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A system for sampling a liquid includes a sample fluid conduit including a membrane having pores. The membrane prevents the passage of the sample liquid through the pores at a first pressure of the sample liquid in the sample fluid conduit. A surface sampling capture probe has a distal end. The capture probe includes a solvent supply conduit and a solvent exhaust conduit. A solvent composition flowing at the distal end of the capture probe establishes a liquid junction with the membrane and establishes a second pressure within the liquid junction at the membrane. The second pressure is lower than the first pressure. Sample liquid will be drawn through the pores of the membrane by the second pressure at the liquid junction. A method for sampling a liquid and for performing chemical analysis on a liquid are also disclosed.
The solid-electrolyte interphase (SEI) enables the remarkable capacity retention of lithium-ion batteries, yet a comprehensive quantitative description of the SEI composition remains elusive. Using a combination of differential electrochemical mass spectrometry and mass spectrometry titration, we quantify graphite SEI components formed under electrolytes of varying salt concentrations. We find that, regardless of salt concentration, a conversion of initially deposited lithium ethylene dicarbonate to monocarbonates (likely lithium ethylene monocarbonate) and noncarbonate species occurs, and the extent of this conversion increases with electrolyte aging. Here, we additionally demonstrate that as the concentration increases (up to 2.0 M LiPF6), the SEI becomes progressively thinner with more LiF and less solid carbonates deposited. Finally, we reveal that less dead lithium formation and less solid carbonate deposition occur during prolonged fast charging for higher-concentration electrolytes. Because of the advantages imparted by a thinner SEI, the onset state of charge for lithium plating for the 2.0 M electrolyte is later than that predicted by a standard electrochemical model, underscoring the importance of explicit SEI effects in future electrochemical models.
The ability to improve the data quality of ion mobility–mass spectrometry (IM-MS) measurements is of great importance for enabling modular and efficient computational workflows and gaining better qualitative and quantitative insights from complex biological and environmental samples. We developed the PNNL PreProcessor, a standalone and user-friendly software housing various algorithmic implementations to generate new MS-files with enhanced signal quality and in the same instrument format. Different experimental approaches are supported for IM-MS based on Drift-Tube (DT) and Structures for Lossless Ion Manipulations (SLIM), including liquid chromatography (LC) and infusion analyses. The algorithms extend the dynamic range of the detection system, while reducing file sizes for faster and memory-efficient downstream processing. Specifically, multidimensional smoothing improves peak shapes of poorly defined low-abundance signals, and saturation repair reconstructs the intensity profile of high-abundance peaks from various analyte types. Further, other functionalities are data compression and interpolation, IM demultiplexing, noise filtering by low intensity threshold and spike removal, and exporting of acquisition metadata. Several advantages of the tool are illustrated, including an increase of 19.4% in lipid annotations and a two-times faster processing of LC-DT IM-MS data-independent acquisition spectra from a complex lipid extract of a standard human plasma sample. The software is freely available at https://omics.pnl.gov/software/pnnl-preprocessor.
Human disease states are biomolecularly multifaceted and can span across phenotypic states, therefore it is important to understand diseases on all levels, across cell types, and within and across microanatomical tissue compartments. To obtain an accurate and representative view of the molecular landscape within human lungs, this fragile tissue must be inflated and embedded to maintain spatial fidelity of the location of molecules and minimize molecular degradation for molecular imaging experiments. Here, we evaluated agarose inflation and carboxymethyl cellulose embedding media and determined effective tissue preparation protocols for performing bulk and spatial mass spectrometry-based omics measurements. Mass spectrometry imaging methods were optimized to boost the number of annotatable molecules in agarose inflated lung samples. This optimized protocol permitted the observation of unique lipid distributions within several airway regions in the lung tissue block. Laser capture microdissection of these airway regions followed by high-resolution proteomic analysis allowed us to begin linking the lipidome with the proteome in a spatially resolved manner, where we observed proteins with high abundance specifically localized to the airway regions. We also compared our mass spectrometry results to lung tissue samples preserved using two other inflation/embedding media, but we identified several pitfalls with the sample preparation steps using this preservation method. Overall, we demonstrated the versatility of the inflation method, and we can start to reveal how the metabolome, lipidome, and proteome are connected spatially in human lungs and across disease states through a variety of different experiments.
