A review of expert systems for chromatography

Chemical Methods for the Separation of Copper Oxide Nanoparticles From Colloidal Suspension in Dodecane

Several chemical methods for the separation of nanoparticles from a colloidal mixture in a phase change material (PCM) have been developed and systematically investigated. The phase changing property of the colloidal mixture is used in energy storage applications and the mixture is labeled as the nanostructure enhanced phase change materials (NEPCM). The objective is to investigate viable methods for the separation and reclamation of the nanoparticles from the NEPCM before its disposal after its useful life. The goal is to find, design, test, and evaluate separation methods which are simple, safe, effective, and economical. The specific NEPCM considered in this study is a colloidal mixture of dodecane (C12H26) and CuO nanoparticles of 1–5% mass fraction and 5–15 nm size distribution. The nanoparticles are coated with a surfactant to maintain colloidal stability. Various methods for separating the nanoparticles from the NEPCM are explored. The identified methods are: (i) chemical destabilization of nanoparticle surfactants to facilitate gravitational precipitation, (ii) silica column chromatography, and (iii) adsorption on silica particle surface. These different methods have been pursued, tested, and analyzed; and the results are presented in this article. These methods are found to be highly efficient, simple, safe, and economical.

Interactions between minimum run time, modifier concentration, and efficiency parameters in a high performance liquid chromatography separation

We modeled and studied the separation of uracil, nicotinamide, resorcinol, theobromine, theophylline, and caffeine on four C-18 columns of different lengths packed with the same stationary phase using water/methanol mobile phase at one temperature. Predictions of retention times and peak widths were compared with experimental results and were found to be sufficiently accurate for performing optimization calculations. With limits set on the required resolution and on maximum values for pressure and flow rate, calculations were performed for numerous virtual column lengths seeking the smallest possible analysis time for each length while allowing methanol concentration and flow rate to vary as required to minimize run time. Predictions were experimentally verified for the column lengths actually available. These calculations revealed the dependence of best-possible analysis time on column length, modifier concentration, flow rate, and pressure for the real system that was modeled, and provided insight into parameter interactions with respect to analysis times meeting the needs and limits specified. We show that when these parameters are considered in concert, rather than individually, conventional guidelines regarding setting their values may not always lead to the optimum.

Intelligent automation of high-performance liquid chromatography method development by means of a real-time knowledge-based approach

We describe the development, attributes and capabilities of a novel type of artificial intelligence system, called LabExpert, for automation of HPLC method development. Unlike other computerised method development systems, LabExpert operates in real-time, using an artificial intelligence system and design engine to provide experimental decision outcomes relevant to the optimisation of complex separations as well as the control of the instrumentation, column selection, mobile phase choice and other experimental parameters. LabExpert manages every input parameter to a HPLC data station and evaluates each output parameter of the HPLC data station in real-time as part of its decision process. Based on a combination of inherent and user-defined evaluation criteria, the artificial intelligence system programs use a reasoning process, applying chromatographic principles and acquired experimental observations to iteratively provide a regime for a priori development of an acceptable HPLC separation method. Because remote monitoring and control are also functions of LabExpert, the system allows full-time utilisation of analytical instrumentation and associated laboratory resources. Based on our experience with LabExpert with a wide range of analyte mixtures, this artificial intelligence system consistently identified in a similar or faster time-frame preferred sets of analytical conditions that are equal in resolution, efficiency and throughput to those empirically determined by highly experienced chromatographic scientists. An illustrative example, demonstrating the potential of LabExpert in the process of method development of drug substances, is provided.