Accurate Mass Research Articles

High-quality identification of volatile organic compounds (VOCs) originating from breath

IntroductionVolatile organic compounds (VOCs) can arise from underlying metabolism and are detectable in exhaled breath, therefore offer a promising route to non-invasive diagnostics. Robust, precise, and repeatable breath measurement platforms able to identify VOCs in breath distinguishable from background contaminants are needed for the confident discovery of breath-based biomarkers.ObjectivesTo build a reliable breath collection and analysis method that can produce a comprehensive list of known VOCs in the breath of a heterogeneous human population.MethodsThe analysis cohort consisted of 90 pairs of breath and background samples collected from a heterogenous population. Owlstone Medical’s Breath Biopsy® OMNI® platform, consisting of sample collection, TD-GC-MS analysis and feature extraction was utilized. VOCs were determined to be “on-breath” if they met at least one of three pre-defined metrics compared to paired background samples. On-breath VOCs were identified via comparison against purified chemical standards, using retention indexing and high-resolution accurate mass spectral matching.Results1471 VOCs were present in > 80% of samples (breath and background), and 585 were on-breath by at least one metric. Of these, 148 have been identified covering a broad range of chemical classes.ConclusionsA robust breath collection and relative-quantitative analysis method has been developed, producing a list of 148 on-breath VOCs, identified using purified chemical standards in a heterogenous population. Providing confirmed VOC identities that are genuinely breath-borne will facilitate future biomarker discovery and subsequent biomarker validation in clinical studies. Additionally, this list of VOCs can be used to facilitate cross-study data comparisons for improved standardization.

Step-By-Step Procedure to Identify Previously Unknown Compounds by LC-QTOF-MS Exemplified by an Intoxication With the Methaqualone Analog SL-164.

In September 2019, a 22-year-old man with a history of drug abuse presented to the hospital with altered mental status. Due to a suspected drug overdose, a blood sample taken on admission and a urine sample collected 30 h thereafter were submitted to our laboratory to test for illegal drugs, pharmaceutical substances, and designer drugs. During the routine toxicological analysis of the serum sample, morphine and phenobarbital were identified by liquid chromatography-quadrupole-time-of-flight mass spectrometry (LC-QTOF-MS). Additionally, two compounds showing identical accurate masses and isotope ratios as the designer benzodiazepine diclazepam and the benzodiazepine lormetazepam were found. However, retention times differed significantly from the expected values, and the acquired MS/MS spectra did not match the library entries of the two compounds, indicating the presence of two previously unknown substances. After further investigation, SL-164 (5-chloro-3-(4-chloro-2-methylphenyl)-2-methyl-4(3H)-quinazolinone), a methaqualone analog, which has recently emerged on the research chemical market, and its hydroxy metabolite were tentatively identified by accurate mass, isotope matching, and plausible fragmentation. However, for unequivocal confirmation and quantification, a reference standard is required. As no reference material was available by the end of 2019, SL-164 was obtained from an online shop, and its identity and purity (97.8%) were confirmed by nuclear magnetic resonance spectroscopy. The subsequent quantitative analysis revealed a concentration of 390 ng/mL SL-164 in serum. In the urine sample, the parent compound was not detected, but three suspected monohydroxylated metabolites were found. This example shows that LC-QTOF-MS is a powerful approach for the (tentative) identification of unknown compounds in biological matrices.

The weighing process in patients on hemodialysis: an opportunity to improve volume management