Pyrolysis of waste plastics can produce a product mixture with a high concentration of olefins (>50 wt%). The olefins, as building blocks in the petroleum industry, are potential precursors for valuable commodity chemicals with higher values (>$2000 per ton). In this work, we produce aldehydes by hydroformylation of the olefins present in pyrolysis oil from colored post-consumer recycled high-density polyethylene (PCR-HDPE). The obtained aldehydes in the oil are then converted into aliphatic amines via reductive amination with a Ru/C catalyst. The aminated oil was characterized by multiple analytical chemistry techniques including elemental analysis, nuclear magnetic resonance spectroscopy, high-resolution liquid chromatography-mass spectrometry, and gas chromatography-mass spectrometry with a Polyarc flame ionization detector. The concentration of metals in the PCR-HDPE and oil changes during the tandem processes, showing limited effects of these elements (e.g., Al, Ca, Fe, Mg, Ti, Zn) on hydroformylation and reductive amination. Additionally, we demonstrated that reductive amination of aldehydes with varied carbon numbers and branching properties can be achieved in the presence of a complex mixture, including paraffins and aromatics. The results indicate that waste plastics have the potential to serve as a renewable source for mono and di aliphatic amines, thereby diminishing reliance on fossil feedstocks as the current primary amine source.
The LLNL “Microbes Persist” Soil Microbiome Scientific Focus Area (SFA) seeks to determine how microbial soil ecophysiology, population dynamics, and microbe-mineral-organic matter interactions regulate the persistence of microbial residues and formation of soil carbon (C). In much of our research, we use imaging, mass spectrometry, and related methods to study plantmicrobe-mineral interactions, viral and microbial particles from soil, and the signatures plant and microbial necromass contribute as part of soil organic matter (SOM). Our project’s signature techniques include NanoSIMS-enabled approaches (to understand cell-cell and OM-mineral interactions at the single cell and even viral particle scale), radiocarbon (14C) analyses (to determine both the age and turnover time of soil organic matter), and a suite of soil chemical characterization techniques we describe below, and collectively refer to as ‘Multi- dimensional SOM-mineral characterization’ (SEM, TEM, STXM, NEXAFS, NMR, FTICR-MS, LC-MS). In this report, we focus on how imaging, NMR, beamline and mass spectrometry approaches can deepen our understanding of soil microbiomes and their engagement with the soil matrix.
Here, we present the results of a study to generate reference glasses that reflect an environment analogous to historic nuclear fallout samples of interest for post-detonation nuclear forensics. The glasses were generated by melting and then quenching SiO 2 , Al 2 O 3 and CaCO 3 powders. Two suites of glasses with three distinct U isotopic ratios were successfully made with enrichments in the 235 U isotope (~ natural [0.72%], ~ 53% and 94%), but the bulk elemental data showed heterogeneity (~ 10% RSD) with U mass fractions ranging from 331.47 to 373.63 μg g -1 . Spatially resolved U isotopic measurements were performed using three mass spectrometry techniques (secondary ion mass spectrometry-single stage accelerator mass spectrometry [SIMS-SSAMS], large geometry [LG] - SIMS, and laser ablation-inductively coupled plasma-mass spectrometry [LA-ICP-MS]) across five National Laboratories. The results showed good agreement with the bulk U isotopic data for the low, medium, and high U mass fractions. We conclude that despite elemental heterogeneity, these samples can serve as useful working reference materials for spatially resolved nuclear fallout analyses, as well as for other related spatially resolved analyses.
Identification of compounds with minimal ambiguity remains a central challenge in mass spectrometry-based metabolomics. Conventional compound identification relies on comparing analytical signatures (e.g., mass-to-charge ratio, collision cross section, tandem mass spectra) against reference data obtained from measurements of authentic chemical standards. The breadth of annotatable compounds using this approach is necessarily limited by availability of authentic standards, analytical throughput, and resolving power of the separations that underly the measurements. The maturation of computational methods, both theory-driven and artificial intelligence/machine learning-based, for prediction of various molecular properties relevant to multidimensional mass spectrometry measurements has opened the door to a new “reference-free” paradigm of compound annotation. Through augmenting existing reference data for molecular properties with computational predictions, the universe of identifiable chemical species can be expanded significantly beyond its current limits. An unexplored aspect of this novel approach is understanding how to gauge confidence in resulting annotations, especially as the compound search space is expanded. Intuitively, the confidence of a compound annotation is related to the inherent discriminatory power of the molecular properties used for identification, as well as the precision with which the properties are measured or predicted. In this work, we characterize this relationship between measurement precision and identification probability in a systematic and quantitative fashion for a defined region of chemical space that includes organic small molecule metabolites. Importantly, this work establishes a framework for conducting metabolite identification probability analysis that enables others to quantify this relationship for their own compounds and properties of interest.
