ACS Measurement Science Au最新文献

Synthetic, ecofriendly fuels and chemicals can be produced through Power-To-X (PtX) processes. To study such catalytic processes operando and spatially resolved, magnetic resonance imaging (MRI) is a versatile tool. A main issue in the application of MRI in reactive studies is a lack of knowledge about how the gathered signals can be interpreted into reaction data like temperature or species concentration. In this work, the interaction of methane and gaseous water is studied regarding their longitudinal relaxation time T₁ and the chemical shift. To this end, defined quantities of methane-water mixtures were sealed in glass tubes and probed at temperatures between 130 and 360 °C and pressures from 6 to 20 bar. From the obtained T₁ relaxation times, the collision cross section of methane with water σ_j,CH4-H2O is derived, which can be used to estimate the temperature and molar concentration of methane during the methanation reaction. The obtained T₁ relaxation times can additionally be used to improve the timing of MRI sequences involving water vapor or methane. Further, details about the measurement workflow and tube preparation are shared.

Electrochemical advanced oxidation (EAO) systems are of significant interest due to their ability to treat a wide range of organic contaminants in water. Boron doped diamond (BDD) electrodes have found considerable use in EAO. Despite their popularity, no laboratory scale method exists to quantify anodic corrosion of BDD electrodes under EAO conditions; all are qualitative using techniques such as scanning electron microscopy, electrochemistry, and spectroscopy. In this work, we present a new method which can be used to quantify average corrosion rates as a function of solution composition, current density, and BDD material properties over relatively short time periods. The method uses white light interferometry (WLI), in conjunction with BDD electrodes integrated into a 3D-printed flow cell, to measure three-dimensional changes in the surface structure due to corrosion over a 72 h period. It is equally applicable to both thin film and thicker, freestanding BDD. A further advantage of WLI is that it lends itself to large area measurements; data are collected herein for 1 cm diameter disk electrodes. Using WLI, corrosion rates as low as 1 nm h^–1 can be measured. This enables unequivocal demonstration that organics in the EAO solution are not a prerequisite for BDD anodic corrosion. However, they do increase the corrosion rates. In particular, we quantify that addition of 1 M acetic acid to 0.5 M potassium sulfate results in the average corrosion rate increasing ∼60 times. In the same solution, microcrystalline thin film BDD is also found to corrode ∼twice as fast compared to freestanding polished BDD, attributed to the presence of increased sp² carbon content. This methodology also represents an important step forward in the prediction of BDD electrode lifetimes for a wide range of EAO applications.

Precision medicine is a new medical approach which considers both population characteristics and individual variability to provide customized healthcare. The transition from traditional reactive medicine to personalized medicine is based on a biomarker-driven process and a deep knowledge of biological mechanisms according to which the development of diseases occurs. In this context, the advancements in high-throughput omics technologies represent a unique opportunity to discover novel biomarkers and to provide an unbiased picture of the biological system. One of the medical fields in which omics science has started to be recently applied is that of ophthalmology. Ocular diseases are very common, and some of them could be highly disabling, thus leading to vision loss and blindness. The pathogenic mechanism of most ocular diseases may be dependent on various genetic and environmental factors, whose effect has not been yet completely understood. In this context, large-scale omics approaches are fundamental to have a comprehensive evaluation of the whole system and represent an essential tool for the development of novel therapies. This Review summarizes the recent advancements in omics science applied to ophthalmology in the last ten years, in particular by focusing on proteomics, metabolomics and lipidomics applications from an analytical perspective. The role of high-efficiency separation techniques coupled to (high-resolution) mass spectrometry ((HR)MS) is also discussed, as well as the impact of sampling, sample preparation and data analysis as integrating parts of the analytical workflow.

Statistical analysis and modeling of mass spectrometry (MS) data have a long and rich history with several modern MS-based applications using statistical and chemometric methods. Recently, machine learning (ML) has experienced a renaissance due to advents in computational hardware and the development of new algorithms for artificial neural networks (ANN) and deep learning architectures. Moreover, recent successes of new ANN and deep learning architectures in several areas of science, engineering, and society have further strengthened the ML field. Importantly, modern ML methods and architectures have enabled new approaches for tasks related to MS that are now widely adopted in several popular MS-based subdisciplines, such as mass spectrometry imaging and proteomics. Herein, we aim to provide an introductory summary of the practical aspects of ML methodology relevant to MS. Additionally, we seek to provide an up-to-date review of the most recent developments in ML integration with MS-based techniques while also providing critical insights into the future direction of the field.

