flow stress of TC4 titanium alloy

Constitutive relationship of 7075 aluminum alloy based on modified Zerilli-Armstrong (M - ZA) model

The Gleeble – 3500 thermal simulation testing machine was used to perform isothermal tensile test on 7075 aluminum alloy at a deformation temperature of 300 – 450 °C and a strain rate of 0,01 – 1 s-1, and the true stress-strain curve of the alloy was obtained. Based on the true stress-strain data, the modified Zerilli-Armstrong (M-ZA) model was used to construct the constitutive model of the alloy, and the fitting accuracy of the model was analyzed

Constitutive model of 3Cr23Ni8Mn3N heat-resistant steel based on back propagation (BP) neural network (NN)

The 3Cr23Ni8Mn3N heat-resistant steel was subjected to isothermal constant strain rate compression experiments using a Gleeble - 1 500D thermal simulator. The thermal deformation behavior in the range of deformation temperature 1 000 - 1 180 °C and strain rate 0,01 - 10 s-1 was studied. Based on experimental data, the stress-strain curves of 3Cr23Ni8Mn3N were established. And the constitutive relation of BP neural network (3 × 10 × 10 × 1) was constructed. The flow stress was predicted and compared by the ANN constitutive model. The correlation coefficient (R) is 0,999, and the average relative error (AARE) is 0,697 %. The results show that the ANN constitutive model has high accuracy for predicting the thermal deformation behavior of 3Cr23Ni8Mn3N. The model can provide a good reference value for thermal processing