Abstract Introduction Hemodialysis relies on accurate body mass (BM) assessment to determine ultrafiltration volumes, but we have not identified published practice patterns, disclosing how to handle clothing mass. Here we investigated the potential impact of clothing mass on predialysis BM determination, hypothesizing that a standardized template for clothing mass estimation enhances accuracy, compared to conventional practice. Methods Measurements included dressed and undressed BM predialysis. A pre-established template for average clothing mass was used to approximate undressed BM from clothed measurements. Differences to undressed BM were compared using Bland-Altman plots and tested for statistical significance using Wilcoxon signed rank tests. Results After excluding erroneous results, data from 48 patients were analyzed. Thirty-six patients (75%) did not habitually estimate clothing mass, but used their dressed BM as the predialysis BM, while the other 12 patients (25%) reported deducting a self-estimated clothing mass from their clothed predialysis BM. The differences to undressed BM were 0.819 ± 0.462 kg and 0.342 ± 0.321 kg in these two groups, respectively, indicating that patients underestimated clothing mass. Using the template to deduct clothing mass from clothed predialysis BM, these differences could be reduced to 0.197 ± 0.220 kg and 0.133 ± 0.135 kg, respectively. The average differences using the patient-reported BM and the template-based BM made up 39.4% and 8.6% of the average, subsequent ultrafiltration volume, respectively, suggesting that potential overestimation of the actual ultrafiltration volume could be reduced. Conclusion A standardized template for clothing mass may be useful to derive representative predialysis BM, leading to more precise ultrafiltration calculation. Exact BM determination might improve volume management in hemodialysis.

Euclid preparation

Precise and accurate mass calibration is required to exploit galaxy clusters as astrophysical and cosmological probes in the Euclid era. Systematic errors in lensing signals by galaxy clusters can be empirically estimated by comparing different surveys with independent and uncorrelated systematics. To assess the robustness of the lensing results to systematic errors, we carried out end-to-end tests across different data sets. We performed a unified analysis at the catalogue level by leveraging the Euclid combined cluster and weak-lensing pipeline (COMB-CL). Notably, COMB-CL will measure weak lensing cluster masses for the Euclid Survey. Heterogeneous data sets from five recent, independent lensing surveys (CHFTLenS, DES SV1, HSC-SSP S16a, KiDS DR4, and RCSLenS), which exploited different shear and photometric redshift estimation algorithms, were analysed with a consistent pipeline under the same model assumptions. We performed a comparison of the amplitude of the reduced excess surface density and of the mass estimates using lenses from the Planck PSZ2 and SDSS redMaPPer cluster samples. Mass estimates agree with the results in the literature collected in the LC2 catalogues. Mass accuracy was further investigated considering the AMICO-detected clusters in the HSC-SSP XXL-North field. The consistency of the data sets was tested using our unified analysis framework. We found agreement between independent surveys at the level of systematic noise in Stage-III surveys or precursors. This indicates successful control over systematics. If this control continues into Stage IV, Euclid will be able to measure the weak lensing masses of around 13 000 (considering shot noise only) or 3000 (noise from shape and large-scale-structure) massive clusters with a signal-to-noise ratio greater than three.

Studying microbially induced corrosion on glass using ToF-SIMS.

Microbially induced corrosion (MIC) is an emerging topic that has huge environmental impacts, such as long-term evaluation of microbial interactions with radioactive waste glass, environmental cleanup and disposal of radioactive material, and weathering effects of microbes. Time-of-flight secondary ion mass spectrometry (ToF-SIMS), a powerful mass spectral imaging technique with high surface sensitivity, mass resolution, and mass accuracy, can be used to study biofilm effects on different substrates. Understanding how to prepare biofilms on MIC susceptible substrates is critical for proper analysis via ToF-SIMS. We present here a step-by-step protocol for preparing bacterial biofilms for ToF-SIMS analysis, comparing three biofilm preparation techniques: no desalination, centrifugal spinning (CS), and water submersion (WS). Comparisons of two desalinating methods, CS and WS, show a decrease in the media peaks up to 99% using CS and 55% using WS, respectively. Proper desalination methods also can increase biological signals by over four times for fatty acids using WS, for example. ToF-SIMS spectral results show chemical compositional changes of the glass exposed in a Paenibacillus polymyxa SCE2 biofilm, indicating its capability to probe microbiologically induced corrosion of solid surfaces. This represents the proper desalination technique to use without significantly altering biofilm structure and substrate for ToF-SIMS analysis. ToF-SIMS spectral results showed chemical compositional changes of the glass exposed by a Paenibacillus bacterial biofilm over 3-month inoculation. Possible MIC products include various phosphate phase molecules not observed in any control samples with the highest percent increases when experimental samples were compared with biofilm control samples.

