ACS Measurement Science Au最新文献

Microbial detection techniques, such as bacterial counting, are essential in all aspects of environmental monitoring and analysis. However, the standard plate count method for bacterial enumeration with colony-forming units is time-consuming and labor-intensive. In this study, we present a fast and accurate method to count bacteria cells using the technique of time-domain reflectometry (TDR) based on the electrical properties of bacterial cell suspensions. A series of suspensions with various bacterial concentrations were used as the test materials, and the electrical conductivity (σ_a) was determined using the TDR method. The TDR measured-σ_a value was converted to the concentration of bacterial suspension using a pre-established standard curve on three types of bacteria, i.e., Bacillus subtilis (B. subtilis), Pseudomonas fluorescens (P. fluorescens), and Escherichia coli (E. coli). The σ_a values of suspensions increased exponentially with bacteria concentrations, mainly due to the release of Cl^- and extracellular polymeric substances from the cells that were electrically conductive. For the three types of bacterial strains, the lower detection limits were 6 log CFU mL^-1 for B. subtilis, and 7 log CFU mL^-1 for P. fluorescens and E. coli. Independent evaluation showed that values from the TDR based method matched well with those obtained with the traditional plate count method, with RMSEs of 0.260, 0.166, and 0.198 log CFU mL^-1 for B. subtilis, P. fluorescens, and E. coli, respectively. The TDR based approach provides a fast and accurate means for detecting bacterial cell numbers in suspensions.

The reliability of the Tauc plot method for estimating a material's optical bandgap critically depends on the accurate quantification of its absorption coefficient (α), defined as the path length-normalized absorbance. This study systematically evaluates and compares three spectroscopic techniques, ultraviolet-visible (UV-vis) spectroscopy, diffuse reflectance spectroscopy (DRS), and integrating sphere-assisted resonance synchronous spectroscopy (ISARS), for their effectiveness in determining the absorption coefficient spectrum used in Tauc plot-based bandgap analysis. For each technique, a generalized mathematical model is developed by parametrizing the measured spectral signal as a functional expression of the sample's optical properties and experimental conditions. These models provide a conceptual framework under which the measured spectra can reliably approximate the true absorption coefficient spectrum, particularly for materials with diverse optical behaviors. UV-vis spectroscopy is found to have highly limited applicability and is suitable only in rare cases where samples are free from scattering and fluorescence interference. While DRS and ISARS yield comparable accuracy for nonfluorescent solids, DRS is constrained by its sensitivity to fluorescence artifacts and its restriction to solid-state samples. In contrast, ISARS consistently outperforms both methods: it is effective for both solid- and solution-phase samples, demonstrates strong resilience against scattering and fluorescence interference, and requires minimal sample preparation. Importantly, ISARS can be readily implemented by using a standard commercial spectrofluorometer equipped with an integrating sphere, making it both practical and accessible. Given its superior accuracy, broad applicability, and ease of use, ISARS stands out as a robust and versatile technique for precise bandgap characterization, offering significant promise for accelerating the discovery and development of photoactive materials.

Chemometrics associated with advanced analytical separation methods are crucial for the chemical profiling of complex samples, such as bio-oil, enabling more accurate and efficient identification of differential features. The composition of bio-oils influences the selection of pretreatment methods for fuel production, which may include processes such as filtration, guard bed usage, or reactions such as hydrothermal liquefaction and esterification. This study focuses on the chemical profiling of pyrolytic bio-oils from sugar cane bagasse and straw using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS). Chemometric approaches such as tile-based Fisher ratio analysis (FRA) and principal component analysis (PCA) are employed for the feature selection of class-differentiating analytes. Bio-oils from both feedstocks exhibited chromatographic profiles with subtle differences, which were observed in the composition and relative abundance of specific compound classes. Bagasse bio-oil was rich in phenolics and hexose derivatives, such as furans and aldehydes. In contrast, straw bio-oil presented a higher abundance of hydrocarbons and fatty acid methyl esters. Tile-based FRA enabled the identification of 16 differential features and the detection of low-intensity compounds, such as long-chain esters and hydrocarbons, not previously detected by the peak table-based approach. PCA based on these differential features explained 98.7% of the total variance (PC1 + PC2), clearly grouping bio-oils by feedstock origin. The findings highlight the potential of GC×GC-TOFMS and chemometrics for differentiating bio-oils, demonstrating the importance of advanced analytical techniques in studying biomass conversion processes and characterizing bioproducts.

