Determination of effective parameters of Biot model with the Kelvin–Voight rheological skeleton

2015 ◽  
Vol 15 (4) ◽  
pp. 1173-1179 ◽  
Author(s):  
M. Bartlewska-Urban ◽  
T. Strzelecki ◽  
R. Urban
Keyword(s):  
Effective Parameters ◽  
Biot Model

Selective electromembrane extraction and sensitive colorimetric detection of copper(II)

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Wajid Ali Khan ◽  
Muhammad Balal Arain ◽  
Hashmat Bibi ◽  
Mustafa Tuzen ◽  
Nasrullah Shah ◽  
...  
Keyword(s):  
Applied Voltage ◽  
Colorimetric Detection ◽  
Environmental Water ◽  
Selective Extraction ◽  
Effective Parameters ◽  
Relative Standard ◽  
Electromembrane Extraction ◽  
Optimized Conditions ◽  
Donor Phase

AbstractIn this study, an extremely effective electromembrane extraction (EME) method was developed for the selective extraction of Cu(II) followed by Red-Green-Blue (RGB) detection. The effective parameters optimized for the extraction efficiency of EME include applied voltage, extraction time, supported liquid membrane (SLM) composition, pH of acceptor/donor phases, and stirring rate. Under optimized conditions, Cu(II) was extracted from a 3 mL aqueous donor phase to 8 µL of 100 mM HCl acceptor solution through 1-octanol SLM using an applied voltage of 50 V for 15 min. The proposed method provides a working range of 0.1–0.75 µg·mL−1 with 0.03 µg·mL−1 limit for detection. Finally, the developed technique was applied to different environmental water samples for monitoring environmental pollution. Obtained relative recoveries were within the range of 93–106%. The relative standard deviation (RSD) and enhancement factor (EF) were found to be ≤4.8% and 100 respectively. We hope that this method can be introduced for quantitative determination of Cu(II) as a fast, simple, portable, inexpensive, effective, and precise procedure.

Theoretical determination of all the effective parameters in the BETS related crystals by HF and DFT methods

2003 ◽  
Vol 133-134 ◽  
pp. 565-567 ◽  
Author(s):  
T. Kawakami ◽  
Y. Kitagawa ◽  
T. Taniguchi ◽  
S. Nakano ◽  
R. Takeda ◽  
...  
Keyword(s):  
Effective Parameters ◽  
Theoretical Determination ◽  
Dft Methods

Determination of number of check dams by artificial neural networks in arid regions of Iran

2015 ◽  
Vol 72 (6) ◽  
pp. 952-959 ◽  
Author(s):  
Seyed Ali Asghar Hashemi ◽  
Hamed Kashi
Keyword(s):  
Curve Number ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Effective Parameters ◽  
Check Dams ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Mlp Model

An artificial neural network (ANN) model with six hydrological factors including time of concentration (TC), curve number, slope, imperviousness, area and input discharge as input parameters and number of check dams (NCD) as output parameters was developed and created using GIS and field surveys. The performance of this model was assessed by the coefficient of determination R2, root mean square error (RMSE), values account and mean absolute error (MAE). The results showed that the computed values of NCD using ANN with a multi-layer perceptron (MLP) model regarding RMSE, MAE, values adjustment factor (VAF), and R2 (1.75, 1.25, 90.74, and 0.97) for training, (1.34, 0.89, 97.52, and 0.99) for validation and (0.53, 0.8, 98.32, and 0.99) for test stage, respectively, were in close agreement with their respective values in the watershed. Finally, the sensitivity analysis showed that the area, TC and curve number were the most effective parameters in estimating the number of check dams.

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