PFAS Levels, Early Life Factors, and Mammographic Breast Density in Premenopausal Women.

Mammographic breast density (MBD) is a strong risk factor and an intermediate phenotype for breast cancer, yet there are limited studies on how environmental pollutants are associated with MBD. We investigated associations of perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), and perfluorohexane sulfonate (PFHxS) levels with measures of MBD and evaluated if early life factors modified any associations. Metabolon performed metabolomics analysis using ultrahigh-performance liquid chromatography/tandem accurate mass spectrometry in fasting blood from 705 premenopausal women completing their annual screening mammogram in St. Louis, Missouri. We calculated least square means (LSM) of mammographic volumetric percent density (VPD), dense volume (DV), and nondense volume (NDV) by quartiles (Q) of PFOS, PFOA, and PFHxS from multivariable linear regression modeling overall and stratified by recruitment period, race, age at menarche, and body shape at age 10. Models were adjusted for age, age at menarche, body fat percentage, race, family history of breast cancer, oral contraceptive use, alcohol consumption, parity/age at first birth, and body shape at age 10. PFOS, PFOA, and PFHxS were not significantly associated with VPD or NDV. PFHxS was significantly positively associated with DV (, , , ; ). PFOS was positively associated with DV (, , , ; ) with DV being 8.1%, 12%, and 12.3% higher in Q2, Q3, and Q4 compared to Q1. Among women who were underweight/normal weight at age 10, PFOS was positively associated with VPD (, , , ; ) while there was an inverse association among women who were overweight/obese at age 10 (, , , ; ) (). We report novel associations of PFHxS and PFOS with DV in premenopausal women. PFOS, PFOA, and PFHxS were not associated with VPD and NDV. In addition, body shape at age 10 may modify the associations of PFOS with MBD. Further studies are needed to validate our findings and to evaluate the associations of other per- and polyfluoroalkyl substances (PFAS), as well as mixtures of PFAS, with MBD. https://doi.org/10.1289/EHP14065.

Automated Assignment of 15N And 13C Enrichment Levels in Doubly-Labeled Proteins.

Uniform enrichment of 15N and 13C in proteins is commonly employed for 2D heteronuclear NMR measurements of the three-dimensional protein structure. Achieving a high degree of enrichment of both elements is important for obtaining high quality data. Uniform labeling of proteins and glycoproteins expressed in higher organisms (yeast or mammalian cell lines) is more challenging than expression in Escherichia coli, a prokaryote that grows on simple, chemically defined media but does not provide appropriate eukaryotic modifications. It is difficult to achieve complete incorporation of both heavy isotopes, and quality control measures are important for quantitating the level of their enrichment. Mass spectrometry measurements of the isotopic distribution of the intact protein or its proteolytic fragments provide the means to assess the enrichment level. A mass accuracy of 1 ppm or better is shown to be required to distinguish the correct combination of 13C and 15N enrichment due to subtle shifts in peak centroids with differences in the underlying, but unresolved, isotopic fine structure. A simple computer program was developed to optimize the fitting of experimental isotope patterns to statistically derived distributions. This method can determine the isotopic abundance from isotope patterns and isotopologue masses in conventional MS data for peptides, intact proteins, and glycans. For this purpose, MATLAB's isotope simulator, isotopicdist, has been modified to permit the variation of 15N and 13C enrichment levels and to perform a two-dimensional grid search of enrichment levels of both isotopes. We also incorporated an alternate isotope simulator, js-emass, into MATLAB for use in the same fitting program. Herein we benchmark this technique on natural abundance ubiquitin and uniformly [15N,13C]-labeled ubiquitin at both the intact and peptide level, outline considerations for data quality and mass accuracy, and report several improvements we have made to the previously reported analysis of the [15N,13C]-enriched human IgG Fc domain, a glycoprotein that has been expressed in Saccharomyces cerevisiae.