Protein phosphorylation is a critical post-translational modification (PTM), with cell signaling networks being tightly regulated by protein phosphorylation. Despite recent technological advances in reversed-phase liquid chromatography (RPLC)-mass spectrometry (MS)-based proteomics, comprehensive phosphoproteomic coverage in complex biological systems remains challenging, especially for hydrophilic phosphopeptides that often have multiple phosphorylation sites. Herein we describe an MS-based phosphoproteomics protocol for effective quantitative analysis of hydrophilic phosphopeptides. This protocol was built upon a simple tandem mass tag (TMT)-labeling method for significantly increasing peptide hydrophobicity, thus effectively enhancing RPLC-MS analysis of hydrophilic peptides. Through phosphoproteomic analyses of MCF7 cells, this method was demonstrated to greatly increase the number of identified hydrophilic phosphopeptides and improve MS signal detection. With the TMT labeling method, we were able to identify a previously unreported phosphopeptide from the G protein-coupled receptor (GPCR) CXCR3, QPpSSSR, which is thought to be important in regulating receptor signaling. This protocol is easy to adopt and implement, and thus should have broad utility for effective RPLC-MS analysis of the hydrophilic phosphoproteome as well as other highly hydrophilic analytes.
Lithium-excess, cation-disordered rocksalt (DRX) materials have been subject to intense scrutiny and development in recent years as potential cathode materials for Li-ion batteries. Despite their compositional flexibility and high initial capacity, they suffer from poorly understood parasitic degradation reactions at the cathode-electrolyte interface. These interfacial degradation reactions deteriorate both the DRX material and electrolyte, ultimately leading to capacity fade and voltage hysteresis during cycling. In this work, differential electrochemical mass spectrometry (DEMS) and titration mass spectrometry are combined to quantify the extent of bulk redox and surface degradation reactions for a set of Mn 2+/4+ -based DRX oxyfluorides during initial cycling with a high-voltage charging cutoff (4.8 V vs Li/Li + ). Increasing the fluorine content from 7.5 to 33.75% is shown to diminish oxygen redox and suppresses high-voltage O 2 evolution from the DRX surface. Additionally, electrolyte degradation processes resulting in the formation of both gaseous species and electrolyte-soluble protic species are observed. Subsequently, DEMS is paired with a fluoride-scavenging additive to demonstrate that increasing fluorine content leads to increased dissolution of fluorine from the DRX material into the electrolyte. Finally, a suite of ex situ spectroscopy techniques (X-ray photoelectron spectroscopy, inductively coupled plasma optical emission spectroscopy, and solid-state nuclear magnetic resonance spectroscopy) are employed to study the change in DRX composition during charging, revealing the dissolution of manganese and fluorine from the DRX material at high voltages. Here this work provides insight into the degradation processes occurring at the DRX-electrolyte interface and points toward potential routes of interfacial stabilization.
Purpose of Review: Precision treatments to address the multifaceted pathophysiology of traumatic brain injury (TBI) are desperately needed, which has led to the intense study of fluid-based protein biomarkers in TBI. Mass Spectrometry (MS) is increasingly being applied to biomarker discovery and quantification in neurological disease to explore the proteome, allowing for more flexibility in biomarker discovery than commonly encountered antibody-based assays. In this narrative review, we will provide specific examples of how MS technology has advanced translational research in traumatic brain injury (TBI) focusing on clinical studies, and looking ahead to promising emerging applications of MS to the field of Neurocritical Care. Recent Findings: Proteomic biomarker discovery using MS technology in human subjects has included the full range of injury severity in TBI, though critically ill patients can offer more options to biofluids given the need for invasive monitoring. Blood, urine, cerebrospinal fluid, brain specimens, and cerebral extracellular fluid have all been sources for analysis. Emerging evidence suggests there are distinct proteomic profiles in radiographic TBI subtypes, and that biomarkers may be used to distinguish patients sustaining TBI from healthy controls. Metabolomics may offer a window into the perturbations of ongoing cerebral insults in critically ill patients after severe TBI. Summary: Emerging MS technologies may offer biomarker discovery and validation opportunities not afforded by conventional means due to its ability to handle the complexities associated with the proteome. In conclusion, while MS techniques are relatively early in development in the neurosciences space, the potential applications to TBI and neurocritical care are likely to accelerate in the coming decade.
Abstract Metabolite identification in non-targeted mass spectrometry-based metabolomics remains a major challenge due to limited spectral library coverage and difficulties in predicting metabolite fragmentation patterns. Here, we introduce Multiplexed Chemical Metabolomics (MCheM), which employs orthogonal post-column derivatization reactions integrated into a unified mass spectrometry data framework. MCheM generates orthogonal structural information that substantially improves metabolite annotation through in silico spectrum matching and open-modification searches, offering a powerful new toolbox for the structure elucidation of unknown metabolites at scale.
