Nanoparticle-assembled ZnO micro-octahedrons were synthesized by a facile homogeneous precipitation method. The ZnO micro-octahedrons are hexagonal wurtzite with high crystallinity. Abundant structure defects were confirmed on ZnO surface by photoluminescence. Gas sensors based on the ZnO micro-octahedrons exhibited high response, selectivity and stability to 1–1000 ppm formaldehyde at 400 °C. Especially, even 1 ppm formaldehyde could be detected with high response (S = 22.7). It is of interest to point out that formaldehyde could be easily distinguished from ethanol or acetaldehyde with a selectivity of about 3. The high formaldehyde response is mainly attributed to the synergistic effect of high contents of electron donor defects (Zni and VO) and highly active oxygen species (O2−) on the ZnO surface. 相似文献
Ordered mesoporous SnO2 and mesoporous Pd/SnO2 have been successfully synthesized via nanocasting method using the hexagonal mesoporous SBA-15 as template. Two different procedures, impregnation technique and direct synthesis, were utilized for the doping of Pd in the mesoporous SnO2. The results of small angle X-ray diffraction (SAXD), nitrogen adsorption–desorption and transmission electron microscopy (TEM) demonstrate that the SnO2 and Pd/SnO2 display ordered mesoporous structures and high surface areas. Wide angle X-ray diffraction (WAXD) and X-ray photoelectron spectroscopy (XPS) reveal tetragonal structure of SnO2 and the existence of Pd element. The sensing properties of mesoporous SnO2 and mesoporous Pd/SnO2 for H2 were detected. The sensor utilizing mesoporous Pd/SnO2 via direct synthesis method exhibits excellent response and recovery behavior and much higher sensitivity to H2, compared to those using mesoporous SnO2 and mesoporous Pd/SnO2 via impregnation technique. It is believed that its high gas sensing performance is derived from the large surface area, high activity and well dispersion of Pd additive, as well as high porosity, which lead to highly effective surface interaction between the target gas molecules and the surface active sites. 相似文献
Freeform optics has become a practical solution to solving number of problems in modern optical design. In this paper, we
proposed a fabrication method using the combination of ultraprecision diamond machining and microinjection molding to achieve
high volume and low cost freeform microlens manufacturing. The freeform microlens array discussed in this research is capable
of redistributing a collimated light into a pre determined, in this case, a uniform pattern. The optical design, slow tool
servo diamond machining, microinjection molding process and optical measurement were discussed. The simple optical design
provided a platform for freeform microlens calculation. Slow tool servo diamond broaching was selected to fabricate the mold
insert. After the mold insert was fabricated, microinjection molding machine was utilized to replicate the optical geometry
into plastic substrates. The freeform microlens array that was fabricated in this research could achieve light re-distribution
at the target with approximately 80% uniformity. The research conducted in this paper can be readily implemented in optical
industry. 相似文献
A nickel micromirror array was designed and successfully fabricated using a thick photoresist as a sacrificial layer and as a mold for nickel electroplating. It was composed of two address electrodes, two support posts and a nickel mirror plate. The mirror plate, which is supported by two nickel posts, is overhung about 10 μm from the silicon substrate. The nickel mirror plate is actuated by an electrostatic force generated by electrostatic potential difference applied between the mirror plate and the address electrode. Optimized fabrication processes have been developed to reduce residual stress in mirror plate and prevent contact between the mirror plate and the substrate, which ensure a reasonable flat and smooth micromirror for operation at low actuation voltage.
The main theme of this paper is to present a novel evolution, the genetic regulatory network-based symbiotic evolution (GRNSE), to improve the convergent speed and solution accuracy of genetic algorithms. The proposed GRNSE utilizes genetic regulatory network (GRN) reinforcement learning to improve the diversity and symbiotic evolution (SE) initialization to achieve the parallelism. In particular, GRN-based learning increases the global rate by regulating members of genes in symbiotic evolution. To compare the efficiency of the proposed method, we adopt 41 benchmarks that contain many nonlinear and complex optimal problems. The influences of dimension, individual population size, and gene population size are examined. A new control parameter, the population rate is introduced to initiate the ratio between the gene and chromosome. Finally, all the studies of there 41 benchmarks demonstrate that from the statistic point of view, GRNSE give a better convergence speed and a more accurate optimal solution than GA and SE. 相似文献
This paper is devoted to investigating inventory control problems under nonstationary and uncertain demand. A belief-rule-based inventory control (BRB-IC) method is developed, which can be applied in situations where demand and demand-forecast-error (DFE) do not follow certain stochastic distribution and forecasting demand is given in single-point or interval styles. The method can assist decision-making through a belief-rule structure that can be constructed, initialized and adjusted using both manager’s knowledge and operational data. An extended optimal base stock (EOBS) policy is proved for initializing the belief-rule-base (BRB), and a BRB-IC inference approach with interval inputs is proposed. A numerical example and a case study are examined to demonstrate potential applications of the BRB-IC method. These studies show that the belief-rule-based expert system is flexible and valid for inventory control. The case study also shows that the BRB-IC method can compensate DFE by training BRB using historical demand data for generating reliable ordering policy. 相似文献
Recently, research of financial distress prediction has become increasingly urgent. However, existing static models for financial distress prediction are not able to adapt to the situation that the sample data flows constantly with the lapse of time. Financial distress prediction with static models does not meet the demand of the dynamic nature of business operations. This article explores the theoretical and empirical research of dynamic modeling on financial distress prediction with longitudinal data streams from the view of individual enterprise. Based on enterprise’s longitudinal data streams, dynamic financial distress prediction model is constructed by integrating financial indicator selection by using sequential floating forward selection method, dynamic evaluation of enterprise’s financial situation by using principal component analysis at each longitudinal time point, and dynamic prediction of financial distress by using back-propagation neural network optimized by genetic algorithm. This model’s ex-ante prediction efficiently combines its ex-post evaluation. In empirical study, three listed companies’ half-year longitudinal data streams are used as the sample set. Results of dynamic financial distress prediction show that the longitudinal and dynamic model of enterprise’s financial distress prediction is more effective and feasible than static model. 相似文献