Neutron-based analytical techniques for the elemental composition analysis of electronic waste samples: advantages and challenges

Recycling electronic waste (WEEE) is a fundamental aspect of the circular economy. To develop technology and assess its economic aspects, the composition characterization of the waste items is crucial. Neutron-based analytical techniques, such as the INAA, in-beam NAA, and PGAA, are applicable to measure the elemental composition of WEEE. These techniques are bulk representative and offer simultaneous determination of the relevant elements without sample dissolution. This study explores the suitability of neutron-based elemental analysis methods for measuring common WEEE items, e.g., printed circuit boards and integrated circuits. Matrix effects mostly related to the thermal neutron self-shielding, were identified and successfully corrected, ensuring accurate mass fraction measurements.

Interlaboratory and cross-platform accessibility of time-of-flight wood identification database

The mass spectral database of tree species built by US Fish and Wildlife Service has thousands of entries and has been a valuable resource to combat illegal logging and international trade. The database was and continues to be constructed using a particular ambient ionization time-of-flight mass spectrometry (TOF-MS) platform in the agency branch in Ashland, OR, with which queries of unknown wood samples are investigated exclusively. Laboratories that operate different MS instruments also have an interest in using the database if they can produce valid matches to known samples compatible with the database. Four species were selected for inter-laboratory comparison using Orbitrap MS instruments and the equivalent TOF-MS platform with direct analysis in real time ionization of institution-sourced wood samples. Identities of the known samples were confirmed by examination of their microscopic wood anatomy. Orbitrap analysis was able to identify each species as confidently as the TOF instruments, often with less variation in spectra but not necessarily greater mass accuracy or better-matched signal abundance to the control database. The Orbitrap program also had to be doubled to two scanned mass ranges appended for greater peak intensity, before spectra could be correctly matched to the database, but the program was successful.

Complementary Nanoparticle Characterization by Resistive-Pulse Sensing, Electron Microscopy, and Charge Detection Mass Spectrometry.

Nanotechnology has provided novel modalities for the delivery of therapeutic and diagnostic agents. In particular, nanoparticles (NPs) can be engineered at a low cost for drug loading and delivery. For example, silica NPs have proven useful as a controlled release platform for anti-inflammatory drugs. Despite the wide-ranging potential applications for NPs, robust characterization across all size ranges remains elusive. Electron microscopy (EM) is the conventional tool for measuring NP diameters. However, imitations in throughput and the inability to provide comprehensive information on physical properties, such as mass and density, without underlying assumptions, hinder a complete analysis. In addition, assessing sample heterogeneity, aggregation, or coalescence in solution by traditional EM analysis is not possible. Resistive-pulse sensing (RPS) provides a high throughput, solution-phase method for characterizing particle heterogeneity based on volume. Complementing these methods, charge detection mass spectrometry (CD-MS), a single particle technique, provides accurate mass information for heterogeneous samples including NPs. By combining EM, RPS and CD-MS, accurate volume, mass, and densities were obtained for silica NPs of various sizes. The results show that the density for 20 nm silica NPs is close to the density of fused silica (2.2 g/cm3). Larger silica NPs were found to have densities that were either smaller or larger, while also falling outside the range of densities usually found for silica colloids and NPs (1.9-2.3 g/cm3). Lower densities are attributed to pores (i.e., porous particles). For one sample, the mass distribution showed two components attributed to two populations of particles in the sample with different densities. The synergistic combination of EM, RPS, and CD-MS measurements outlined here for NP samples, allows much more extensive information to be obtained than from any of the techniques alone.

Investigations into the Concentrations and Metabolite Profiles of Doping Agents and Antidepressants in Human Seminal Fluid Using Liquid Chromatography-Mass Spectrometry.

