Use of Box Behnken Design for Development of High Throughput Quantitative Proton Nuclear Magnetic Resonance Experiments for Industrial Applications

Abstract

The design of experiments has been used for the development of highly efficient and accurate industrial application of quantitative nuclear magnetic resonance (qNMR) spectroscopy. Since these factors are highly dependent on the choice of proper data acquisition and data processing parameters, in this work, seven data acquisition and data processing parameters (excitation pulse width/duration, central frequency, number of scans, sample temperature, acquisition time, line broadening function, and inter scan delay) were investigated and optimized for quantitative proton NMR (qHNMR) experiments to ascertain optimal values. Box Behnken design of experiments was used to arrive at these values by performing minimum number of experiments. To test this methodology, decontamination formulation number two (DS2), a universal field decontaminating reagent of highly toxic chemical warfare agents, was used as a model mixture. Quantification of its components (methyl cellosolve and diethylene triamine) was performed with respect to dimethyl sulfone (DMS) as an internal standard. Under the optimized conditions, ∼100% recovery and maximum signal-to-noise ratio (of DMS was 39908) were obtained within a short experiment time (∼15 min). Lower limits of detection and quantification for DETA were found to be 1.92 and 6.40 mg mL–1, whereas those for MC were found to be 0.89 and 2.86 mg mL–1, respectively. Finally, this method was applied for the analysis of 14 unknown samples of DS2. A t test conducted on these samples clearly indicated that there was no significant difference (p > 0.05) between the means of the results obtained from this qHNMR method and the earlier reported classical method of analysis. This clearly suggests that this novel and comprehensive methodology can be used as an independent analytical tool for quantification of the components of complex chemical mixtures.

Multiobjective Optimization for the Greener Synthesis of Chloromethyl Ethylene Carbonate by CO2 and Epichlorohydrin via Response Surface Methodology

“…Several authors including AboElazayem et al [38] and Onyenkeadi et al [39], have criticized the traditional "trial-and-error" optimization methods and "one-factor-at-a-time" (OFAT) as time-consuming and considered quite expensive due to a large number of samples and experimental trials involved. Another drawback identified with traditional optimization methods is low overall efficiency [40]. Sadeghi and Sharifnia [41], describe OFAT as a method that excludes the interactive effects among the variables and does not express the complete effects of the parameters on the process.…”