손상된 콘크리트 구조물은 적절한 보수 및 보강을 통해 성능과 기능을 회복시켜야 한다. 장기간 공기 중에 노출된 콘크리트는 동결융화 작용으로 균열 및 박리를 일으켜 내부 철근의 부식을 유발하게 되는 주요 요인이 된다. 본 연구에서는 동 결융해 손상을 입은 콘크리트 교각의 FRP 보강의 연성에너지 증가 효과를 분석하였다. 보강 FRP 재료와 보강 높이, 보강 겹수 에 따라 동결융해 손상 콘크리트의 푸쉬오버 매개변수 해석을 수행하여 모멘트 곡률의 연성에너지를 비교 분석하였다. FRP 보 강 높이는 소성 힌지 이상의 높이 보강은 비효율적이며, 동결융해 손상이 커질수록 FRP 보강으로 인한 연성에너지 증가량은 커 지는 것을 확인하였다. 보강으로 인한 연성에너지 증가를 위해서는 고강도 FRP 재료보다는 높은 탄성계수를 갖는 FRP 재료가 효율적으로 나타났다. 또한 각 FRP 재료의 특성에 따라 일정 보강 겹수 이상에서 보강 효과가 나타나는 것을 확인하여 FRP 보 강으로 인한 손상된 콘크리트 교각의 연성에너지를 비교 분석하였다.

This study was conducted to investigate the feed value and silage quality according to storage period and film layers in whole crop oat silage. The crude protein content was increased in all silage during the storage periods compared to those before silage, under prolonged storage period slightly and the number of film layers of silage, six layer were higher than four layers, but no significant. NDF and ADF contents were also increased in all silage of storage duration compared to those before silage, but they was similar level between storage duration and number of film layers. TDN content was decreased of the storage duration. However, it was similar under the storage duration and the number of film layers. The pH value was decreased during prolonged storage period and six layers was lower than four layers depending on the film layers. In the organic acid contents, lactic acid and acetic acid were increased under the prolonged storage duration, and butyric acid was higher significantly(p<0.05), six layers of the film were showed higher lactic acid and lower butyric acid(p<0.05). Therefore, oat silage should be used within 6 months it was when treated with 4 layers, if considering the long-term storage, it is desirable to treat it with 6 layers or more.

The electronic and optical characteristics of molybdenum disulphide (MoS2) film significantly vary with its thickness, and thus a rapid and accurate estimation of the number of MoS2 layers is critical in practical applications as well as in basic researches. Various existing methods are currently available for the thickness measurement, but each has drawbacks. Transmission electron microscopy allows actual counting of the MoS2 layers, but is very complicated and requires destructive processing of the sample to the point where it will no longer be useable after characterization. Atomic force microscopy, particularly when operated in the tapping mode, is likewise time-consuming and suffers from certain anomalies caused by an improperly chosen set point, that is, free amplitude in air for the cantilever. Raman spectroscopy is a quick characterization method for identifying one to a few layers, but the laser irradiation causes structural degradation of the MoS2. Optical microscopy works only when MoS2 is on a silicon substrate covered with SiO2 of 100~300 nm thickness. The last two optical methods are commonly limited in resolution to the micrometer range due to the diffraction limits of light. We report here a method of measuring the distribution of the number of MoS2 layers using a low voltage field emission electron microscope with acceleration voltages no greater than 1 kV. We found a linear relationship between the FESEM contrast and the number of MoS2 layers. This method can be used to characterize MoS2 samples at nanometer-level spatial resolution, which is below the limits of other methods.

The unique characteristics of graphene make it an optimal material for crucial studies; likewise, its potential applications are numerous. Graphene’s characteristics change with the number of total layers, and thus the rapid and accurate estimation of the number of graphene layers is essential. In this work, we review the methods till date used to identify the number of layers but they incorporate certain drawbacks and limitations. To overcome the limitations, a combination of these methods will provide a direct approach to identify the number of layers. Here we correlate the data obtained from Raman spectroscopy, optical microscopy images, and atomic force microscopy to identify the number of graphene layers. Among these methods, correlation of optical microscopy images with Raman spectroscopy data is proposed as a more direct approach to reliably determine the number of graphene layers.

Poria cocos is an edible and pharmaceutical mushroom with a long history of medicinal use in Korea. For the last 30 years, the domestic cultivated supply of Poria cocos has been unable to meet consumer demand, so Poria cocos is collected in mountainous areas and also imported from China. Thus, to increase the supply of Poria cocos, many artificial cultivation methods have been studied. In this study, Poria cocos is cultivated under different environmental conditions using plastic bags and the results compared. When cultivating Poria cocos at different temperatures (20, 25, 30 and 35oC) and using different numbers of plastic bag layers (1, 2), the most efficient cultivation conditions were a temperature of 25-30oC and 2 plastic bag layers. The fastest growth was at 25-30oC, and the Poria cocos exhibited no weight change when cultivated using layers of plastic bags (1, 2).

연구에서는 작동 유체중에 포집되어 있는 불용성 포집 계면층을 통과하는 금속입자 운동에 대한 특성을 조사하기 위하여 상호 불용인 착색 글리세린수용액과 사염화탄소 등의 계면층을 만들고 단일강구를 하강시켜 이 계면을 통과 할 때의 계면의 변화에 의한 유체 이동의 궤적과 그 후연에 생기는 기포체적에 관한 이론해석 및 실험분석을 통하여 다음의 결론을 얻었다. 1. 상층액 내에서의 강구 하강에 의한 유체 이동궤적은 상층액과 하층액의 비중 및 점도가 모두 같은 경우를 제외하고는 상층액의 두께에 영향을 받는다. 2. 하층액의 점도가 상층액의 점도와 같거나 큰 경우의 불용성 계면에서는 용액의 물성치에 관계없이 그 궤적 형상이 일정하고, 하층액의 점도에 의해 지배적이며 그 궤적의 형상은 다음과 같다. (1)/(V-2.35)=0.014+0.038μl super(-1) + 0.028μl super(-2). 3. 하층액의 점도가 상층액의 것보다 큰 경우의 유체 이동궤적의 체적은 구의 하강거리와 상하층액의 점도비에 의해 다음과 같다. V(Sr) super(0.34)=7.86a super(-2.08). 4. 후류기포생성체적의 비는 R sub(e)수가 100 이하에서 R sub(e)수에 의존하나 그 이상에서는 M sub(0)수의 영향이 지배적이며 그의 상관식은 다음과 같다. (V sub(w)/V sub(b))M sub(0) super(-0.22)=0.012R sub(e) super(1.53) ; R sub(e)〈100. (V sub(w)/V sub(b))M sub(0) super(-0.22)=62.5 ;R sub(e)〉100

Carbonation of reinforced concrete is a major factor in the deterioration of reinforced concrete, and prediction of the resistance to carbonation is important in determining the durability life of reinforced concrete structures. In this study, basic research on the prediction of carbonation penetration depth of concrete using Deep Learning algorithm among artificial neural network theory was carried out. The data used in the experiment were analyzed by deep running algorithm by setting W/B, cement and blast furnace slag, fly ash content, relative humidity of the carbonated laboratory, temperature, CO2 concentration, Deep learning algorithms were used to study 60,000 times, and the analysis of the number of hidden layers was compared.