Results of efforts to diagnose infections with SARS-CoV-2 using a sampling method that was less invasive than the nasopharyngeal swab led to the rapid adoption of anterior nasal swabs. Saliva was also shown to have potential as a sample matrix and, like anterior nasal swabs, could be obtained noninvasively (e.g., passive drool). However, due to its inherent complexity and heterogeneity across patient populations (e.g., presence of mucins and RNases), saliva was largely disregarded as point-of-care diagnostics were being developed and broadly implemented. For molecular diagnostic approaches (e.g., RT-PCR or RT-LAMP), these matrix effects from saliva could lead to undesirable false positives or false negatives. The opportunity to address these challenges by normalizing the performance of saliva could enable important applications of molecular tests, particularly at the point-of-care. Toward these goals, we developed a one-pot RT-LAMP assay for the colorimetric detection of SARS-CoV-2 from saliva samples. The assay is performed in five steps: (i) a patient collects a passive saliva sample, (ii) the sample is placed on a heat block for 10 min at 95 °C, (iii) the undiluted sample is added to the one-pot RT-LAMP assay, (iv) the RT-LAMP reaction tube is place on a heat block for 40 min at 65 °C, and, (v) immediately postamplification, the reaction tube is inverted to observe the colorimetric output. We demonstrated the clinical performance of our assay using a panel of 127 patient samples. Our assay had an overall accuracy of 98%, with a sensitivity of 88% and a specificity of 100%. These results indicate excellent diagnostic agreement with the gold standard, RT-PCR, and highlight the potential to improve the clinical utility of saliva for point-of-care (e.g., mobile clinics) testing of SARS-CoV-2 and other upper respiratory viruses and emerging pathogens.

Ubiquitously distributed in the environment, food supply, and consumer products, endocrine-disrupting chemicals (EDCs) are exogenous substances that disrupt hormonal activities in the endocrine system. Increasing evidence suggests that women with reproductive disorders tend to accumulate higher levels of EDCs, such as phthalates and parabens, in ovarian follicular fluid. However, most existing studies focus on the measurements of a limited number of prevalent EDCs, overlooking chemicals and metabolites that are not known or prioritized. To address the knowledge gap, we developed a non-targeted analysis (NTA) workflow for broader EDC detection in follicular fluid samples using liquid chromatography-high-resolution mass spectrometry (LC-HRMS). By taking advantage of the higher-energy collisional dissociation (HCD) in the Orbitrap mass spectrometer, we first identified up to 17 characteristic product ions for parabens and their metabolites. Compared to conventional mass spectral matching via online databases and in silico fragmentation algorithms, paraben precursor ion prioritization through such diagnostic fragment ion extraction achieved more accurate compound identification at concentrations as low as 1 ng/mL. To extend the chemical coverage beyond known fragmentation patterns, we also assessed mass spectral library search via Compound Discoverer software, along with retention time model predictions. As a proof-of-concept application, the entire workflow was applied to a pooled follicular fluid sample collected from 211 Canadian patients receiving fertility treatment. Our compound identification results revealed that parabens could undergo several possible metabolic pathways, including hydrolysis, hydroxylation, sulfation, and amino acid conjugation. Furthermore, a total of 14 compounds were identified with level 1 confidence, including EDCs and their metabolites such as monophthalates, UV filters, and phenolic acids. The underlying implications of reproductive health associated with these substances are an area for future study.

Measuring surface functional groups (FGs) on nanomaterials (NMs) is essential for designing dispersible and stable NMs with tailored and predictable functionality. FG screening and quantification also plays a critical role for subsequent processing steps, NM long-term stability, quality control of NM production, and risk assessment studies and enables the implementation of sustainable and safe-(r)-by-design concepts. This calls for simple and cost-efficient methods for broadly utilized FGs that can be ideally automated to speed up FG screening, monitoring, and quantification. To expand our NM surface analysis toolbox, focusing on simple methods and broadly available, cost-efficient instrumentation, we explored a NM-adapted pH titration method with potentiometric and optical readout for measuring the total number of (de)-protonable FGs on representatively chosen commercial and custom-made aminated silica nanoparticles (SiO₂ NPs). The accuracy and robustness of our stepwise optimized workflows was assessed by several operators in two laboratories and method validation was done by cross-comparison with two analytical methods relying on different signal generation principles. This included traceable, chemo-selective quantitative nuclear magnetic resonance spectroscopy (qNMR) and thermogravimetric analysis (TGA), providing the amounts of amino silanes released by particle dissolution and the total mass of the surface coatings. A comparison of the potentiometric titration results with the reporter-specific amounts of surface amino FGs determined with the previously automated fluorescamine (Fluram) assay highlights the importance of determining both quantities for surface-functionalized NMs. In the future, combined NM surface analysis with optical assays and pH titration will simplify quality control of NM production processes and stability studies and can yield large data sets for NM grouping that facilitates further developments in regulation and standardization.

