Theoretical characterization of gas–liquid chromatographic stationary phases with quantum chemical descriptors

Transfer of gas chromatographic retention data among poly(siloxane) columns by quantitative structure-retention relationships based on molecular descriptors of both solutes and stationary phases

In the present study, computational molecular descriptors of 90 saturated esters and seven poly(siloxane) stationary phases with different polarity (SE-30, OV-7, DC-710, OV-25, XE-60, OV-225 and Silar-5CP) were combined into quantitative structure-retention relationship (QSRR) models aimed at predicting the Kováts retention indices (RIs) of the solutes. The molecular descriptors (174) of the stationary phases included in the models were computed using Dragon software from poly(siloxane) oligomers made of 20 siloxane units reflecting the nominal composition of the stationary phase, whereas 439 molecular descriptors were adopted to represent the esters. Different QSRR models were generated by means of Partial Least Squares (PLS) regression to assess the accuracy of this approach in predicting the RIs of unexplored solutes both in known and external stationary phases. After calibration of each PLS model, the descriptors were selected/discarded according to their relevance, evaluated by Covariance Selection (CovSel), and the PLS models were re-built, which resulted in a noticeable improvement of their predictive ability. Firstly, all the available data were equally divided into a training and a test set; the model built on the calibration set was used to predict the RIs of the validation observations. Successively, seven diverse PLS models were created following a "leave-one-column-out" fashion procedure, each one finalized to the estimation of the RIs of the 90 esters associated with a single stationary phase, whereas the calibration model was calculated on the remaining data. All the estimated models provided successful results on the external stationary phase, and predictive performance further increased after variable selection based on CovSel analysis. The final models provided a Root Mean Square Error in Cross Validation (RMSECV) in the range 12-20, a Root Mean Square Error in Prediction (RMSEP) in the range 11-26, and Mean Absolute Percentage Errors in Prediction (MAMEPs) in the range 0.7-1.5, revealing accurate cross-column prediction. Eventually, to test the robustness of the proposed approach, the 90 solutes were equally partitioned into a calibration and a test set and two further QSSR strategies were applied. The first PLS model was calibrated on all the seven stationary phases and the RIs of the 45 external solutes in the same seven columns were simultaneously predicted. The last QSRR approach followed a "leave-one-column-out" scheme and RI of 45 test solutes on an external stationary phase was predicted by a PLS model calibrated with the data of the 45 remaining solutes and the six left stationary phases. After selection of the significant molecular descriptors, PLS regression provided RMSECV values in the range 6-19, RMSEPs in the range 10-14, and MAPEPs in the range 0.9-2.4, revealing the suitability of the approach to deduce the RI of unknown solutes in uncharted stationary phases.

Development and validation of an improved, thin film solid phase microextraction based, standard gas generating vial for the repeatable generation of gaseous standards

This work presents the development and validation of novel thin film solid phase micro extraction (TF-SPME) based standard gas generating vials suitable for repeatable generation of gaseous standards for GC-MS analysis and quality control. The vials were developed using carbon mesh membranes loaded with pure polydimethylsiloxane (PDMS), divinylbenzene (DVB/PDMS), hydrophilic-lipophilic balance (HLB/PDMS), and carboxen (Car/PDMS) sorbents that were then spiked with modified McReynolds standards including benzene, 2-pentanone, 1-nitropropane, pyridine, 1-pentanol, octane, dodecane, and hexadecane. Sorbent strength was determined to follow the aforementioned order, with pure PDMS presenting the weakest sorption capabilities and Car/PDMS the strongest. While the weaker, pure PDMS based gas generating vials transferred an instrument-overloading amount of McReynolds probes to the 1.1 mm DVB/PDMS SPME arrows used for extraction, vials prepared using Car/PDMS TF-SPME as a sorbent failed to provide consistently detectable amounts of analytes less volatile than 1-nitropropane. The DVB/PDMS and HLB/PDMS based vials were found to maintain optimal sorption capabilities for the tested analytes, providing a sorption strength strong enough to not exhibit any depletion in 10 replicate runs, while still delivering a consistent amount of all the regular McReynolds components. Moreover, with intra-vial%RSDs of 5% or less for all analytes tested, these HLB and DVB vials were found to deliver very good repeatability. After purposely submitting vials to 200 accelerated depletion extractions (1.1 mm DVB/PDMS arrow at 55 °C for 3 min), vials prepared with DVB/PDMS were found to deplete by 33%, 38%, 34%, 33%, 40%, and 33% while vials prepared with HLB/PDMS were found to deplete by 21%, 16%, 12%, 31%, 16% and 0% for benzene, 2-pentanone, 1-nitropropane, pyridine, 1-pentanol, and octane, respectively. When user typical extractions conditions were used instead (50/30 μm DVB/Car/PDMS SPME fiber at 35 °C for 1 min), no depletion could be observed from the HLB/PDMS based vial while%RSDs ranged from 1.1-3.0% after the 300 extraction/desorption cycles. Finally, in efforts to demonstrate its real world applicability, the DVB/PDMS vial was used to evaluate the inter-fiber repeatability of commercial DVB/PDMS SPME arrows, with results demonstrating that arrows from a single package were statistically similar (ANOVA at 95% confidence).

