ACS Measurement Science Au最新文献

This study investigates how varying film thickness affects the qualitative identifiability of plastics using optical photothermal infrared spectroscopy (O-PTIR) on examples of common plastics such as polyamide 6 (PA6) and polyethylene terephthalate (PET). The main methodology consists of applying a thin layer of PA6 and PET separately to each substrate, namely, PET to polyethylene (PE) and PA6 to polypropylene (PP), and reducing the thickness of the coatings until the O-PTIR signal of the film is no longer detectable. As expected, the characteristic O-PTIR signal for PA and PET decreased with a decreasing film thickness. However, the results show that the O-PTIR detection limit for the plastics could not be reached, as the characteristic peaks of the substrate plastics are still clearly visible at a layer thickness of approximately 0.18 μm for PET and approximately 0.29 μm for PA6. These are the minimum stable film thicknesses that could be achieved since the selected film production process (drop deposition process) does not allow for thinner layers. As this work is a feasibility study, further factors influencing the O-PTIR measurement of plastic films should be investigated in a subsequent work.

Routine diagnostic practice for cancer and metastasis relies on a time-consuming staining process and the use of antibodies to detect selected molecular markers and is hence limited by a lack of real-time data and the availability of molecular information. Against this background, techniques based on rapid chemical analysis to identify migratory properties are highly desirable. Fourier-Transform Infrared (FTIR) microspectroscopy has a long history in the label-free identification of infrared marker bands for cancer detection. However, it requires extensive postprocessing of the acquired spectra, is of limited suitability for analysis in aqueous environments, and has poor spatial resolution. To overcome these challenges, we are using a new method termed Optical Photothermal Infrared (O-PTIR) spectroscopy to detect local absorption to establish potential IR tumor markers and classification models. We report on experimental outcomes using machine learning and FTIR microspectroscopy for the classification of cells and the analysis of spectral features reflecting cancer and migratory properties, comparing a commercial FTIR microspectrometer to a custom-built O-PTIR instrument dedicated to spectroscopic measurement and imaging in microfluidic channels.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) has become a widely used tool for demonstrating the spatial distribution of biomolecules in tissue. One common issue in this technique that can have an impact on sensitivity and spatial resolution is analyte delocalization, in which the analyte spreads across the tissue or beyond the tissue boundaries, often due to sample handling or matrix application. In this study, we tested several metrics to evaluate delocalization using MALDI-MSI data from mouse brain sections. These metrics included the distance between the centers of mass of the nonzero-intensity pixels of tissue and global, the maximum and mean distances of off-tissue signal from the border, and the total area of background signal. After comparison of these metrics to the data set, a linear combination of area with mean distance was defined as the delocalization score. This score can be tuned depending on the application, and we found that a higher amount of weight on the area worked well in practice. Studying the Pearson correlation coefficients of the delocalization scores across the four groups of mice revealed a strong correlation among the analytes, suggesting that their delocalization patterns behave similarly across all groups.

Nanoplastics (NPs) are emerging contaminants of environmental concern, raising significant alarms due to their prevalence and potential health risks. Unlike larger microplastics, NPs are challenging to detect due to their nanodimensions and the reliance on labor-intensive methods such as nanoparticle tracking analysis (NTA) or scanning electron microscopy (SEM). This underscores the urgent need for rapid and accessible detection methods. To address these challenges, we employed dynamic light scattering (DLS), a widely used technique for measuring nanoparticle sizes, to rapidly quantify NP concentrations and sizes. Using DLS, we demonstrated the prevalence of NPs originating from laboratory-based plastic consumables such as microcentrifuge tubes, cryovials, and Petri dishes. Notably, routine actions, including pipet-tip scraping against plastic labware during sample handling, can introduce NPs into solutions. Moreover, physical or chemical procedures, especially sonication and liquid nitrogen treatment, further exacerbate the NP release. This interfered with experimental outcomes, including skewing of DNA and iron nanoparticle concentrations. Our material analysis revealed that the NPs were made of polystyrene and polypropylene, which correlated to manufacturers' product details. Hence, our study highlights an under-recognized NP source that compromises research integrity while contributing to global NP pollution, thus emphasizing the need for sustainable laboratory practices and robust contamination control.

