Effect of Metal Catalysts on Synthesis of Carbon Nanomaterials by Alcohol Catalytic Chemical Vapor Deposition

Carbon nanomaterials (CNMs) were synthesized by alcohol catalytic chemical vapor deposition (CVD) at atmospheric pressure using different metal catalysts (Ni, Co and Fe) at growth temperature of 700oC. Ni and Fe exhibited as active catalysts for multi-walled carbon nanotubes (MWNTs) growth, while Co acted as an active catalyst for bamboo-like MWNTs and carbon nanofibers (CNFs). The CNMs synthesized from Ni catalyst showed the highest crystallinity with a small amount of byproducts. These results imply that metal catalyst is key parameter to the structure, morphology and crystallinity of CNMs. The different effect of metal catalyst on growth of CNMs can be described in term of the difference in the change in Gibbs free energy for metal carbide formation

Influence of Ti and Zn Dopants on Structural Properties and Electrochromic Performance of Sol-Gel Derived WO3 Thin Films

AbstractThe influence of different transition metals doping (Ti and Zn) on structural properties and electrochromic adjustment and performance of sol-gel derived WO3 thin films was conducted and investigated. Ti-doped WO3 and Zn-doped WO3 thin films were deposited onto F-doped tin oxide (FTO) substrates using spin coating technique. Tungsten powder, Titanium butoxide and zinc acetate dehydrate were used as starting precursors. The as-deposited films were annealed at 500°C for 2h. The effect of Ti and Zn doping on structural, surface morphologies, optical properties of the films were examined using X-ray diffractometer, scanning electron microscope and UV-VIS spectrophotometer. The XRD analyses suggest that the crystalline of WO3 can be identified as a monoclinic WO3 structure. XRD results additionally indicate the existence of ZnWO4 structure in the Zn-doped films, which is clearly observed by SEM. The optical measurement of all films indicate good transparency in the visible region and near infrared region with more than 60% in transmittance. In addition, It was found that the electrochromic performance of WO3 can be enhanced by small doping concentration of titanium due to structural modification of the films caused by close ionic radius of Ti to W rather than that of Zn

Development of Paddy Rice Seed Classification Process using Machine Learning Techniques for Automatic Grading Machine

To increase productivity in agricultural production, speed, and accuracy is the key requirement for long-term economic growth, competitiveness, and sustainability. Traditional manual paddy rice seed classification operations are costly and unreliable because human decisions in identifying objects and issues are inconsistent, subjective, and slow. Machine vision technology provides an alternative for automated processes, which are nondestructive, cost-effective, fast, and accurate techniques. In this work, we presented a study that utilized machine vision technology to classify 14 Oryza sativa rice varieties. Each cultivar used over 3,500 seed samples, a total of close to 50,000 seeds. There were three main processes, including preprocessing, feature extraction, and rice variety classification. We started the first process using a seed orientation method that aligned the seed bodies in the same direction. Next, a quality screening method was applied to detect unusual physical seed samples. Their physical information including shape, color, and texture properties was extracted to be data representations for the classification. Four methods (LR, LDA, k-NN, and SVM) of statistical machine learning techniques and five pretrained models (VGG16, VGG19, Xception, InceptionV3, and InceptionResNetV2) on deep learning techniques were applied for the classification performance comparison. In our study, the rice dataset were classified in both subgroups and collective groups for studying ambiguous relationships among them. The best accuracy was obtained from the SVM method at 90.61%, 82.71%, and 83.9% in subgroups 1 and 2 and the collective group, respectively, while the best accuracy on the deep learning techniques was at 95.15% from InceptionResNetV2 models. In addition, we showed an improvement in the overall performance of the system in terms of data qualities involving seed orientation and quality screening. Our study demonstrated a practical design of rice classification using machine vision technology

High-Quality Large-Magnification Polymer Lens from Needle Moving Technique and Thermal Assisted Moldless Fabrication Process.

The need of mobile microscope is escalating as well as the demand of high quality optical components in low price. We report here a novel needle moving technique to fabricate milli-size lens together with thermal assist moldless method. Our proposed protocol is able to create a high tensile strength structure of the lens and its base which is beneficial for exploiting in convertinga smart phone to be a digital microscope. We observe that no bubble trapped in a lens when this technique is performed which can overcome a challenge problem found in a typical dropping technique. We demonstrate the symmetry, smoothness and micron-scale resolution of the fabricated structure. This proposed technique is promising to serve as high quality control mass production without any expensive equipment required

An infrared photo of lens formation.

<p>(a) Heat transfer from the hot surface into the polymer during 8 seconds of the lens formation, obtained by heating the surface at 180°C, and 20 μl of polymer droplet. Numerical simulation of heat transfer using shows temperature distribution at (b) 0, (c) 2, and (d) 8 seconds during lens formation.</p

A relationship of lens focal length and fabricated temperature.

<p>Measured focal length depends on the surface temperature and the volume of the polymer. Upon temperature increasing, the focal length decreases.</p