Exogenous substances, including drugs and chemicals, can transfer into human seminal fluid and influence male fertility and reproduction. In addition, substances relevant in the context of sports drug testing programs, can be transferred into the urine of a female athlete (after unprotected sexual intercourse) and trigger a so-called adverse analytical finding. Here, the question arises as to whether it is possible to distinguish analytically between intentional doping offenses and unintentional contamination of urine by seminal fluid. To this end, 480 seminal fluids from nonathletes were analyzed to identify concentration ranges and metabolite profiles of therapeutic drugs that are also classified as doping agents. Therefore, a screening procedure was developed using liquid chromatography connected to a triple quadrupole mass spectrometer, and suspect samples (i.e., samples indicating the presence of relevant compounds) were further subjected to liquid chromatography-high-resolution accurate mass (tandem) mass spectrometry. The screening method yielded 90 findings (including aromatase inhibitors, selective estrogen receptor modulators, diuretics, stimulants, glucocorticoids, beta-blockers, antidepressants, and the nonapproved proliferator-activated receptor delta agonist GW1516) in a total of 81 samples, with 91% of these suspected cases being verified by the confirmation method. In addition to the intact drug, phase-I and -II metabolites were also occasionally observed in the seminal fluid. This study demonstrated that various drugs including those categorized as doping agents partition into seminal fluid. Monitoring substances and metabolites may contribute to a better understanding of the distribution and metabolism of exogenous substances in seminal fluid that may be responsible for the impairment of male fertility. SIGNIFICANCE STATEMENT: This study demonstrates that doping agents as well as clinically relevant substances are transferred/eliminated into seminal fluid to a substantial extent and that knowledge about drug levels (and potential consequences for the male fertility and female exposure) is limited. The herein generated new dataset provides new insights into an important and yet little explored area of drug deposition and elimination, and hereby a basis for the assessment of contamination cases by seminal fluid in sports drug testing.

Nontargeted Analysis of Reactive Nitrogenous Compounds in Suwannee River Standard Reference Materials and Authentic River Water Samples.

Concerns over toxic nitrogenous disinfection byproducts (N-DBPs) necessitate identifying their precursors in source water. Natural organic amino compounds are known precursors to N-DBPs. Three Suwannee River (SR) standard reference materials (SRMs), humic acids (HA), fulvic acids (FA), and natural organic matter (NOM), are commonly used to study DBP formation, but the chemical makeup of amino compounds in SRSRMs remains largely unknown. To address this, we combined stable hydrogen/deuterium isotope labeling, HDPairFinder bioinformatics, and nontargeted high-performance liquid chromatography-high-resolution mass spectrometry (HPLC-HRMS) to characterize these compounds in SRSRMs. This method classifies reactive amines, provides accurate masses and MS/MS spectra, and quantifies intensities. We identified 2707 high-quality features with primary and/or secondary amines in SRSRMs and 75% of them having an m/z < 300. Across all three SRSRMs, 327 amino features were detected, while 856, 794, and 200 unique features were found in SRNOM, SRHA, and SRFA, respectively. In North Saskatchewan River (NSR) samples, a total of 6449 amino features were detected, 818 of them matched those in SRSRMs, and 87% of them were different between the two rivers. Using chemical standards, we confirmed 10 compounds and tentatively identified 5 more. This study highlights similarities and differences in reactive N-precursors in SRSRMs and local river water, enhancing the understanding of geo-differences in reactive N-precursors in different source waters.

Relativistic SZ temperatures and hydrostatic mass bias for massive clusters in the FLAMINGO simulations

ABSTRACT The relativistic Sunyaev-Zel’dovich (SZ) effect can be used to measure intracluster gas temperatures independently of X-ray spectroscopy. Here, we use the large-volume FLAMINGO simulation suite to determine whether SZ y-weighted temperatures lead to more accurate hydrostatic mass estimates in massive ($M_{\rm 500c} \gt 7.5\times 10^{14}\, {\rm M}_{\odot }$) clusters than when using X-ray spectroscopic-like temperatures. We find this to be the case, on average. The median bias in the SZ mass at redshift zero is $\left\langle b \right\rangle \equiv 1-\left\langle M_{\rm 500c,hse}/M_{\rm 500c,true} \right\rangle = -0.05 \pm 0.01$, over 4 times smaller in magnitude than the X-ray spectroscopic-like case, $\left\langle b \right\rangle = 0.22 \pm 0.01$. However, the scatter in the SZ bias, $\sigma _{b} \approx 0.2$, is around 40 per cent larger than for the X-ray case. We show that this difference is strongly affected by clusters with large pressure fluctuations, as expected from shocks in ongoing mergers. Selecting the clusters with the best-fitting generalized NFW pressure profiles, the median SZ bias almost vanishes, $\left\langle b \right\rangle = -0.009 \pm 0.005$, and the scatter is halved to $\sigma _{b} \approx 0.1$. We study the origin of the SZ/X-ray difference and find that, at $R_{\rm 500c}$ and in the outskirts, SZ weighted gas better reflects the hot, hydrostatic atmosphere than the X-ray weighted gas. The SZ/X-ray temperature ratio increases with radius, a result we find to be insensitive to variations in baryonic physics, cosmology, and numerical resolution.