Accurate pH measurement in microliter volumes is essential for real-time chemical and biological analysis, underpinning modern portable and point-of-care diagnostics. Here, we present a wireless electrochemical pH sensor system based on nanostructured iridium oxide (IrO₂) electrodeposited onto electron-beam-evaporated, photolithographically patterned gold electrodes (EBLG) and integrated with a low-power Bluetooth potentiostat for continuous, real-time pH monitoring. FESEM, Raman, and XPS confirm uniform spherical IrO₂ nanostructures with mixed Ir³⁺/Ir⁴⁺ states that are conducive to potentiometric transduction. The IrO₂/EBLG sensor exhibited near-Nernstian sensitivity (69.7 mV/pH) across a pH range of 2-9, a fast step response (∼10 s), negligible hysteresis during bidirectional cycling, and exceptionally low potential drift (∼0.12-0.28 mV/h over 12 h). It demonstrates high selectivity toward H⁺ ions against common physiological interferences with excellent reproducibility and robust long-term stability. The hand-held module wirelessly streams real-time potential data to a smartphone, enabling accurate pH quantification in microliter-scale biological, food, and environmental samples. Measurements showed strong agreement with a commercial microelectrode pH meter, with no statistically significant difference (p > 0.05; Bland-Altman and paired t-test). Overall, the IrO₂/EBLG platform combines miniaturization, stability, and wireless functionality, offering a reliable and scalable solution for decentralized pH sensing and paving a promising route toward future wearable, field-deployable, and environmental pH monitoring systems.

Chronic stress is a critical public health concern with strong links to both mental and physical health. Excessive stress alters brain architecture and stimulates the adrenal gland to release cortisol, a key biomarker of stress. Conventional cortisol measurements rely on invasive, laboratory-based methods that are unsuitable for real-time, point-of-care testing. This study reports the development of a noninvasive, flexible electrochemical biosensor for the selective detection of cortisol in human sweat and saliva. The sensor was fabricated on a polyethylene terephthalate (PET) substrate coated with indium tin oxide (ITO) and functionalized with gold nanoparticles (AuNPs) to enhance conductivity and surface area. AnteoBind chemistry was employed for oriented immobilization of monoclonal anticortisol antibodies. The device was integrated with a microfluidic platform to enable controlled sweat analysis. Electrochemical measurements demonstrated a linear response over the range of 0.5-150 ng/mL, with a detection limit of 0.58 ng/mL. The sensor retained 80% of its signal over 30 days, with reproducibility across batches (RSD ∼ 3.2%). Real sample analysis showed recovery values of 95.5-104% in sweat and saliva samples. These findings underscore the potential of this biosensor for noninvasive monitoring of stress markers in wearable applications, supporting early stress detection and personalized health management.

Advances in high-throughput mass spectrometry have shifted the bottleneck in plasma proteomics from data acquisition to sample preparation. While enrichment and depletion strategies enable detection of low-abundance proteins, their complexity and cost limit scalability and clinical translation. Targeting midto-high abundance proteins from neat plasma offers a practical, reproducible alternative aligned with clinical workflows. Here, we combine fully automated sample preparation and Evotip loading on the Bravo AssayMAP system with extensive method optimization on the timsTOF HT and gas-phase fractionation deep spectral libraries to advance neat plasma proteomics. Automation reduced hands-on time by 88% and significantly improved robustness. Mixed-mode searching with a 1788-protein library increased identifications by up to 31% at a throughput of 100 samples per day, with less than 15% variation across plates. In a coronary artery disease cohort, we quantified 936 biologically relevant proteins and found 42 dysregulated compared to healthy controls. This streamlined, high-throughput workflow enables deep, reproducible analysis of neat plasma at scale, paving the way for population-level biomarker discovery and clinical implementation.