Impurity levels were measured in cerium oxide microspheres fabricated by modified internal gelation sol-gel methods. A combination of glow discharge mass spectrometry, electron impact mass spectrometry, combustion gas analysis and instrumental gas analysis were used to assess a wide range of potential elemental impurities. Low concentrations of carbon, nitrogen, and hydrogen impurities in microspheres showed a dependence on how microspheres were washed. Silicon impurities are believed to derive from silicone oil used during sol-gel processing. Spheres washed by the preferred approach had impurity levels below 100 ppm for all elements tested. For applications such as nuclear fuels, sol-gel methods could meet purity specifications as long as metal nitrate feed solutions of sufficient purity are used and microspheres are washed appropriately. In this study, cerium was used as a surrogate for plutonium-238, which is used in radioisotope power systems as a heat source, to determine whether carbon or other impurities were concentrated during internal gelation processing and remained after heat treatments. Analyses indicated low concentrations of impurities in cerium oxide microspheres after sintering steps that were well below documented limits for plutonium-238 oxide fuels. Modified washing methods, combined with a pressurized water treatment, resulted in sintered cerium oxide sol-gel microspheres with low impurity levels.
Abstract. The oxidation of dimethyl sulfide (DMS) in the marine atmosphere represents an important natural source of non-sea-salt sulfate aerosol, but the chemical mechanisms underlying this process remain uncertain. While recent studies have focused on the role of the peroxy radical isomerization channel in DMS oxidation, this work revisits the impact of the other channels (OH addition and OH abstraction followed by bimolecular RO2 reaction) on aerosol formation from DMS. Due to the presence of common intermediate species, the oxidation of dimethyl sulfoxide (DMSO) and dimethyl disulfide (DMDS) can shed light on these two DMS reaction channels; they are also both atmospherically relevant species in their own right. This work examines the OH oxidation of DMSO and DMDS, using chamber experiments monitored by chemical ionization mass spectrometry and aerosol mass spectrometry to study the full range of sulfur-containing products across a range of NO concentrations. The oxidation of both compounds is found to lead to rapid aerosol formation (which does not involve the intermediate formation of SO2), with a substantial fraction (14 %–47 % S yield for DMSO and 5 %–21 % for DMDS) of reacted sulfur ending up in the particle phase and the highest yields observed under elevated NO conditions. Aerosol is observed to consist mainly of sulfate, methanesulfonic acid, and methanesulfinic acid. In the gas phase, the NOx dependence of several products, including SO2 and S2-containing organosulfur species, suggest reaction pathways not included in current mechanisms. Based on the commonalities with the DMS oxidation mechanism, DMSO and DMDS results are used to reconstruct DMS aerosol yields; these reconstructions roughly match DMS aerosol yield measurements from the literature but differ in composition, underscoring remaining uncertainties in sulfur chemistry. This work indicates that both the abstraction and addition channels contribute to rapid aerosol formation from DMS and highlights the need for more study into the fate of small sulfur radical intermediates (e.g., CH3S, CH3SO2, and CH3SO3) that are thought to play central roles in the DMS oxidation mechanism.
We report biological organisms are multifaceted, intricate systems where slight perturbations can result in extensive changes in gene expression, protein abundance and/or activity, and metabolic flux. These changes occur at different timescales, spatially across cells of heterogeneous origins, and within single-cells. Hence, multimodal measurements at the smallest biological scales are necessary to capture dynamic changes in heterogeneous biological systems. Of the analytical techniques used to measure biomolecules, mass spectrometry (MS) has proven to be a powerful option due to its sensitivity, robustness, and flexibility with regard to the breadth of biomolecules that can be analyzed. Recently, many studies have coupled MS to other analytical techniques with the goal of measuring multiple modalities from the same single-cell. It is with these concepts in mind that we focus this review on MS-enabled multiomic measurements at single-cell or near-single- cell resolution.
Microbial cell factories have been extensively engineered to produce free fatty acids (FFAs), key components of crucial nutrients, soaps, industrial chemicals, and fuels. Yet, our ability to control the specificity of microbially synthesized FFAs is still limited for medium-chain fatty acids (MCFAs), which is mainly due to lack of high-throughput approaches for FFA analysis. Here we report a mass spectrometry (MS)-based method for rapid profiling of MCFAs in Saccharomyces cerevisiae by using membrane lipids as a proxy. Specially, matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) MS was used to detect shorter acyl chain phosphatidylcholines from membrane lipids and the m/z peak ratio at 730 and 758 was used as an index for improved MCFA production. This yeast colony-based method can be performed at a rate of ~2 second per sample, which is much faster than the gold standard gas chromatography (GC)-MS (typically > 30 min per sample). To demonstrate the power of this method, we performed site-saturation mutagenesis of the yeast fatty acid synthase and identified 9 mutants that resulted in improved MCFA production relative to the wild-type strain. This colony-based MALDI-ToF MS screening method offers an alternative way for engineering microbial fatty acid compositions in a high-throughput manner.