ACS Measurement Science Au最新文献

Electrical sensing technologies have advanced our ability to infer and evaluate the hydraulic characteristics of porous media that are otherwise inaccessible to direct measurement. Such challenges are particularly prevalent in geo-porous materials such as rocks and soils found in remote regions, harsh environments, or beneath the Earth's surface. Noninvasive sensing and characterization of these materials are indispensable preliminary steps for water-energy nexus activities, including extraction processes (e.g., desalination, groundwater utilization, fossil fuel and geothermal exploration and production) and mitigation efforts (e.g., sediment transport monitoring, contaminant management, and carbon or hydrogen capture, utilization, and storage). These electrical properties are measurable only if the material under investigation possesses an electrical charge and is polarizable. Electrical polarization refers to the relative displacement between positive and negative charges. This raises several critical questions: (i) In what ways can porous media acquire electrical charge and exhibit polarization? (ii) How can their electrical properties be measured both in laboratory and field environments? (iii) What frameworks can be used to interpret the observed electrical properties? (iv) How can we assess the reliability and validity of these interpretations in relation to the hydraulic and physical state of the porous media? This study aims to systematically investigate these questions through a comprehensive synthesis of existing literature and the integration of newly obtained experimental data.

The expanding use of microfluidic droplets and particles across disciplines, from biology to materials science, highlights the need for developing precise characterization methods. Conventional particle characterization based on light scattering typically relies on averaged data from multiple particles, which can lead to inaccuracies due to contamination from larger particles. To overcome this issue, we here present a versatile laser diffraction (LD) system for characterizing individual droplets and particles flowing in a poly-(dimethylsiloxane) (PDMS) microfluidic device. Our system, mounted on a commercial inverted microscope, facilitates the simultaneous estimation of both the diameter and the refractive index of microparticles and droplets of size 20-50 μm. The LD system captures the angular distribution of scattered light from individual droplets as they pass through the PDMS microfluidic channels. Validation experiments were performed using liquid paraffin with varying refractive indices, oil-in-water (O/W) and water-in-oil (W/O) droplets, and size-certified polystyrene beads. Results showed high accuracy, with mean diameter estimation errors under 5% and refractive index estimation errors <0.5%. This adaptable characterization system can be combined with various microfluidic systems for droplet and particle generation, mixing, and sorting, offering broad potential for applications in multiple research domains.

Ex vivo tissue culture can model tissue physiology under well-controlled conditions and is especially promising for understanding the complex mechanisms of the brain. Three-dimensional (3D) printing has immense potential to accelerate microfluidic technology development, especially for ex vivo tissue culture devices where miniaturization is ultimately limited by the physical dimensions of tissue explants. Here we describe the development of a 3D printed microfluidic perfusion device for ex vivo brain slices that utilizes media droplets segmented by oxygen bubbles, a perfusion technique we call "bubble perfusion". Device design considerations are described, including materials property challenges associated with 3D printed plastic, such as wetting behavior and thermal conductivity challenges. Integrating a heated water circulation chamber and media prewarming chambers yielded media droplets delivered to brain slice explants at a temperature of 36.8 ± 0.13 °C, with tissue experiencing a temperature drift of 0.5 ± 0.09 °C over the course of a 60 s media droplet exposure. Murine brain tissue explants containing the suprachiasmatic nucleus (SCN) or entorhinal cortex (EC) were observed to be viable within the perfusion system by fluorescence imaging of intracellular Ca²⁺ flux induced by single-droplet stimulus of 60 mM KCl. Robust Ca²⁺ flux was observed for perfusion experiments lasting up to 12 h, with sequential droplet observations indicating the temporal dynamics of Ca²⁺ responses. End-point propidium iodide staining was used to characterize the health of EC and SCN tissue, with ca. 60% of cells in both regions showing no sign of membrane damage after 12 h of perfusion. The utility of the perfusion system toward pharmacological studies was demonstrated by comparing the Ca²⁺ flux induced by stimulus with 50 μM cannabidiol (CBD) vs 50 μM anandamide (AEA). Interestingly, similar magnitude and temporal dynamics of Ca²⁺ flux were observed for both CBD and AEA stimuli despite differential proposed mechanisms of action with respect to the CB1 receptor. These studies demonstrate the utility of the 3D printed bubble perfusion system toward the study of receptor-binding ligands that induce relatively modest magnitudes of Ca²⁺ flux.

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.