Liquid Chromatography-Electron Capture Negative Ionization-Tandem Mass Spectrometry Detection of Pesticides in a Commercial Formulation

Exploring Negative Chemical Ionization of Per- and Polyfluoroalkyl Substances via a Liquid Electron Ionization LC-MS Interface

Per- and polyfluoroalkyl substances (PFASs) are a class of aliphatic manufactured compounds comprising fluoro-chemicals with varied functional groups and stable carbon-fluorine bonds. They are defined as "forever chemicals" due to their persistent and bioaccumulative character. These substances have been detected in various environmental samples, including water, air, soil, and human blood, posing significant health hazards. High-performance liquid chromatography coupled with electrospray ionization mass spectrometry (HPLC-ESI-MS) is typically employed for the analysis of PFASs. Negative chemical ionization (NCI) is generally coupled to gas chromatography (GC) and offers high selectivity and sensitivity for compounds containing electronegative atoms, such as PFASs. The liquid electron ionization (LEI) interface is an efficient mechanism developed to robustly couple a liquid flow rate from an LC system to an EI or a CI source. This interface has been successfully utilized for pesticide determination in UHPLC-LEI-CI in negative ion mode (NCI). This work aims to evaluate different parameters involved in the ionization of PFASs analyzed in LC-LEI-NCI and subsequently develop a method for their detection in real samples. The parameters considered for this study include (i) a comparison of different CI reagent gases (methane, isobutane, and argon); (ii) the use of acetonitrile as both the chromatographic solvent and CI reagent gas; (iii) the presence of water and formic acid as chromatographic mobile phase components; and (iv) the mobile phase flow rate. The optimal combination of these parameters led to promising results. Tentative fragmentation pathways of PFASs in NCI mode are proposed based on the dissociative electron capture mechanism.

Measurement of gluconeogenesis by 2H2O labeling and mass isotopomer distribution analysis

The gluconeogenesis pathway, which converts nonsugar molecules into glucose, is critical for maintaining glucose homeostasis. Techniques that measure flux through this pathway are invaluable for studying metabolic diseases such as diabetes that are associated with dysregulation of this pathway. We introduce a new method that measures fractional gluconeogenesis by heavy water labeling and gas chromatographic-mass spectrometric analysis. This technique circumvents cumbersome benchwork or inference of positionality from mass spectra. The enrichment and pattern of deuterium label on glucose is quantified by use of mass isotopomer distribution analysis, which informs on how much of glucose-6-phosphate-derived glucose comes from the gluconeogenesis (GNG) pathway. We use an in vivo model of the GNG pathway that is based on previously published models but offers a new approach to calculating GNG pathway and subpathway contributions using combinatorial probabilities. We demonstrated that this method accurately quantifies fractional GNG through experiments that perturb flux through the pathway and by probing analytical sensitivity. While this method was developed in mice, the results suggest that it is translatable to humans in a clinical setting.

Toxicity monitoring signals analysis of selenite using microbial fuel cells

Microbial fuel cells (MFCs) based biosensors are widely studied to environmental monitoring. The suitable responsive signal is important for microbial electrochemical sensors. However, the responsive signals of toxins have not been investigated in detail. Using sodium selenite as a toxic substance, the different response signals are analyzed over a concentration range from 0 to 150 mg/L in the double chambered. The output voltage and power density had the opposite trend between 0 and 2.5 mg/L and 2.5-150 mg/L. To analyze the reasonable signal of Se(IV) monitoring sensor, correlation analysis of concentrations and responsive signal data (maximum voltage, maximum power density, coulombic recovery, coulombic efficiency, and normalized energy recovery, etc.) has been accomplished. The high concentration of exogenous selenite (2.5-100 mg/L) is negatively correlated with maximum voltage (r = -0.901, p < 0.01) and max power density (r = -0.910, p < 0.01). The low concentration of exogenous selenite is positively correlated with average voltage, max power density, coulombic yield (r = 0.973, 0.999 and 0.975, respectively. p < 0.05). Furthermore, Illumina sequencing results indicate that the addition of sodium selenite solution changes the anode community structure, thereby affecting the removal efficiency of organic matter, which may be the reason why coulombic efficiency and normalized energy recovery are not suitable as sensing signal. Overall, based on the analysis of experimental data, the maximum power density is the best response signal, which provides a reference for the selection of sensor response signal based on microbial fuel cells.