Targeted quantification of per- and polyfluoroalkyl substances (PFASs) in livestock serum by liquid chromatography-high-resolution mass spectrometry.

Per- and polyfluoroalkyl substances (PFASs) have attracted increasing attention due to their persistence in the environment and potential adverse effects on human and animal health. The detection and quantification of PFASs in livestock could substantially contribute to monitoring their presence within the food chain. We developed a targeted quantification method for 34 PFASs in livestock serum by liquid chromatography-high-resolution mass spectrometry (LC-HRMS). We used protein precipitation for serum sample extraction and accurate mass measurement of targeted PFAS compounds for quantification. We validated the method with various analytical parameters, achieving accuracy of 70-120% and precision of <20%. The method also demonstrated good analytical sensitivity, with a limit of detection of <0.051 ng/mL and a limit of quantification of <0.175 ng/mL. When applying the developed method to actual serum samples from a variety of livestock, we successfully identified and quantified various PFASs in different livestock species. Our method has the potential to be a valuable tool for veterinary laboratory analysis of PFAS contamination in livestock.

Development of a genotoxicity/carcinogenicity assessment method by DNA adductome analysis

Safety evaluation is essential for the development of chemical substances. Since in vivo safety evaluation tests, such as carcinogenesis tests, require long-term observation using large numbers of experimental animals, it is necessary to develop alternative methods that can predict genotoxicity/carcinogenicity in the short term, taking into account the 3Rs (replacement, reduction, and refinement). We established a prediction model of the hepatotoxicity of chemicals using a DNA adductome, which is a comprehensive analysis of DNA adducts that may be used as an indicator of DNA damage in the liver. An adductome was generated with LC-high-resolution accurate mass spectrometer (HRAM) on liver of rats exposed to various chemicals for 24 h, based on two independent experimental protocols. The resulting adductome dataset obtained from each independent experiment (experiments 1 and 2) and integrated dataset were analyzed by linear discriminant analysis (LDA) and found to correctly classify the chemicals into the following four categories: non-genotoxic/non-hepatocarcinogens (−/−), genotoxic/non-hepatocarcinogens (+/−), non-genotoxic/hepatocarcinogens (−/+), and genotoxic/hepatocarcinogens (+/+), based on their genotoxicity/carcinogenicity properties. A prototype model for predicting the genotoxicity/carcinogenicity of the chemicals was established using machine learning methods (using random forest algorithm). When the prototype genotoxicity/carcinogenicity prediction model was used to make predictions for experiments 1 and 2 as well as the integrated dataset, the correct response rates were 89 % (genotoxicity), 94 % (carcinogenicity) and 87 % (genotoxicity/carcinogenicity) for experiment 1, 47 % (genotoxicity), 62 % (carcinogenicity) and 42 % (genotoxicity/carcinogenicity) for experiment 2, and 52 % (genotoxicity), 62 % (carcinogenicity), and 48 % (genotoxicity/carcinogenicity) for the integrated dataset. To improve the accuracy of the toxicity prediction model, the toxicity label was reconstructed as follows; Pattern 1: when +/+ and −/− chemicals were used from the toxicity labels +/+, +/−, −/+ and −/−; and Pattern 2: when +/+, +/−, and −/+ other than −/− were replaced with the label "Others". As a result, chemicals with only +/+ and −/− toxicity labels were used and the correct response rates were approximately 100 % for the measured data in experiment 1, 53 %–66 % for the data in experiment 2, and 59–73 % for the integrated data, all of which were 10 %–30 % higher compared with the data before the label change. In contrast, when the toxicity labels were replaced with −/− and “Others”, they reached nearly 100 % in the measured data from experiment 1, 65 %–75 % in the data from experiment 2, and 70 %–78 % in the integrated data, all of which were 10 %–50 % higher compared with the data before the label change.

