PubMed · 14125121
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L R DRAPER, C R MENCKEN. 1964. AN AUTOMATIC PIPETTING MACHINE.. https://pubmed.ncbi.nlm.nih.gov/14125121/
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Laboratory and field evaluations were performed to validate a solid-phase microextraction (SPME) device that was used as a diffusive sampler. Hydrogen bromide (HBr) was loaded onto the carboxen-polydimethylsiloxane (CAR-PDMS) fiber for the determination of ethylene oxide (EtO) with on-fiber derivatization. For laboratory evaluations, known concentrations of ethylene oxide around the threshold limit values (TLV)/time-weighted average and specific relative humidities (RHs) were generated by syringe pumps in a dynamic generation system. The SPME diffusive samplers and the commercially available 3M 3551 passive monitors were placed side-by-side in an exposure chamber which was designed to allow measurement of face velocities, temperatures, exposing vapor concentrations, and RHs. Field validations with both SPME diffusive sampler and 3M 3551 passive monitors were also performed. The correlations between the results from both SPME device and 3M 3551 passive monitor were found to be linear (r > 0.9699) and consistent (slope approximately equal to 1.12 +/- 0.07). However, the variations of diffusion coefficients at different temperatures needs to be considered and the adjustment of sampling constant was a must when sampling at temperatures different from 25 degrees C.
Analytical chemistry contributes significantly to the life sciences through developing measurement techniques that provide quantitative chemical information. Developments in biological research present new challenges for analytical chemistry and call for revolutionary methods and new thinking. Dana Spence outlines here some key and unique challenges encountered when making measurements on samples of a biological nature, supported with real examples from his own research.
The relative response factor (RRF) of an electron-capture detection (ECD) system is predicted for a set of 118 polychlorinated biphenyls (PCBs). Due to the wide range of relative retention times of PCB congeners, the RRFs of these compounds were calculated based on two different internal standards. Therefore, the compounds were divided into two molecular subsets. As a first step, multiple linear regression (MLR) was employed to find informative descriptors that can predict the RRFs of these compounds. Two descriptors of molecular ion ionization potential (MIIP) and ionization potential of the molecule (IP) that are related to affinity of the compounds for the electrons show the highest mean effects in subsets 1 and 2. respectively. The descriptors appearing in the MLR models were considered as inputs for developing the back-propagation artificial neural networks (BP-ANN). Two networks with the architectures of 5-5-1 and 7-6-1 were generated for the prediction of RRFs of molecules of subsets 1 and 2, respectively. Comparison of the results indicates the superiority of neural networks over that of the MLR method indicating the nonlinear behaviors of the ECD system. Inspection of the models reveals that the surface of the molecules play different roles in response factors of two subsets due to rotation of one phenyl group with respect to the other for the subset consisting of larger number of chlorine atoms.