Per- and polyfluoroalkyl substances (PFAS) are of increasing concern due to their environmental persistence, bioaccumulative nature, and association with adverse health outcomes. The growing need for large-scale monitoring and long-term exposure assessment studies necessitates the development of high-throughput, sustainable analytical methodologies. In this work, a solid-phase microextraction-microfluidic open interface-mass spectrometry (SPME-MOI-MS) platform was developed for the rapid screening of 18 PFAS compounds in human plasma. By bypassing the liquid chromatography separation, the method achieves high-throughput performance with an average analysis time of 3.7 min per sample. A novel SPME coating, comprising hydrophilic-lipophilic balanced mixed-mode weak anion exchange sorbent (HLB-WAX) particles embedded in a polyacrylonitrile (PAN) binder, enabled efficient extraction and effective cleanup of complex biological matrices, facilitating direct MS analysis. The method demonstrated excellent linearity (1-100 ng/mL) and low limits of detection (0.11-0.86 ng/mL) across target PFAS compounds. For practical application, PFOA and PFNA were detected in human plasma samples during these initial investigations, demonstrating the potential of the SPME-MOI-MS approach for large-scale PFAS biomonitoring and exposure assessment.

Per- and polyfluoroalkyl substances (PFAS) are ubiquitous environmental pollutants that pose potential risks to ecosystems and human health. Prior to mass spectrometric analysis of environmental samples, it is necessary to separate PFAS from compounds that can cause ion suppression and compromise analyte identification and quantification accuracy. Although liquid chromatography-mass spectrometry (LC-MS) is the gold standard for PFAS trace analysis, some PFAS species still coelute in the LC dimension and could benefit from an orthogonal dimension of separation. Moreover, an additional orthogonal dimension of separation could potentially aid in the identification of unknown fluorinated species (e.g., those identified within a specified mass-defect range). Here, we investigate the sequential use of LC and differential mobility spectrometry (DMS) separation to analyze 34 PFAS species. Upon incorporating DMS in a two-dimensional (2D) separation scheme, we observed baseline resolution of 29 compounds in the 2D LC × DMS space, with partial resolution of the remaining five. In comparison, only five PFAS compounds were baseline-resolved in 1D LC experiments. Because DMS measurements can be acquired in milliseconds, targeted 2D LC × DMS-MS2 analyses operate on the same time scale as LC-MS2 analysis. However, the limits of quantitation for PFAS using the 2D LC × DMS-MS2 method are slightly higher than those achieved by the state-of-the-art LC-MS2 method owing to ion fragmentation within the energetic DMS environment. Nevertheless, distinct trends observed in the 2D separation space for the various PFAS subclasses will facilitate analyte identification of unknown species in future nontargeted analyses. Finally, we assessed the feasibility of our method for quantifying PFAS in a series of wastewater samples obtained from a Southern Ontario wastewater treatment plant. We were successful in quantifying PFOS, although the concentrations determined were consistently higher than those measured with LC-MS2.

Liquid chromatography (LC) is a cornerstone of analytical separations, but comparing the retention times (RTs) across different LC methods is challenging because of variations in experimental parameters such as column type and solvent gradient. Nevertheless, RTs are powerful metrics in tandem mass spectrometry (MS2) that can reduce false positive rates for metabolite annotation, differentiate isobaric species, and improve peptide identification. Here, we present Graphormer-RT, a novel graph transformer that performs the first single-model method-independent prediction of RTs. We use the RepoRT data set, which contains 142,688 reverse phase (RP) RTs (from 191 methods) and 4,373 HILIC RTs (from 49 methods). Our best RP model (trained and tested on 191 methods) achieved a test set mean average error (MAE) of 29.3 ± 0.6 s, comparable performance to the state-of-the-art model which was only trained on a single LC method. Our best-performing HILIC model achieved a test MAE = 42.4 ± 2.9 s. We expect that Graphormer-RT can be used as an LC "foundation model", where transfer learning can reduce the amount of training data needed for highly accurate "specialist" models applied to method-specific RP and HILIC tasks. These frameworks could enable the machine optimization of automated LC workflows, improved filtration of candidate structures using predicted RTs, and the in silico annotation of unknown analytes in LC-MS2 measurements.

Fentanyl and its analogues are potent opioids that pose a significant threat to society. Over the last several years, considerable focus has been on the concerning trend of increasing fentanyl usage among drug users. Fentanyl analogues are mainly synthesized to evade analytical detection or increase their potency; thus, very low concentrations are sufficient to achieve a therapeutic effect. In an effort to help combat the synthetic opioid epidemic, developing targeted mass spectrometric methods for quantifying fentanyl and its analogues at ultralow concentrations is incredibly important. Most methods used to analyze fentanyl and its analogues from whole blood require manual sample preparation protocols (solid-phase extraction or liquid-liquid extraction), followed by chromatographic separation and mass spectrometric detection. The main disadvantages of these methods are the tedious sample preparation workflows, resulting in lengthy analysis times. To mitigate these issues, we present a targeted method capable of analyzing 96 samples containing fentanyl, several fentanyl analogues, and a common fentanyl (analogue) precursor simultaneously in 2.4 min per sample. This is possible by using a high-throughput solid phase microextraction workflow on the Concept96 autosampler followed by manual coupling of solid-phase microextraction fibers to the microfluidic open interface for tandem mass spectrometry analysis. Our quantitative method is capable of extremely sensitive analysis, with limits of quantification ranging from 0.002 to 0.031 ng mL-1 and linearity ranging from 0.010 to 25.0 ng mL-1. The method shows very good reproducibility (1-18%), accuracy (81-100%) of calibration and validation points, and good interday reproducibility (6-15%).

Binders are critical components used in the preparation of a range of extraction devices, including solid-phase microextraction (SPME) devices. While the main role of a binder is to affix the sorbent particles to the selected support, it is critical to select the optimal binder to ensure that it does not negatively impact the coating's particle sorption capability. This work presents the first comprehensive investigation of the interactions between binders and solid sorbent particles as these interactions can significantly impact the performance of the coating. Specifically, the findings presented herein provide a better understanding of the extraction mechanisms of composite coatings and new rules for predicting the particle adhesion forces and binder distribution in the coating. The influence of binder chemistry on coating performance is investigated by examining a selection of the most used binders, namely, polydimethylsiloxane (PDMS), polyacrylonitrile (PAN), poly(vinylidene difluoride) (PVDF), polytetrafluoroethylene amorphous fluoroplastics (PTFE AF 2400), and polybenzimidazole (PBI). The solid particles (e.g., hydrophilic-lipophilic balanced (HLB) and C18) used in this work were selected for their ability to provide optimal extraction coverage for a broad range of analytes. The results show that PDMS does not change the properties of the solid particles and that the binder occupies a negligible volume due to shrinking after polymerization, resulting in the solid particles making up most of the coating volume. Hence, the coating sorption characteristics correspond closely to the properties of the selected solid particles. On the other hand, the results also showed that PTFE AF 2400 can interact with the active surface of the sorbent, leading to the deactivation of the sorbent particles. Therefore, the extraction performance and permeability coefficients decrease as the size of the penetrant increases, indicating a rigid porous structure. The results of this study can aid in the optimization of SPME devices as they provide reference values that can be used to determine the optimal binder and the sorbent affinity for the targeted compounds. Finally, the present work also provides the broader scientific community with a strategy for investigating the properties of sorbent particle/binder structures and defines the characteristics of a good coating/membrane by analyzing all parameters such as kinetics, thermodynamic equilibria, and morphology.