Determination of Anthropogenic Pollutants in Adipose Tissue of the Caspian Seal Pusa caspica Gmelin, 1788 by High-Resolution Accurate Mass Spectrometry.

Tissue contamination with persistent organic pollutants (POPs) in organisms proved possible to comprehensively characterize in a single test by combining gas chromatography and high-resolution accurate mass spectrometry. Adipose tissue samples were collected from two Caspian seals (Pusa caspica Gmelin, 1788) found dead on the Caspian Sea shore in 2020. Organochlorine pesticides, primarily DDT and HCH, and polychlorinated biphenyls (PCBs) were major pollutants found in the Caspian seals. The distribution of metabolites indicated the absence of recent pesticide use. The PCB content was relatively high, but still at the lower limit of the range of values determined previously, as was also the case with pesticides. Chlordanes, polychlorinated naphthalenes, and polybrominated diphenyl ethers were detected in minor quantities and were therefore not considered to be major pollutants of the Caspian seal. The pollutant levels were below a threshold at which a distinct effect on seal health can be expected. High-resolution accurate mass (HRAM) spectrometry was found to provide a convenient tool for both targeted and nontargeted analyses of a wide range of organic pollutants in a single experiment.

Addressing Mass Conservation in Two-Dimensional Modeling of Lithium Metal Batteries with Electrochemically Plated/Stripped Interfaces

Several previous studies have aimed to develop mathematical models based on the moving boundary approach for predicting changes in lithium morphology during the cycling of lithium metal batteries under different operating conditions. These modeling frameworks play a crucial role in enhancing our fundamental understanding of the transport and reaction mechanisms and guide experimentalists in developing safer and more efficient lithium metal anodes. In this work, using a simple two-dimensional model for the lithium metal battery, we aim to bring attention to the bulk convection in the liquid electrolytes induced by the movement of the lithium metal surface modeled as a moving boundary. The back-and-forth motion of the lithium metal surface during the plating and stripping of lithium introduces a weak fluid motion in the liquid electrolyte that should be incorporated in the model equations and corresponding boundary conditions (Figure 1). The results for the electrochemical signatures and morphology evolution thus obtained by solving a coupled fluid model are compared with the case where the velocity distribution in the liquid electrolyte is ignored. This work extends our previously reported perspective on the convective flux correction to rectify the mass conservation failures at moving boundaries in one-dimensional models to two dimensions. This careful implementation of the correct boundary conditions ensures the mass conservation of lithium in two-dimensional simulations for predicting the morphological evolution of lithium metal electrodes over cycles. These revised battery models with accurate mass and charge conservation are crucial for predicting the performance of the battery over cycles. Additionally, these relative fluxes at the moving and fixed boundaries are sometimes ignored by assuming a bulk concentration condition at the far end, especially at the cathode/separator interface. While it may not affect overpotential signatures at the anode, it leads to mass conservation issues with implications for the accuracy of cycling simulations. Thus, with the help of a two-dimensional electro-convection model, this study addresses the role of bulk convection in liquid electrolytes and the importance of ensuring mass conservation in lithium metal battery models.

Identification of Fatty Acids, Amides and Cinnamic Acid Derivatives in Supercritical-CO2 Extracts of Cinnamomum tamala Leaves Using UPLC-Q-TOF-MSE Combined with Chemometrics.

