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Synergistic effect of nickel nanoparticles and carbon nanotubes buckypaper for enhancement of microwave shielding properties
(2020-01-01)
Sukgorn, Nuttaya
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Yordsri, Visittapong
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Thanachayanon, Chanchana
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Horprathum, Mati
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Chudpooti, Nonchanutt
Carbon nanotubes (CNTs) are considered as the most promising materials to solve the electromagnetic interference (EMI) issue. Various forms of CNTs including CNTs/polymer composites, metal nanoparticles-decorated CNTs and freestanding CNT buckypapers (CNT BPs) have been proposed to enhance shielding effectiveness. In this study, the synergistic effect of nickel nanoparticles (NPs) and relatively short CNTs for the enhancement of microwave shielding properties was investigated. CNT BPs were prepared by vacuum filtration of well-dispersed multi-walled CNTs and subsequently nickel was decorated on the CNT BPs (Ni/CNT) by pulsed DC sputtering technique with different deposition times of 0, 5, 10 and 15 min (hereinafter referred to as CNi0, CNi05, CNi10 and CNi15, respectively). The diameter of Ni/CNT increased from 8.74±0.53 to 72.5±3.2 nm and the conductivity improved from 9.57±0.87 to 12.57±0.59 S/cm when the nickel deposition time was 15 min. Nickel NPs were the mixed phases of nickel and nickel oxide with a dominant nickel phase. The shielding effectiveness at the frequency of 9.5 GHz achieved to-34.1 dB for CNi15. The enhancement of shielding effectiveness of CNi15 is attributed to the synergistic effect of CNTs and nickel NPs on wave dissipation.
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A Hybrid of Shallow and Deep Learning for Odor Classification Based on Adaptive Boosting
(2019-11-01)
Grodniyomchai, Boonyawee
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Chalapat, Khattiya
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Jitkajornwanich, Kulsawasd
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Jaiyen, Saichon
An electronic nose is very useful for identifying an odor that is harmful to humans. To get the most accurate odor predictions from an electronic nose, we combined the models of traditional machine learning and deep learning, including deep neural network (DNN), support vector machine (SVM) and decision tree, to make a new hybrid model that adopts the AdaBoost algorithm to adjust the weights of weak classifiers to build a strong classifier using odor data. Experimental results from our model were compared with other models, including a single deep neural network, an ensemble of SVM models and an ensemble of decision trees. Our model achieved an averaged accuracy of 99.58%, which is better than other models, and the standard deviation, 0.67%, is also less than other models.
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A deep learning model for odor classification using deep neural network
(2019-07-01)
Grodniyomchai, Boonyawee
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Chalapat, Khattiya
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Jitkajornwanich, Kulsawasd
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Jaiyen, Saichon
The odor is an environment that surrounds us. However, to identify the odor by using the human nose in order to prove the odor is very dangerous. Therefore, the artificial intelligent (AI) system should be built based on machine learning in order to achieve more accurate results. This research adopts the Deep Neural Network (DNN) model to identify some types of odor including odorless, beer odor, whisky odor, and wine odor. Each contains 60 instances that are obtained from seven sensors of the electronic nose. The experiments are conducted, and the results are compared to the comparative machine learning methods including Multilayer Perceptron (MLP), Decision Tree and Naïve Bayes (NB). From the experimental results, it can signify that the proposed deep learning model can achieve the best average accuracy.
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Radio-frequency characterization of multi-walled carbon nanotube/poly-lactic acid composites
(2017-01-01)
Sukgorn, Nuttaya
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Siraleartmukul, Krisana
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Yordsri, Visittapong
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Chudpooti, Nonchanutt
;
Chaimool, Sarawuth
Nowadays radio and microwave frequencies are widely used in wireless broadcastings and communications. But these waves can cause electromagnetic interferences in some electronic devices, including the equipment used in hospitals. The problems of the electromagnetic interference can be solved by using the materials that can reflect and/or absorb radio-frequency (RF) and microwaves. The shielding effectiveness (SE) of a material depends on its conductivity and the electrical permittivity. Recently, carbon nanotubes (CNTs) have been proposed as promising materials for shielding applications owing to its flexibility, durability, lightweight and exceptional electrical conductivity compared to conventional metal. In this work, the RF properties of multi-walled carbon nanotube (MWCNT) composites were investigated. Poly-lactic acid (PLA), a natural bio-degradable material, is used as the polymer matrix. The composites with different MWCNT concentrations (0 to 0.4 wt%) were prepared and molded into thin rectangular samples (5.6 cm x 6.3cm). The surfaces of the composites were morphologically characterized by a scanning electron microscope. The electrical permittivity of the samples was measured by using a vector network analyzer and a microstrip resonator within a range of 1-11 GHz. The effects of MWCNT concentration on electrical permittivity will be discussed.
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Humidity sensor using carboxymethyl cellulose hydrogel membrane
(2016-09-06)
Pinming, Chinathun
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Sukgorn, Nuttaya
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Suhatcho, Tanyarat
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Saetang, Benjaporn
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Kerdkhong, Phaophoom
Sensitivity and reliability of humidity sensors depend on the design of electrical components and the characteristics of materials used as absorbing layer. Here, an experiment is conducted to study the fabrication of a carboxymethyl cellulose (CMC) hydrogel membrane and its function as an absorbing layer in a resistive-based humidity sensor. The hydrogel membrane is prepared by coating the electrodes with 2 wt% CMC solution (in 3≦1 water:ethanol mixture). Next, the CMC membrane is cross-linked in epichlorohydrin (ECH). Finally, the sensor is tested by measuring the resistance of the CMC hydrogel membrane at various relative humidity: RH = 53 %, 75 %, 84 % and 93 %. The measurement results show that the resistive-based humidity sensor made from the CMC hydrogel can function within this high humidity range.
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Nanoporous anodic aluminum oxide (AAO) thin film fabrication with low-grade aluminium
(2016-01-01)
Sumtong, Peerawith
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Eiad-Ua, Apiluck
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Chalapat, Khattiya
Anodic aluminum oxide (AAO) is well known for its nanoscopic structures and its applications in microfluidics, sensors and nanoelectronics. The pore density, the pore diameter, and the interpore distance of an AAO substrate can be controlled by varying anodization process conditions. In this research, the self-organized two-step anodization is carried out with a low-grade (Al6061) aluminium substrate using a 40V voltage at the temperature of 2 to 5 °C. Three experiments are done with the anodization time of 24 hours, 48 hours and 72 hours. The structural features of AAO are characterized by a field emission electron microscope (FE-SEM). The data from FE-SEM show that the average pore diameter increases with the anodization time, and that the Al6061 aluminium substrate can be used to fabricate a nanoporous AAO film with an average pore diameter smaller than 17 nanometers.

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