Development of a Drone-Based Thin-Film Solid-Phase Microextraction Water Sampler to Facilitate On-Site Screening of Environmental Pollutants

To simplify on-site water sampling and screening, particularly in hard-to-reach or dangerous sites, a drone equipped with a hydrophilic-lipophilic balance (HLB), thin-film solid-phase microextraction (TF-SPME) sampler was developed. The drone-based sampler was shown to protect the sorbent phase from external contamination while preventing any detectable loss of components of a spiked modified McReynolds mixture on the membrane in the sampler for at least 10 min. HLB/poly(dimethylsiloxane) (PDMS) membranes deployed in flight on the drone sampler were demonstrated to extract disinfection by-products, including trichloromethane, dichloroacetonitrile, 1,1,1-trichloro-2-propanone, 2,2,2-trichloroethanol, benzonitrile, and benzyl nitrile, from hot tub water. When analyzed on-site, in duplicate, using hand-portable instrumentation, reasonably repeatable results were achieved (%relative standard deviations (RSD's) 5-16%). Finally, drone TF-SPME sampling of an anthropogenically impacted watercourse indicated that impact from the suspected nearby landfill site was minimal, instead suggesting that internal combustion by-products from vehicles on the nearby Highway 401 played a much larger role in contaminating the watercourse. This conclusion was supported by the confirmed presence of BTEX, styrene, isopropylbenzene, propylbenzene, and 1,3,5-trimethylbenzene. In addition to immediately identifying these compounds on-site using portable gas chromatography-mass spectrometry (GC-MS), samples were taken back to the laboratory for benchtop analysis, further supporting this conclusion.

Characterisation of Gas-Chromatographic Poly(Siloxane) Stationary Phases by Theoretical Molecular Descriptors and Prediction of McReynolds Constants

Retention in gas–liquid chromatography is mainly governed by the extent of intermolecular interactions between the solute and the stationary phase. While molecular descriptors of computational origin are commonly used to encode the effect of the solute structure in quantitative structure–retention relationship (QSRR) approaches, characterisation of stationary phases is historically based on empirical scales, the McReynolds system of phase constants being one of the most popular. In this work, poly(siloxane) stationary phases, which occupy a dominant position in modern gas–liquid chromatography, were characterised by theoretical molecular descriptors. With this aim, the first five McReynolds constants of 29 columns were modelled by multilinear regression (MLR) coupled with genetic algorithm (GA) variable selection applied to the molecular descriptors provided by software Dragon. The generalisation ability of the established GA-MLR models, evaluated by both external prediction and repeated calibration/evaluation splitting, was better than that reported in analogous studies regarding nonpolymeric (molecular) stationary phases. Principal component analysis on the significant molecular descriptors allowed to classify the poly(siloxanes) according to their chemical composition and partitioning properties. Development of QSRR-based models combining molecular descriptors of both solutes and stationary phases, which will be applied to transfer retention data among different columns, is in progress.