scholarly journals Effect of Argon CarriEffect of Argon Carrier Gas Flux on TiO2 Nanostructures

10.5772/61639 ◽  
2015 ◽  
Author(s):  
Reza Sabet Dariani ◽  
Zohreh Nafari Qaleh
Keyword(s):  
Gas Flux ◽  
Carrier Gas ◽  
Tio2 Nanostructures

The Shape Control of ZnO Based Nanostructures

2006 ◽  
Vol 6 (11) ◽  
pp. 3628-3632
Author(s):  
M. N. Jung ◽  
S. Y. Ha ◽  
H. S. Kim ◽  
H. J. Ko ◽  
H. Ko ◽  
...  
Keyword(s):  
Growth Temperature ◽  
Shape Control ◽  
Deep Level ◽  
Growth Parameters ◽  
Stoichiometric Composition ◽  
Growth Conditions ◽  
Second Phase ◽  
Gas Flux ◽  
Carrier Gas ◽  
Si Substrates

Tetrapod-shape ZnO nanostructures are formed on Si substrates by vapor phase transportation method. The effects of two important growth parameters, growth temperature and VI/II ratio, are investigated. The growth temperature is varied in the range from 600 °C to 900 °C to control the vapor pressure of group II-element and the formation process of nanostructures. VI/II ratio was changed by adjusting the flux of carrier gas which affects indirectly the supplying rate of group VI-element. From the scanning electron microscopy (SEM), systematic variation of shape including cluster, rod, wire and tetrapod was observed. ZnO tetrapods, formed at 800 °C under the carrier gas flux of 0.5 cc/mm2 min, show considerably uniform shape with 100 nm thick and 1 ∼ 1.5 μm long legs. Also stoichiometric composition (O/Zn ∼ 1) was observed without any second phase structures. While, the decrease of growth temperature and the increase of carrier gas flux, results in the irregular shaped nanostructures with non-stoichiometric composition. The excellent luminescence properties, strong excitonic UV emission at 3.25 eV without deep level emission, indicate that the high crystalline quality tetrapod structures can be formed at the optimized growth conditions.

scholarly journals On the use of plume models to estimate the flux in volcanic gas plumes

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Julia Woitischek ◽  
Nicola Mingotti ◽  
Marie Edmonds ◽  
Andrew W. Woods
Keyword(s):  
Electrochemical Sensors ◽  
Measurement Techniques ◽  
Flux Measurement ◽  
Hydrothermal Systems ◽  
Mount Etna ◽  
Volcanic Gas ◽  
Gas Flux ◽  
Convective Structures ◽  
Video Images ◽  
Villarrica Volcano

AbstractMany of the standard volcanic gas flux measurement approaches involve absorption spectroscopy in combination with wind speed measurements. Here, we present a new method using video images of volcanic plumes to measure the speed of convective structures combined with classical plume theory to estimate volcanic fluxes. We apply the method to a nearly vertical gas plume at Villarrica Volcano, Chile, and a wind-blown gas plume at Mount Etna, Italy. Our estimates of the gas fluxes are consistent in magnitude with previous reported fluxes obtained by spectroscopy and electrochemical sensors for these volcanoes. Compared to conventional gas flux measurement techniques focusing on SO2, our new model also has the potential to be used for sulfur-poor plumes in hydrothermal systems because it estimates the H2O flux.

scholarly journals Experimental Investigation and Prediction on Pressure Drop during Flow Boiling in Horizontal Microchannels

Micromachines ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 510
Author(s):  
Yan Huang ◽  
Bifen Shu ◽  
Shengnan Zhou ◽  
Qi Shi
Keyword(s):  
Pressure Drop ◽  
Flow Model ◽  
Two Phase Flow ◽  
Flow Boiling ◽  
Separated Flow ◽  
Heat Fluxes ◽  
Phase Flow ◽  
Vapor Quality ◽  
Two Phase ◽  
Gas Flux

In this paper, two-phase pressure drop data were obtained for boiling in horizontal rectangular microchannels with a hydraulic diameter of 0.55 mm for R-134a over mass velocities from 790 to 1122, heat fluxes from 0 to 31.08 kW/m2 and vapor qualities from 0 to 0.25. The experimental results show that the Chisholm parameter in the separated flow model relies heavily on the vapor quality, especially in the low vapor quality region (from 0 to 0.1), where the two-phase flow pattern is mainly bubbly and slug flow. Then, the measured pressure drop data are compared with those from six separated flow models. Based on the comparison result, the superficial gas flux is introduced in this paper to consider the comprehensive influence of mass velocity and vapor quality on two-phase flow pressure drop, and a new equation for the Chisholm parameter in the separated flow model is proposed as a function of the superficial gas flux . The mean absolute error (MAE ) of the new flow correlation is 16.82%, which is significantly lower than the other correlations. Moreover, the applicability of the new expression has been verified by the experimental data in other literatures.

Superheated steam as carrier gas and the sole heat source to enhance biomass torrefaction

2021 ◽  
Vol 331 ◽  
pp. 124955
Author(s):  
Dongdong Zhang ◽  
Xuejiao Chen ◽  
Zhiyong Qi ◽  
Hong Wang ◽  
Rui Yang ◽  
...  
Keyword(s):  
Heat Source ◽  
Superheated Steam ◽  
Carrier Gas

scholarly journals Feasibility of Machine Learning Algorithms for Predicting the Deformation of Anodic Titanium Films by Modulating Anodization Processes

Materials ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1089
Author(s):  
Sung-Hee Kim ◽  
Chanyoung Jeong
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Multiclass Classification ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Smart Manufacturing ◽  
Gradient Boosting ◽  
Experimental Conditions ◽  
Learning Techniques ◽  
Tio2 Nanostructures

This study aims to demonstrate the feasibility of applying eight machine learning algorithms to predict the classification of the surface characteristics of titanium oxide (TiO2) nanostructures with different anodization processes. We produced a total of 100 samples, and we assessed changes in TiO2 nanostructures’ thicknesses by performing anodization. We successfully grew TiO2 films with different thicknesses by one-step anodization in ethylene glycol containing NH4F and H2O at applied voltage differences ranging from 10 V to 100 V at various anodization durations. We found that the thicknesses of TiO2 nanostructures are dependent on anodization voltages under time differences. Therefore, we tested the feasibility of applying machine learning algorithms to predict the deformation of TiO2. As the characteristics of TiO2 changed based on the different experimental conditions, we classified its surface pore structure into two categories and four groups. For the classification based on granularity, we assessed layer creation, roughness, pore creation, and pore height. We applied eight machine learning techniques to predict classification for binary and multiclass classification. For binary classification, random forest and gradient boosting algorithm had relatively high performance. However, all eight algorithms had scores higher than 0.93, which signifies high prediction on estimating the presence of pore. In contrast, decision tree and three ensemble methods had a relatively higher performance for multiclass classification, with an accuracy rate greater than 0.79. The weakest algorithm used was k-nearest neighbors for both binary and multiclass classifications. We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.

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