O-glycosylation is a common post-translational modification that is essential for the defensive properties of mucus barriers. Incomplete and altered O-glycosylation is often linked to severe diseases, such as cancer, cystic fibrosis, and chronic obstructive pulmonary disease. Originating from a nontemplate-driven biosynthesis, mucin-type O-glycan structures are very complex. They are often present as heterogeneous mixtures containing multiple isomers. Therefore, the analysis of complex O-glycan mixtures usually requires hyphenation of orthogonal techniques such as liquid chromatography (LC), ion mobility spectrometry, and mass spectrometry (MS). However, MS-based techniques are mainly qualitative. Moreover, LC separation of O-glycans often lacks reproducibility and requires sophisticated data treatment and analysis. Here we present a mucin-type O-glycomics analysis workflow that utilizes hydrophilic interaction liquid chromatography for separation and fluorescence labeling for detection and quantification. In combination with mass spectrometry, a detailed analysis on the relative abundance of specific mucin-type O-glycan compositions and features, such as fucose, sialic acids, and sulfates, is performed. Furthermore, the average number of monosaccharide units of O-glycans in different samples was determined. To demonstrate universal applicability, the method was tested on mucins from different tissue types and mammals, such as bovine submaxillary mucins, porcine gastric mucins, and human milk mucins. To account for day-to-day retention time shifts in O-glycan separations and increase the comparability between different instruments and laboratories, we included fluorescently labeled dextran ladders in our workflow. In addition, we set up a library of glucose unit values for all identified O-glycans, which can be used to simplify the identification process of glycans in future analyses.

This proof-of-principle study presents the ability of the recently developed iLovEnose to measure ultratrace levels of volatile organic compounds (VOCs) in simulated human breath based on the combination of multiple gas sensors. The iLovEnose was developed by our research team as a test bed for gas sensors that can be hosted in three serially connected compact low-volume and temperature-controlled compartments. Herein, the eNose system was equipped with conventional semiconducting metal oxide (MOX) gas sensors using a variety of base technologies providing 11 different sensor signals that were evaluated to determine six VOCs of interest at eight low to ultralow concentration levels (i.e., ranging from 3 to 0.075 ppm) at humid conditions (90% rh at 22 °C). The measurements were randomized and performed four times over a period of 2 weeks. Partial least-squares regression analysis was applied to estimate the concentration of these six analytes. It was shown that the iLovEnose system is able to discriminate between these VOCs and provide reliable quantitative information relevant for future applications in exhaled breath analysis as a diagnostic disease detection or monitoring device.

Relaxation rate dispersion, i.e., nonexponential or multicomponent kinetics, is observed in complex systems when measuring relaxation kinetics. Often, the origin of rate dispersion is associated with the heterogeneity in the system. However, both homogeneous (where all molecules experience the same rate but inherently nonexponential) and heterogeneous (where all molecules experience different rates) systems can exhibit rate dispersion. A multidimensional correlation analysis method has been demonstrated to detect and quantify rate dispersion observed in molecular rotation, diffusion, solvation, and reaction kinetics. One-dimensional (1D) autocorrelation function detects rate dispersion and measures its extent. Two-dimensional (2D) autocorrelation function measures the origin of rate dispersion and distinguishes homogeneous from heterogeneous. In a heterogeneous system, implicitly there exist subensembles of molecules experiencing different rates. A three-dimensional (3D) autocorrelation function measures subensemble exchange if present and reveals if the system possesses static or dynamic heterogeneity. This perspective discusses the principles, applications, and potential and also presents a future outlook of two-dimensional fluctuation correlation spectroscopy (2D-FlucCS). The method is applicable to any experiment or simulation where a time series of fluctuation in an observable (emission, scattering, current, etc.) around a mean value can be obtained in steady state (equilibrium or nonequilibrium), provided the system is ergodic.

The development of artificial receptors has great significance in measurement science and technology. The need for a robust version of natural receptors is getting increased attention because the cost of natural receptors is still high along with storage difficulties. Aptamers, imprinted polymers, and nanozymes are some of the matured artificial receptors in analytical chemistry. Recently, a new direction has been discovered by organic chemists, who can synthesize robust, activity-based, self-immolative organic molecules that have artificial receptor properties for the targeted analytes. Specifically designed trigger moieties implant selectivity and sensitivity. These latent electrochemical redox substrates are highly stable, mass-producible, inexpensive, and eco-friendly. Combining redox substrates with the merits of electrochemical techniques is a good opportunity to establish a new direction in artificial receptors. This Review provides an overview of electrochemical redox substrate design, anatomy, benefits, and biosensing potential. A proper understanding of molecular design can lead to the development of a library of novel self-immolative redox molecules that would have huge implications for measurement science and technology.