Cinnamomum tamala leaf (CTL), also known as Indian bay leaf, is used all over the world for seasoning, flavoring, and medicinal purposes. These characteristics could be explained by the presence of several essential bioactive substances and lipid derivatives. In this work, rapid screening and identification of the chemical compounds in supercritical (SC)-CO2 extracts of CTL by use of UPLC-Q-TOF-MSE with a multivariate statistical analysis approach was established in both negative and positive mode. A total of 166 metabolites, including 66 monocarboxylic fatty acids, 52 dicarboxylic fatty acids, 27 fatty acid amides, and 21 cinnamic acid derivatives, were tentatively identified based on accurate mass and the mass spectrometric fragmentation pattern, out of which 142 compounds were common in all SC-CO2 extracts of CTL. Further, PCA and cluster hierarchical analysis clearly discriminated the chemical profile of analyzed extracts and allowed the selection of SC-CO2 extract rich in fatty acids, fatty acid amides, and other bioactive constituents. The result showed that the higher number of compounds was detected in CTL4 (300 bar/55 °C) extract than the other CTL extracts. The mono- and di-carboxylic fatty acids, fatty acid amides, and cinnamic acid derivatives were identified in CTL for the first time. UPLC-Q-TOF-MSE combined with chemometric analysis is a powerful method to rapidly screen the metabolite profiling to justify the quality of CTL as a flavoring agent and in functional foods.

Charge Detection Mass Spectrometry Reveals Conformational Heterogeneity in Megadalton-Sized Monoclonal Antibody Aggregates.

Aggregation of protein-based therapeutics can occur during development, production, or storage and can lead to loss of efficacy and potential toxicity. Native mass spectrometry of a covalently linked pentameric monoclonal antibody complex with a mass of ∼800 kDa reveals several distinct conformations, smaller complexes, and abundant higher-order aggregates of the pentameric species. Charge detection mass spectrometry (CDMS) reveals individual oligomers up to the pentamer mAb trimer (15 individual mAb molecules; ∼2.4 MDa) whereas intermediate aggregates composed of 6-9 mAb molecules and aggregates larger than the pentameric dimer (1.6 MDa) were not detected/resolved by standard mass spectrometry, size exclusion chromatography (SEC), capillary electrophoresis (CE-SDS), or by mass photometry. Conventional quadrupole time-of-flight mass spectrometry (QTOF MS), mass photometry, SEC, and CE-SDS did not resolve partially or more fully unfolded conformations of each oligomer that were readily identified using CDMS by their significantly higher extents of charging. Trends in the charge-state distributions of individual oligomers provides detailed insight into how the structures of compact and elongated mAb aggregates change as a function of aggregate size. These results demonstrate the advantages of CDMS for obtaining accurate masses and information about the conformations of large antibody aggregates despite extensive overlapping m/z values. These results open up the ability to investigate structural changes that occur in small, soluble oligomers during the earliest stages of aggregation for antibodies or other proteins.

Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Applications for Metabolomics.

Metabolomics is an interdisciplinary field that aims to study all metabolites < 1500 Da that are ubiquitously found within all organisms. Metabolomics is experiencing exponential growth and commonly relies on high-resolution mass spectrometry (HRMS). Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a form of HRMS that is particularly well suited for metabolomics research due to its exceptionally high resolution (105-106) and sensitivity with a mass accuracy in parts per billion (ppb). In this regard, FT-ICR-MS can provide valuable insights into the metabolomics analysis of complex biological systems due to unique capabilities such as the easy separation of isobaric and isomeric species, isotopic fine structure analysis, spatial resolution of metabolites in cells and tissues, and a high confidence (<1 ppm mass error) in metabolite identification. Alternatively, the large and complex data sets, long acquisition times, high cost, and limited access mainly through national mass spectrometry facilities may impede the routine adoption of FT-ICR-MS by metabolomics researchers. This review examines recent applications of FT-ICR-MS metabolomics in the search for clinical and non-human biomarkers; for the analysis of food, beverage, and environmental samples; and for the high-resolution imaging of tissues and other biological samples. We provide recent examples of metabolomics studies that highlight the advantages of FT-ICR-MS for the detailed and reliable characterization of the metabolome. Additionally, we offer some practical considerations for implementing FT-ICR-MS into a research program by providing a list of FT-ICR-MS facilities and by identifying different high-throughput interfaces, varieties of sample types, analysis methods (e.g., van Krevelen diagrams, Kendrick mass defect plot, etc.), and sample preparation and handling protocols used in FT-ICR-MS experiments. Overall, FT-ICR-MS holds great promise as a vital research tool for advancing metabolomics investigations.