Enhanced photoelectric properties of PbSnSe thin films via quick oxygen ion-implantation sensitization

Journal of Materials Science - Although chalcogenide materials continue to generate considerable interest due to great potentials for various optoelectronic devices, annealing for a long time in...     

Design of Deep Convolution Neural Networks for categorical signature classification of raw panchromatic satellite images

Multimedia Tools and Applications - Remote Sensing categorical signature classification has gained significant implications on spatial resolution image analysis due to differences in the...     

One step from nanofiber to functional hybrid structure: Pd doped ZnO/SnO2 heterojunction nanofibers with hexagonal ZnO columns for enhanced low-temperature hydrogen gas sensing

In this paper, a novel hybrid structure of Pd doped ZnO/SnO₂ heterojunction nanofibers with hexagonal ZnO columns was one step synthesized from electrospun precursor nanofibers. Due to the synergistic effect of hexagonal ZnO, SnO₂ and Pd, the structure exhibited excellent hydrogen (H₂) gas sensing properties. At low-temperature of 120 °C, the response (R_a/R_g) to 100 ppm H₂ gas exceeded 160, the response/recovery time was only 20 s and 6 s respectively and the limit of detection was only 0.5 ppm. Meanwhile, it also had good selectivity for H₂ gas and excellent linearity. In addition, the materials were characterized by XRD, FESEM, HRTEM, XPS, and the synthesis mechanism and gas sensing mechanism were proposed.     

Improving gelling properties of myofibrillar proteins incorporating with cellulose micro/nanofibres

The effects of cellulose microfibres (CMFs, Average size: 100 ± 5 μm) and cellulose nanofibres (CNFs, Average size: 60 ± 3 nm) on the properties of myofibrillar protein (MP) gels from duck breast meat were studied. The results demonstrated that CMFs and CNFs were mostly connected to MP by non-covalent bonds, the diffusion and cross-linking of MP molecules was promoted, and a denser and more complete gel network was formed. With the increases of CMFs and CNFs concentration (0–10%), the hardness was increased by 13.15% and 19.78% for CMFs10% and CNFs10% gels, respectively, and the elasticity was increased by 40% and 80%, respectively. At the same concentration (0–10%), the increase in gel hardness, viscoelasticity and immobilised water content was greater in the CNFs-MP group than in the CMFs-MP group. The CNFs-MP group had a tighter gel network, and CNFs had a better potential to improve the gelation performance of MP.     

Influence of Titanium Excess in a Ti–Si Reaction Mixture on the Synthesis of a Composite in a Combustion Wave

Theoretical Foundations of Chemical Engineering - On the basis of the classic concepts of the theory of solid-phase combustion, for the first time, a model with a detailed scheme of chemical...     

Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.     

Effect of Drosophila suzukii on Blueberry VOCs: Chemical Cues for a Pupal Parasitoid,Trichopria anastrephae

Journal of Chemical Ecology - Biocontrol agents such as parasitic wasps use long-range volatiles and host-associated cues from lower trophic levels to find their hosts. However, this chemical...     

传统牦牛酸奶源高产胞外多糖乳酸菌特性及发酵性能

为了研究四川藏区传统牦牛酸奶中高产胞外多糖乳酸菌特性及发酵性能，以本实验室分离出的六株高产胞外多糖乳酸菌为研究对象，以菌株生长量、耐酸性、胆盐耐受力、渗透压耐受力和细胞表面疏水性为指标进行特性研究；以发酵酸奶的凝乳时间、持水力、酸化及后酸化能力、挥发性物质及质构等为指标进行发酵性能研究。菌株的生长曲线表明，6株菌株分别在14 h（218、276、266）、16 h（271、285、231）进入对数生长稳定期，其中编号218的菌株生长量最高，OD值为2.188。耐受性实验结果表明，菌株276具有良好的耐酸能力（P<0.05）；菌株266、218、231耐胆盐的能力较强；6株菌株均具有较好的耐渗透压的能力，其中菌株285对高渗透压的耐受性较强；菌株285和218在两种有机试剂中的疏水性均显著（P<0.05）高于其他菌株，且与十六烷的结合效果较好。发酵试验结果表明，6株菌株所发酵酸奶均在6~8 h内凝固，酸乳在冷藏期间，活菌数均保持在107 CFU/mL以上，均符合发酵剂的标准，其中菌株276的最高为3.22×106 CFU/mL；菌株218发酵制得的酸奶质构特性较好，持水力和酸化能力较其余菌株均最强，分别为60.98%和83 °T；菌株276、266抗后酸化能力较好；菌株231产香性能最优，乙醛含量为24~26 μg/mL，双乙酰含量为1.58~3.73 μg/mL，能够明显改善酸奶的风味。通过综合比较6株高产胞外多糖乳酸菌特性及发酵性能，菌株218为一株性能良好、具有良好稳定性的菌株，可作为乳酸菌发酵剂且具有一定的应用潜力。研究为利用四川藏区传统牦牛酸奶中分离出的高产胞外多糖乳酸菌在发酵乳制品的应用提供理论依据。     

Influence of Continuous Wave Interference on the Efficiency of the Non-Threshold Search Procedure for a Noise-Like Signal by Delay Time with Transition to the Frequency Domain

Journal of Communications Technology and Electronics - A statistical study of the effectiveness of the non-threshold search procedure for a noise-like phase-shift keyed signal by the delay time is...