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Computer assisted multivariate analysis of the resulting high volume data (8000 data points) was performed.
The 3D position reconstruction of interaction points was performed using artificial neural networks trained with experimental data.
To identify the effects of the main factors and their interactions on copper removal efficiency and to optimize the process, a full 24 factorial design with central points was performed.
A central composite face-centered design with 3 center points was performed in order to optimize conditions and to study the effect of four variables (pH of the sample, concentration of APDC, concentration of Triton-X-100, and sample volume).
The geometry optimization of gas-phase ion pairs of over 30 chosen ionic liquid and related analogues (some salts with high melting points) was performed first, then the interaction energies of the series of molecules between cation and anion were calculated and the basis set superposition error (BSSE) was corrected by counterpoise method (CP).
A standardized protocol for marking the measuring points was performed by the same study examiner (the second author).
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Comparisons between two groups with different time points were performed using two-way ANOVA with Bonferroni post tests.
A detailed study on the effect of different distributions of the so-called support points is performed.
Quantitative analysis of CT values of ROIs at corresponding time points were performed.
Thus, based on the CCD, 20 experiments including 8 cube points, 6 center points and 6 axial points were performed.
All time points were performed in triplicate and the experiment was performed three times.
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