• Journal of Adhesion and Interface
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• v.8 no.2
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• pp.15-21
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• 2007

To prepare a natural rubber-based pressure-sensitive adhesive (PSA) for protection film of opto- functionalized sheet, natural rubber (NR) was blended with a DCPD type tackifier and three types of aliphatic hydrocarbon resins, respectively. Also, to supply low cohesion strength of NR, in the fixed ratio of tackifier, synthetic rubber, styrene-isoprene-styrene (SIS) block copolymer was blended with NR as a function of SIS contents. PSA performance of prepared PSAs was evaluated using probe tack and peel strength. Probe tack of NR/tackifier blends was increased with increasing tackifier contents, and showed maximum peak. In addition, probe tack of NR/tackifier blends slightly increased with increasing softening point of aliphatic hydrocarbon resins. Their peel strength increased up to 50 wt% of tackifier contents, but in the over contents of tackifier, they showed stick-slip failure mode. Finally, probe tack of NR/SIS/tackifier blends showed the maximum values at 20~40 wt% of tackifier contents, but at 20 wt% of tackifier contents, they showed fibrillation. For this reason, peel strength showed maximum values at 40 wt% of tackifier contents.

• Elastomers and Composites
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• v.38 no.3
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• pp.235-242
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• 2003

A vulcanization reaction characteristics of an isoprene rubber (IR)-modified natural rubber/carbon black (NR/CB) composite was studied using in-situ electrical property measuring technique. Since the electrical conductivity of the sample composite would be changed continuously during the vulcanization reaction by rearranging of the carbon black particles within the sample, volume resistivity (${\rho}$) might be obtained as a function or reaction time. A stabilization time ($t_i$), maximum reaction speed time ($t_p$), and volume resistivity at that time(${\rho}_p$) were defined from the data for the Arrhenius analysis. Volume resistivity ${\rho}$ showed a comparatively high value of ${\sim}10^8$ order before the reaction started, and dramatically decreased to be stabilized within $1{\sim}2$ minutes as soon as the reaction started. As the more time elapsed, thereafter, ${\rho}$ decreased monotonously to a certain constant value through a peak, ${\rho}_p$ at time $t_p$, which was considered as the maximum reaction rate. As a result, while $t_i$ values were comparatively constant as $1{\sim}2$ minutes, $t_p$ values showed to become shorter and shorter as the reaction temperature.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.48-59
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• 2023

Ground-level ozone affects human health and plant growth. Ozone is produced by chemical reactions between oxides of nitrogen (NOx) and volatile organic compounds (VOCs) from anthropogenic and biogenic sources. In this study, two different land cover and emission factor datasets were input to the MEGAN v2.1 emission model to examine how these parameters contribute to the biogenic emissions and ozone production. Four input sensitivity scenarios (A, B, C and D) were generated from land cover and vegetation emission factors combination. The effects of BVOCs emissions by scenario were also investigated. From air quality modeling result using CAMx, maximum 1 hour ozone concentrations were estimated 62 ppb, 60 ppb, 68 ppb, 65 ppb, 55 ppb for scenarios A, B, C, D and E, respectively. For maximum 8 hour ozone concentration, 57 ppb, 56 ppb, 63 ppb, 60 ppb, and 53 ppb were estimated by scenario. The minimum difference by land cover was up to 25 ppb and by emission factor that was up to 35 ppb. From the modeling performance evaluation using ground ozone measurement over the six regions (East Seoul, West Seoul, Incheon, Namyangju, Wonju, and Daegu), the model performed well in terms of the correlation coefficient (0.6 to 0.82). For the 4 urban regions (East Seoul, West Seoul, Incheon, and Namyangju), ozone simulations were not quite sensitive to the change of BVOC emissions. For rural regions (Wonju and Daegu) , however, BVOC emission affected ozone concentration much more than previously mentioned regions, especially in case of scenario C. This implies the importance of biogenic emissions on ozone production over the sub-urban to rural regions.