In this study, we used Transmission Electron Microscopy (TEM) to establish that bacterial cellulose formed nanowhisker (BCWN) rod-like structures with distinct short and long morphologies when fabricated in a ternary deep eutectic solvent (TDES) of choline chloride, imidazole, and tannic acid. STEM-EDS confirmed nanowhisker twisting, diameter, elongation, and elemental composition. BCNWs enhanced TDES thermal stability by increasing the decomposition temperature and residual yield. DLS and zeta potential showed particle size enlargement and charge reversal, while DSC indicated a reduced melting temperature and restricted molecular mobility. Thus, TEM not only elucidated cellulose morphology but also provided insights into structural transformations and the reinforcing role of BCNWs in tuning eutectic solvent properties for sustainable nanomaterials and potential polymer electrolyte applications.

Catalytic oxygenation-mediated extraction (COME) is an environmentally friendly liquid-gas extraction technique that generates oxygen microbubbles via the catalytic decomposition of hydrogen peroxide. While corona discharge atmospheric pressure chemical ionization (APCI) is widely used for analyzing moderately polar and lower-polarity analytes with low molecular weights, secondary electrospray ionization (SESI) is a soft ionization technique that effectively ionizes polar volatile analytes. This study aims to integrate APCI and SESI with COME drift-tube ion-mobility (IM) triple quadrupole mass spectrometry (MS) to analyze volatile organic compounds (VOCs) with different physicochemical properties present in liquid matrices. The coupling of a house-built ion-mobility spectrometer with a commercial triple quadrupole mass spectrometer provides 2D separation at low cost. The user can choose one of the two ionization modes to achieve high signals with VOCs of different polarity. COME was applied to extract ethyl acetate from complex matrices (Taiwanese millet wine and whiskey) for immediate IM-MS analysis. An isotopically labeled internal standard was used to compensate for drift time and intensity shifts across multiple analyses. The system operates automatically with a graphical user interface enabling immediate ion-mobility spectrum visualization for targeted m/z.

Sensitive and selective detection of neurochemicals such as neuropeptides is critical for understanding brain signaling. While carbon-fiber microelectrodes (CFMEs) are widely used for these measurements, alternative electrode materials and fabrication techniques could improve sensitivity and versatility. In this study, we investigate pyrolyzed parylene-N microelectrodes (PPNMEs) as a promising platform for making thin-film carbon electrodes for the detection of electroactive amino acids and neuropeptides. We evaluated the performance of PPNMEs for the detection of tryptophan (Trp), tyrosine (Tyr), and the neuropeptide gonadotropin-releasing hormone (GnRH), which contains these electroactive residues. PPNMEs demonstrated significantly greater sensitivity with fast-scan cyclic voltammetry, with signal amplitudes approximately four times higher than those observed with CFMEs. After normalization for surface area, PPNMEs exhibited 3-, 5-, and 2.7-fold higher signals than CFMEs for Trp, Tyr, and GnRH, respectively. Additionally, PPNMEs facilitated faster electron transfer kinetics, as evidenced by reduced oxidation potentials. There were enhanced signals for secondary oxidation peaks at PPNMEs because the rougher surface can trap intermediates near the surface, facilitating detection of downstream electrochemical reactions. Scan rate analysis indicates more adsorption-controlled detection, contributing to improved sensitivity. Importantly, PPNMEs enabled sensitive detection of GnRH in brain tissue slices, including both puffed-on applications and spontaneous endogenous GnRH release in the median eminence. These results highlight the potential of PPNMEs as a new class of carbon-based electrodes, offering a promising alternative to CFMEs for high-sensitivity, low-potential detection of neurochemicals in biological tissues.