Electrochemical Sensing of Nitroaromatic Compounds in Natural Waters and Soil Samples

2012 ◽  
pp. 407-438
Keyword(s):  
Natural Waters ◽  
Nitroaromatic Compounds ◽  
Soil Samples ◽  
Electrochemical Sensing

DETERMINATION OF NITROAROMATIC COMPOUNDS IN SOIL SAMPLES BY HPLC, USING ON-LINE PRECONCENTRATION

2002 ◽  
Vol 25 (20) ◽  
pp. 3177-3185 ◽  
Author(s):  
Katarína Hrobonová ◽  
Miroslav Lacuška ◽  
Karol Balog ◽  
Jozef Lehotay
Keyword(s):  
Nitroaromatic Compounds ◽  
Soil Samples ◽  
On Line

Enhanced electrochemical sensing of nitroaromatic compounds based on hydroxyl modified carbon submicroparticles

2016 ◽  
Vol 203 ◽  
pp. 301-308 ◽  
Author(s):  
Shangshu Pan ◽  
Linan Wang ◽  
Xi Chen ◽  
Yang Tang ◽  
Yongmei Chen ◽  
...  
Keyword(s):  
Nitroaromatic Compounds ◽  
Electrochemical Sensing

scholarly journals Flagellin-based electrochemical sensing layer for arsenic detection in water

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hajnalka Jankovics ◽  
Patrik Szekér ◽  
Éva Tóth ◽  
Balázs Kakasi ◽  
Zoltán Lábadi ◽  
...  
Keyword(s):  
Natural Waters ◽  
Dissociation Constants ◽  
Binding Capacity ◽  
High Surface ◽  
Inorganic Arsenic ◽  
Electrochemical Sensing ◽  
Titration Calorimetry ◽  
Surface Binding ◽  
Arsenic Detection ◽  
Monomeric Proteins

AbstractRegular monitoring of arsenic concentrations in water sources is essential due to the severe health effects. Our goal was to develop a rapidly responding, sensitive and stable sensing layer for the detection of arsenic. We have designed flagellin-based arsenic binding proteins capable of forming stable filament structures with high surface binding site densities. The D3 domain of Salmonella typhimurium flagellin was replaced with an arsenic-binding peptide motif of different bacterial ArsR transcriptional repressor factors. We have shown that the fusion proteins developed retain their polymerization ability and have thermal stability similar to that of wild-type filament. The strong arsenic binding capacity of the monomeric proteins was confirmed by isothermal titration calorimetry (ITC), and dissociation constants (Kd) of a few hundred nM were obtained for all three variants. As-binding fibers were immobilized on the surface of a gold electrode and used as a working electrode in cyclic voltammetry (CV) experiments to detect inorganic arsenic near the maximum allowable concentration (MAC) level. Based on these results, it can be concluded that the stable arsenic-binding flagellin variant can be used as a rapidly responding, sensitive, but simple sensing layer in a field device for the MAC-level detection of arsenic in natural waters.

K-MEANS CLUSTERING ALGORITHM BASED CLASSIFICATION OF SOIL FERTILITY IN NORTH WEST NIGERIA

2020 ◽  
Vol 4 (2) ◽  
pp. 780-787
Author(s):  
Ibrahim Hassan Hayatu ◽  
Abdullahi Mohammed ◽  
Barroon Ahmad Isma’eel ◽  
Sahabi Yusuf Ali
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Clustering Algorithm ◽  
Soil Samples ◽  
North West ◽  
R Programming ◽  
Available Information ◽  
Northwest Region ◽  
The Relationship

Soil fertility determines a plant's development process that guarantees food sufficiency and the security of lives and properties through bumper harvests. The fertility of soil varies according to regions, thereby determining the type of crops to be planted. However, there is no repository or any source of information about the fertility of the soil in any region in Nigeria especially the Northwest of the country. The only available information is soil samples with their attributes which gives little or no information to the average farmer. This has affected crop yield in all the regions, more particularly the Northwest region, thus resulting in lower food production.  Therefore, this study is aimed at classifying soil data based on their fertility in the Northwest region of Nigeria using R programming. Data were obtained from the department of soil science from Ahmadu Bello University, Zaria. The data contain 400 soil samples containing 13 attributes. The relationship between soil attributes was observed based on the data. K-means clustering algorithm was employed in analyzing soil fertility clusters. Four clusters were identified with cluster 1 having the highest fertility, followed by 2 and the fertility decreases with an increasing number of clusters. The identification of the most fertile clusters will guide farmers on where best to concentrate on when planting their crops in order to improve productivity and crop yield.

Sign in / Sign up

Export Citation Format

Share Document