Headspace fingerprinting approach to identify the major pathway influencing volatile patterns of vinasse-cured duck processed by high pressure,as well as its impact on physicochemical and sensory attributes

As a decisive attribute, flavour could be influenced by HP treatments through multiple physical and chemical pathways within the high pressure (HP)-assisted meat curing process. This investigation aimed to identify the major pathway influencing volatile flavour patterns of two representative vinasse-cured duck (VCD) products with HP treatments (150–300 MPa/15 min), including wet and dry types, by employing headspace fingerprinting as an untargeted approach. Results suggested that HP treatments greatly lowered moisture contents and increased Warner-Bratzler shear force and thiobarbituric acid reactive substances of the cured samples. According to multivariate models, the volatile flavour patterns of the HP-processed VCD could be clearly separated from the unprocessed samples, but the VCD pressurised at different intensities represented similar volatile fingerprinting, which was validated by e-nose analysis. The discriminant analysis (OPLS-DA) model outlined vinasse-derived ethanol, acetic acid, 3-methyl-1-butanol, 2-methyl-1-butanol, phenethyl alcohol and 2-methyl-3-octanone as the major discriminant aromas across the unpressurised and pressurised samples.     

Tailoring the electron-blocking layer by addition of YSZ to Ba-containing anode for improvement of ceria-based solid oxide fuel cell

The in-situ fabrication of an electron-blocking layer between the Ba-containing anode and the ceria-based electrolyte is an effective approach in suppressing the internal electronic leakage in ceria-based solid oxide fuel cell (SOFC). To improve the thickness of the electron-blocking layer and to research the effect of the layer thickness on the improvement of SOFC, a Ba-containing compound (0.6NiO-0.4BaZr_0.1Ce_0.7Y_0.2O_3-δ) modified by Y stabilized zirconia (YSZ) was employed as a composite anode in this research. SEM analyses demonstrated that the thickness of the interlayer can be simply controlled by regulating the proportion of YSZ at anode. The in-situ formed interlayer in the cell with the anode modified by 20?mol% YSZ possesses a thickness of 0.9?µm which is more suitable for the cell achieving an enhanced performance.     

丙烷脱沥青装置调合前沥青的综合利用

在丙烷脱沥青装置加热炉试烧装置自产调合前沥青,通过观察油枪雾化沥青的能力、火嘴的燃烧情况以及燃烧后的烟尘状况,筛选出适合沥青燃烧的油枪,最终实现调合前沥青直接做加热炉燃料、减少重油出厂量的目的。     

Structural,morphological, dielectric and magnetic properties of Zn-Zr co-doping yttrium iron garnet

In this study, yttrium iron garnet co-doped with Zn and Zr atoms with a chemical formula Y₃Zn_xZr_xFe_(5−2x)O₁₂ (x = 0.0-0.3) has been successfully prepared by the solid-state reaction method. The effects of doping concentration on the microstructure, crystal structure, magnetic properties, and dielectric properties of Y₃Zn_xZr_xFe_(5−2x)O₁₂ were investigated. The microstructure analysis indicates that co-doping of YIG with Zn and Zr can effectively reduce the grain size of the ceramic. The crystal structure results reveal that the doping concentration of Zn–Zr has substantial influence on the lattice parameters of YIG, such as, increases the lattice constant, crystal cell size, and interplanar spacing. However, the second phase of ZrO₂ appears once x ≥ 0.15. Additionally, the dielectric properties of YIG ferrite can be regulated using this Zn–Zr co-doping method. Zn–Zr co-doping can improve the dielectric stability and reduce the dielectric loss at high temperature. The magnetization measurement shows that the saturation magnetization is stabilized at x < 0.15, and the magnetic loss is decreased with the increase in the doping concentration. Overall, the findings show that the ceramic with x = 0.1 exhibits better properties included high saturation magnetization (24.607 emu/g), low magnetic loss (0.0025 @ 1 MHz), and relatively low dielectric loss (496 @ 400°C).     

Evaluation model of green supply chain cooperation credit based on BP neural network

Neural Computing and Applications - More and more enterprises hope to achieve cooperation and win–win. However, many companies often have problems such as insufficient partner credit, which...     

Predicting visual similarity between colour palettes

This work is concerned with the prediction of visual colour difference between pairs of palettes. In this study, the palettes contained five colours arranged in a horizontal row. A total of 95 pairs of palettes were rated for visual difference by 20 participants. The colour difference between the palettes was predicted using two algorithms, each based on one of six colour-difference formulae. The best performance (r² = 0.86 and STRESS = 16.9) was obtained using the minimum colour-difference algorithm (MICDM) using the CIEDE2000 equation with a lightness weighing of 2. There was some evidence that the order (or arrangement) of the colours in the palettes was a factor affecting the visual colour differences although the MICDM algorithm does not take order into account. Application of this algorithm is intended for digital design workflows where colour palettes are generated automatically using machine learning and for comparing palettes obtained from psychophysical studies to explore, for example, the effect of culture, age, or gender on colour associations.