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  3. Bioenvironmental Systems Engineering / 生物環境系統工程學系
  4. Development of Color-Based Bacterial Biosensor for Arsenic Detection
 
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Development of Color-Based Bacterial Biosensor for Arsenic Detection

Date Issued
2015
Date
2015
Author(s)
Huang, Chi-Wei
URI
http://ntur.lib.ntu.edu.tw//handle/246246/272570
Abstract
Arsenic is one of the heavy metal pollutants and a known human carcinogen. Using arsenic contaminated groundwater for drinking or irrigation has caused major health problems for humans around the world. Current arsenic detecting techniques based on physicochemical methods have high sensitivity and accuracy. Yet, in addition to high cost, the applications to large scale detection were still limited. Therefore, this study developed a color-based bacterial biosensor which is easy and inexpensive for arsenic screening and could be complementary to physicochemical methods. The arsR-lacZ recombinant gene cassette in nonpathogenic strain E. coli DH5α was used in the color-based biosensor which could be measured by eyes or spectrometer. The developed bacterial biosensor demonstrates a quantitative ranging from 10 - 500 μg/l in 1 - 3 hours reaction time. In addition, the result showed that biosensor with higher OD600 has higher induction and lower response time, but lower accuracy. Furthermore, the biosensor was able to successfully detect and estimate arsenic concentration in groundwater samples. The result further showed that OD600 did not affect the shelf life of biosensor. Among different storage methods, biosensor in liquid at 4℃ showed the longest shelf life about 9 days, whereas others were about 3 - 4 days. In conclusion, this study showed that the arsenic biosensor with reliable color signal makes it useful for large scale rapid screening of arsenic pollutants providing the potential for better management strategies for environmental quality control.
Subjects
biosensor
arsenic pollutants
color-based detection
shelf life
groundwater
SDGs

[SDGs]SDG3

Type
thesis
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ntu-104-R01622048-1.pdf

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23.54 KB

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Checksum

(MD5):51970ca5848117565deb83422bad1673

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