Human milk oligosaccharides (HMOs) are a biologically important class of carbohydrates responsible for promoting the healthy development of infants. However, to better understand their specific biological roles, analytical techniques are needed to unambiguously characterize them. While liquid chromatography-tandem mass spectrometry (LC-MS/MS) remains the gold standard for HMO analysis, new orthogonal techniques are desired for improving their isomer analysis. Ion mobility spectrometry-mass spectrometry (IMS-MS) has emerged as a complementary technique to LC-MS/MS but has seen little use toward HMO sequencing analysis beyond the construction of collision cross section (CCS) databases. In this work, we describe the use of collision-induced dissociation performed prior to high-resolution cyclic ion mobility separations (i.e., pre-cIMS CID) in conjunction with CCS measurements to characterize the linkage positioning in various HMOs irrespective of the starting precursor ion. We then demonstrated how our developed approach could be used to sequence an unknown HMO present in a purified extract. Lastly, we applied our workflow to sequence an isomeric mixture in the same extract using cIMS/cIMS instead of pre-cIMS CID. Overall, our developed approach is a first step toward standard-free de novo HMO sequencing as well as being a complementary and orthogonal method to existing LC-MS/MS-based workflows.

Accurate and rapid analysis of chirality is crucial for understanding biological processes and molecular interactions, yet traditional vibrational circular dichroism (VCD) techniques are limited by long acquisition times and low throughput. We present a quantum cascade laser (QCL)-based VCD system that integrates a photoelastic modulator (PEM) with pulsed laser sources, using precise temporal synchronization and a novel calibration method based on Welch's power spectral density analysis. This hardware-software integration enables real-time demodulation without the need for conventional lock-in amplifiers and achieves accurate, high-SNR VCD spectra of α-pinene (±) mixtures with high reproducibility. Real-time enantiomeric excess determination is achieved with a 10× improvement in speed and a 5× enhancement in SNR compared to conventional VCD methods. These advancements pave the way for high-throughput and nondestructive chiral analysis, with potential applications in biosensing, structural biology, and pharmaceutical research.

In order to increase the lifetime of polymer electrolyte membrane (PEM) fuel cells (PEMFCs) and water electrolyzers (PEMWEs), understanding local degeneration processes in membrane electrode assemblies (MEAs) is crucial. By a combination of scanning electrochemical microscopy (SECM) with a flow-through diffusion cell (DiffC-DC-SECM) and ferrocyanide and protons as redox mediators, a spatially resolved analytical method was developed that can differentiate between different functional and structural degeneration phenomena in the aging process of a membrane. An SECM scan at cathodic potential detects the diffusion of protons through the membrane and thus its through-plane proton conductivity, while a second SECM scan at anodic potential visualizes the diffusion of the iron complex through the membrane, thus perforating structural damage such as cracks and holes. The method was successfully validated for the spatially resolved differentiation of membrane damage in pristine PEMs and catalyst-coated membranes (CCMs) with artificial holes, chemically aged CCMs, and MEAs in fully assembled operational PEMFCs aged by an open-circuit voltage membrane accelerated stress test. DiffC-DC-SECM thus provides a powerful technique with high local resolution for membrane integrity testing under realistic operation conditions to develop long-term durable materials for PEMFCs and PEMWEs.

The contamination of natural basins by agricultural or industrial activities, and the growing need for potable water due to climate changes accelerate the drive to find versatile, fast, practical, and easy-to-use methods for water analysis. A potentially versatile technique suitable for water analysis is Raman Spectroscopy (RS). Featured by good resolution but low sensitivity, RS detects molecular vibrational modes of an analyte in water. Nitrate is an indicator of chemical and/or biological pollution, it displays Raman active vibrational modes affected by the interaction with other systems in solution, allowing a wide range of applications. Concerning Nitrate analysis in water, a general introduction to the Raman effect and the basic instrumentation were herein discussed. RS is a potential solution to wastewater analysis. This review first reports the theoretical background of the technique and its basic working principles, then, the state-of-the-art scientific contributions related to Nitrate detection are investigated with a particular interest in the instrumental setup and the chemometric techniques employed to improve its sensitivity. In the studies hereby considered, instrumental setup (for example, laser frequency, laser power, acquisition times) and different technical solutions (for example, micro- versus macro-Raman instruments) to increase the technique’s sensitivity on Nitrate detection are described. Concisely, the use of deep-UV lasers, optically active Surface-Enhanced Raman Spectroscopy (SERS) or Fiber-Enhanced Raman spectroscopy (FERS) equipment, coupled with instrumental settings, i.e. acquisition time, variable temperature of acquisition, use of special sampling apparatus (cuvettes or immersion probes), or with ion exchange resins for analyte enrichment, have been reported. Remarkably, examples of large data correction of unwanted fluorescence by mathematical processing or chemical quenching were reported too, suggesting solutions for the Raman analysis of wastewaters. Finally, a short digression on Machine Learning (ML) applied to RS is proposed, showing the promising results reported in other fields. Data-driven methods could be a solution to improve the low sensitivity of the RS for Nitrate detection. Hence, an approach of ML methods for the typical RS spectra processing (spike removal, baseline correction, fluorescence curve elimination, instrumental noise correction) was hereby mentioned, suggesting an improvement in the detection capability of Nitrate ion in water.