Koo M.,Dong - Eui University |
Bae J.-S.,Korea Basic Science Institute |
Shim S.E.,Dong - Eui University |
Kim D.,Pohang University of Science and Technology |
And 5 more authors.
Colloid and Polymer Science | Year: 2011
Polyimide-graphene composites (PIG) were prepared with variable amounts of graphene, and their thermal properties were analyzed in films on substrates or sheet states. The thermal conductivities of PIG composite sheets gradually moved upwards with increase of graphene loading. Coefficient of thermal expansion of composite sheet was higher in out-of-plane mode than in-plane mode. The residual stress of a composite film was monotonously changed in accordance with the variation of temperature and lowered with increase of graphene. In addition, the residual stress of a composite film reached to the initial stress value during cooling process after heating. The stress profiles on further heating and cooling runs closely followed the stress profile during the first cooing run. © 2011 Springer-Verlag.
Park J.H.,Kyungpook National University |
Park S.M.,Korea Dyeing Technology Center |
Kim Y.H.,Kyungpook National University |
Oh W.,Dong - Eui University |
And 4 more authors.
Journal of Composite Materials | Year: 2013
Zein is a hydrophobic protein produced from maize and has great potential in a number of industrial applications such as food, food coating and food packaging. The objectives of this study are to determine the effects of montmorillonite on the wettability and microstructure properties of zein/montmorillonite nanocomposite nanofiber mats fabricated by the electrospinning technique in ethyl alcohol aqueous solution. The zein/montmorillonite nanofiber mats were characterized by field-emission scanning electron microscopy, transmission electron microscopy, X-ray diffraction, differential scanning calorimetry, thermogravimetric analysis and contact angle measurements. This study shows that the introduction of montmorillonite resulted in the improvement of the thermal stability and hydrophilicity for the zein matrix. X-ray diffraction patterns and transmission electron microscopy micrographs suggest the coexistence of intercalated montmorillonite layers over the examined montmorillonite contents. Since montmorillonite is a hydrophilic clay, its addition can be used not only to produce nanomaterials with the already known improved properties but also to enhance the hydrophilicity of material. © The Author(s) 2012 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.
Jung W.Y.,Pukyong National University |
Park S.S.,Pukyong National University |
Lee G.D.,Pukyong National University |
Lee M.S.,Korea Institute of Industrial Technology |
And 2 more authors.
Research Journal of Chemistry and Environment | Year: 2010
Nanosized TiO2 and yttrium ions doped TiO2 particles have been prepared using low temperature combustion method. The physical properties were investigated and we have also examined the activity of TiO 2 particles as photocatalysts for the decomposition of methylene blue. From XRD results, the major phase of all TiO2 particles was an anotase structure regardless of doping of yttrium ions. It can be seen that no peak from yttrium oxide were observed and a rutile peak was observed above 700°C. The photocatalytic activity for the decomposition of methylene blue is proportional to the intensity of the PL peaks of the yttrium doped TiO 2 particles. The doping of 1.0 mole% yttrium oxide on the TiO 2 enhanced the photocatalytic activity and showed the higher activity than P-25 used as a commercial catalyst. In addition, the titania particles calcined at 600°C showed the highest photocatalytic activity.
Cha I.,Changwon National University |
Lee S.,Changwon National University |
Lee J.D.,Korea Institute of Industrial Technology |
Lee G.W.,Yoo Sung Co. |
Seo Y.,Changwon National University
Environmental Science and Technology | Year: 2010
This study aims to examine the thermodynamic feasibility of separating sulfur hexafluoride (SF6), which is widely used in various industrial fields and is one of the most potent greenhouse gases, from gas mixtures using gas hydrate formation. The key process variables of hydrate phase equilibria, pressure-composition diagram, formation kinetics, and structure identification of the mixed gas hydrates, were closely investigated to verify the overall concept of this hydrate-based SF6 separation process. The three-phase equilibria of hydrate (H), liquid water (LW), and vapor (V) for the binary SF6 + water mixture and for the ternary N2 + SF6 + water mixtures with various SF6 vapor compositions (10, 30, 50, and 70%) were experimentally measured to determine the stability regions and formation conditions of pure and mixed hydrates. The pressure-composition diagram at two different temperatures of 276.15 and 281.15 K was obtained to investigate the actual SF6 separation efficiency. The vapor phase composition change was monitored during gas hydrate formation to confirm the formation pattern and time needed to reach a state of equilibrium. Furthermore the structure of the mixed N2 + SF6 hydrate was confirmed to be structure II via Raman spectroscopy. Through close examination of the overall experimental results, it was clearly verified that highly concentrated SF6 can be separated from gas mixtures at mild temperatures and low pressure conditions. © 2010 American Chemical Society.
Meher S.K.,Indian Statistical Institute |
Behera S.K.,Institute of Chemical Technology |
Kim M.C.,Yoo Sung Co. |
Park H.-S.,The Clean Tech Center
Applied Artificial Intelligence | Year: 2015
Artificial neural networks (ANN)-based multiple decision expert systems (MDES) were developed for assessing the performance of a boiler system. Different configurations of ANN were used with different decision combination methods, including a neural combiner, to propose the model. The model was developed using the plant data collected over a period of five months to predict steam temperature, pressure, and mass flow rate, using feed water pressure, feed water temperature, conveyor speed, and incinerator exit temperature as the input parameters. The predictive capability of the model is evaluated in terms of both correlation coefficient (R) and mean absolute percentage error (MAPE). The results observed from this work demonstrate that neural combiner and ANN-based MDES can efficiently predict the data on steam properties consistently, and that the model can serve as an efficient tool for monitoring boiler behavior under real-time conditions. Superiority of the proposed model over others under various scenarios is also demonstrated. © 2015 Taylor & Francis Group, LLC.