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The component proportion of the preparation was optimized using uniform experimental design and machine learning techniques.
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The preparation is optimized to get well-crystallized well-covering layers.
Both the ball milling and annealing processes in the preparation were optimized to obtain high quality and density of BNNT films.
Once the sample preparation was optimized, the ability of the GPM search engine to detect endogenous peptides was examined.
The procedure of biosensor preparation was optimized.
To maximize the capacity for long-read sequencing, sample preparation was optimized based on bisulfite conversion and PCR amplification.
This method of MOF preparation was optimized by Repka et al. [ 8].
The effects of pH, the monomer concentration, the number of cycles for the electropolymerization, and the scan rate for the sensor preparation were optimized.
Experimental variables concerning the biosensor preparation were optimized.
Thin film hydration followed by sonication method was employed in liposome preparation and the formulation was optimized using 32 full factorial design.
The effects of pH, concentrations of MWCNTs and monomer, the number of cycles for the electropolymerization and the scan rate for sensor preparation were optimized.
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