• Title/Summary/Keyword: NIR spectroscopy analysis

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Electrical Properties of Transparent Conductive Films of Single-Walled Carbon Nanotubes with Their Purities

  • Lee, Seung-Ho;Goak, Jeung-Choon;Lee, Chung-Yeol;Lee, Nae-Sung
    • Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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    • 2010.06a
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    • pp.56-56
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    • 2010
  • Single-walled carbon nanotubes (SWCNTs) have attracted much attention as a promising material for transparent conducting films (TCFs), due to their superior electrical conductivity, high mechanical strength, and complete flexibility as well as their one-dimensional morphological features of extremely high length-to-diameter ratios. This study investigated three kinds of SWCNTs with different purities: as-produced SWCNTs (AP-SWCNTs), thermally purified SWCNTs (TH-SWCNTs), thermally and acid purified SWCNTs (TA-SWCNTs). The purity of each SWCNT sample was assessed by considering absorption peaks in the semiconducting ($S_{22}$) and metallic ($M_{11}$) tubes with UV-Vis NIR spectroscopy and a metal content with thermogravimetric analysis (TGA). The purity increased as proceeding the purification stages from the AP-SWCNTs through the thermal purification to the acid purification. The samples containing different contents of SWCNTs were dispersed in water using sodium dodecyl benzensulfate (SDBS). Aqueous suspensions of different purities of SWCNTs were prepared to have similar absorbances in UV-Vis absorption measurements so that one can make the TCFs possess similar optical transmittances irrespective of the SWCNT purity. Transparent conductive SWCNT networks were formed by spraying an SWCNT suspension onto a poly(ethyleneterephthalate) (PET) substrate. As expected, the TCFs fabricated with AP-SWCNTs showed very high sheet resistances. Interestingly, the TH-SWCNTs gave lower sheet resistances to the TFCs than the TA-SWCNTs although the latter was of higher purity in the SWCNT content than the former. The TA-SWCNTs would be shortened in length and be more bundled by the acid purification, relative to the TH-SWCNTs. For both purified (TH, TA) samples, the subsequent nitric acid ($HNO_3$) treatment greatly lowered the sheet resistances of the TCFs, but almost eliminated the difference of sheet resistances between them. This seems to be because the electrical conductivity increased not only due to further removal of surfactants but also due to p-type doping upon the acid treatment. The doping effect was likely to overwhelm the effect of surfactant removal. Although the nitric acid treatment resulted in the similar. electrical properties to the two samples, the TCFs of TH-SWCNTs showed much lower sheet resistances than those of the TA-SWCNTs prior to the acid treatment.

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Development of Near-Infrared Reflectance Spectroscopy (NIRS) Model for Amylose and Crude Protein Contents Analysis in Rice Germplasm (근적외선 분광광도계를 이용한 벼 유전자원 아밀로스 및 단백질 함량분석을 위한 모델개발)

  • Oh, Sejong;Lee, Myung Chul;Choi, Yu Mi;Lee, Sukyeung;Oh, Myeongwon;Ali, Asjad;Chae, Byungsoo;Hyun, Do Yoon
    • Korean Journal of Plant Resources
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    • v.30 no.1
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    • pp.38-49
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    • 2017
  • The objective of this research was to develop Near-Infrared Reflectance Spectroscopy (NIRS) model for amylose and protein contents analysis of large accessions of rice germplasm. A total of 511 accessions of rice germplasm were obtained from National Agrobiodiversity Center to make calibration equation. The accessions were measured by NIRS for both brown and milled brown rice which was additionally assayed by iodine and Kjeldahl method for amylose and crude protein contents. The range of amylose and protein content in milled brown rice were 6.15-32.25% and 4.72-14.81%, respectively. The correlation coefficient ($R^2$), standard error of calibration (SEC) and slope of brown rice were 0.906, 1.741, 0.995 in amylose and 0.941, 0.276, 1.011 in protein, respectively, whereas $R^2$, SEC and slope of milled brown rice values were 0.956, 1.159, 1.001 in amylose and 0.982, 0.164, 1.003 in protein, respectively. Validation results of this NIRS equation showed a high coefficient determination in prediction for amylose (0.962) and protein (0.986), and also low standard error in prediction (SEP) for amylose (2.349) and protein (0.415). These results suggest that NIRS equation model should be practically applied for determination of amylose and crude protein contents in large accessions of rice germplasm.

Construction of Database System on Amylose and Protein Contents Distribution in Rice Germplasm Based on NIRS Data (벼 유전자원의 아밀로스 및 단백질 성분 함량 분포에 관한 자원정보 구축)

  • Oh, Sejong;Choi, Yu Mi;Lee, Myung Chul;Lee, Sukyeung;Yoon, Hyemyeong;Rauf, Muhammad;Chae, Byungsoo
    • Korean Journal of Plant Resources
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    • v.32 no.2
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    • pp.124-143
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    • 2019
  • This study was carried out to build a database system for amylose and protein contents of rice germplasm based on NIRS (Near-Infrared Reflectance Spectroscopy) analysis data. The average waxy type amylose contents was 8.7% in landrace, variety and weed type, whereas 10.3% in breeding line. In common rice, the average amylose contents was 22.3% for landrace, 22.7% for variety, 23.6% for weed type and 24.2% for breeding line. Waxy type resources comprised of 5% of the total germplasm collections, whereas low, intermediate and high amylose content resources share 5.5%, 20.5% and 69.0% of total germplasm collections, respectively. The average percent of protein contents was 8.2 for landrace, 8.0 for variety, and 7.9 for weed type and breeding line. The average Variability Index Value was 0.62 in waxy rice, 0.80 in common rice, and 0.51 in protein contents. The accession ratio in arbitrary ranges of landrace was 0.45 in amylose contents ranging from 6.4 to 8.7%, and 0.26 in protein ranging from 7.3 to 8.2%. In the variety, it was 0.32 in amylose ranging from 20.1 to 22.7%, and 0.51 in protein ranging from 6.1 to 8.3%. And also, weed type was 0.67 in amylose ranging from 6.6 to 9.7%, and 0.33 in protein ranging from 7.0 to 7.9%, whereas, in breeding line it was 0.47 in amylose ranging from 10.0 to 12.0%, and 0.26 in protein ranging from 7.0 to 7.9%. These results could be helpful to build database programming system for germplasm management.