Explore the vast range of titles available.

Atmospheric particulate matter (PM) contains various chemicals, some of which generate in vivo reactive oxygen species (ROS). Owing to their high reactivity and oxidation ability, ROS can cause various diseases. To understand how atmospheric PM affects human health, we must clarify the PM components having oxidative potential (OP) leading to ROS production. According to previous studies, OP is exhibited by humic-like substances (HULIS) in atmospheric PM. However, the OP-dependence of the chemical structures of HULIS has not been clarified. Therefore, in this study, humic acid (HA, a model HULIS material) was exposed to ozone and ultraviolet (UV) irradiation, and its OP and structures were evaluated before and after the reactions using dithiothreitol (DTT) assay and Fourier transform infrared (FT-IR), respectively. The OP of HA was more significantly increased by UV irradiation than by ozone exposure. FT-IR analysis showed an increased intensity of the C=O peak in the HA structure after UV irradiation, suggesting that the OP of HA was increased by a chemical change to a more quinone-like structure after irradiation.

Much of the seafood that humans consume comes from estuaries and coastal areas where microplastics (MPs) accumulate, due in part to continual input and degradation of plastic litter from rivers and runoff. As filter feeders, oysters (Crassostrea virginica) are especially vulnerable to MP pollution. In this study, we assessed MP pollution in water at oyster reefs along the Mississippi Gulf Coast when: (1) historic flooding of the Mississippi River caused the Bonnet Carré Spillway to remain open for a record period of time causing major freshwater intrusion to the area and deleterious impacts on the species and (2) the spillway was closed, and normal salinity conditions resumed. Microplastics (~25 µm–5 mm) were isolated using a single-pot method, preparing samples in the same vessel (Mason jars) used for their collection right up until the MPs were transferred onto filters for analyses. The MPs were quantified using Nile Red fluorescence detection and identified using laser direct infrared (LDIR) analysis. Concentrations ranged from ~12 to 381 particles/L and tended to decrease at sites impacted by major freshwater intrusion. With the spillway open, average MP concentrations were positively correlated with salinity (r = 0.87, p = 0.05) for sites with three or more samples examined. However, the dilution effect on MP abundances was temporary, and oyster yields suffered from the extended periods of lower salinity. There were no significant changes in the relative distribution of MPs during freshwater intrusions; most of the MPs (>50%) were in the lower size fraction (~25–90 µm) and consisted mostly of fragments (~84%), followed by fibers (~11%) and beads (~5%). The most prevalent plastic was polyester, followed by acrylates/polyurethanes, polyamide, polypropylene, polyethylene, and polyacetal. Overall, this work provides much-needed empirical data on the abundances, morphologies, and types of MPs that oysters are exposed to in the Mississippi Sound, although how much of these MPs are ingested and their impacts on the organisms deserves further scrutiny. This paper is believed to be the first major application of LDIR to the analysis of MPs in natural waters.

Up to the 1930s, the Italian pictorialism movement dominated photography, and many handcrafted procedures started appearing. Each operator had his own working method and his own secrets to create special effects that moved away from the standard processes. Here, a methodology that combines X-ray fluorescence and infrared analysis spectroscopy with unsupervised learning techniques was developed on an unconventional Italian photographic print collection (the Piero Vanni Collection, 1889–1939) to unveil the artistic technique by the extraction of spectroscopic benchmarks. The methodology allowed the distinction of hidden elements, such as iodine and manganese in silver halide printing, or highlighted slight differences in the same printing technique and unveiled the stylistic practice. Spectroscopic benchmarks were extracted to identify the elemental and molecular fingerprint layers, as the oil-based prints were obscured by the proteinaceous binder. It was identified that the pigments used were silicates or iron oxide introduced into the solution or that they retraced the practice of reusing materials to produce completely different printing techniques. In general, four main groups were extracted, in this way recreating the ‘artistic palette’ of the unconventional photography of the artist. The four groups were the following: (1) Cr, Fe, K, potassium dichromate, and gum arabic bands characterized the dichromate salts; (2) Ag, Ba, Sr, Mn, Fe, S, Ba, gelatin, and albumen characterized the silver halide emulsions on the baryta layer; (3) the carbon prints were benchmarked by K, Cr, dichromate salts, and pigmented gelatin; and (4) the heterogeneous class of bromoil prints was characterized by Ba, Fe, Cr, Ca, K, Ag, Si, dichromate salts, and iron-based pigments. Some exceptions were found, such as the baryta layer being divided into gum bichromate groups or the use of albumen in silver particles suspended in gelatin, to underline the unconventional photography at the end of the 10th century.

Vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, have been successful methods for studying the interaction of light with biological materials and facilitating novel cell biology analysis. Spectrochemical analysis is very attractive in disease screening and diagnosis, microbiological studies and forensic and environmental investigations because of its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, there is now an urgent need for multivariate classification protocols allowing one to analyze biologically derived spectrochemical data to obtain accurate and reliable results. Multivariate classification comprises discriminant analysis and class-modeling techniques where multiple spectral variables are analyzed in conjunction to distinguish and assign unknown samples to pre-defined groups. The requirement for such protocols is demonstrated by the fact that applications of deep-learning algorithms of complex datasets are being increasingly recognized as critical for extracting important information and visualizing it in a readily interpretable form. Hereby, we have provided a tutorial for multivariate classification analysis of vibrational spectroscopy data (FTIR, Raman and near-IR) highlighting a series of critical steps, such as preprocessing, data selection, feature extraction, classification and model validation. This is an essential aspect toward the construction of a practical spectrochemical analysis model for biological analysis in real-world applications, where fast, accurate and reliable classification models are fundamental. A tutorial for multivariate classification analysis of vibrational spectroscopy data (Fourier-transform infrared, Raman and near-IR) is presented. Guidelines are provided for data preprocessing, data selection, feature extraction, classification and model validation.

Abstract The traditional manual analysis of microplastics has been criticized for its labor-intensive, inaccurate identification of small microplastics, and the lack of uniformity. There are already three automated analysis strategies for microplastics based on vibrational spectroscopy: laser direct infrared (LDIR)–based particle analysis, Raman-based particle analysis, and focal plane array-Fourier transform infrared (FPA-FTIR) imaging. We compared their performances in terms of quantification, detection limit, size measurement, and material identification accuracy and speed by analyzing the same standard and environmental samples. LDIR-based particle analysis provides the fastest analysis speed, but potentially questionable material identification and quantification results. The number of particles smaller than 60 μm recognized by LDIR-based particle analysis is much less than that recognized by Raman-based particle analysis. Misidentification could occur due to the narrow tuning range from 1800 to 975 cm−1 and dispersive artifact distortion of infrared spectra collected in reflection mode. Raman-based particle analysis has a submicrometer detection limit but should be cautiously used in the automated analysis of microplastics in environmental samples because of the strong fluorescence interference. FPA-FTIR imaging provides relatively reliable quantification and material identification for microplastics in environmental samples greater than 20 μm but might provide an imprecise description of the particle shapes. Optical photothermal infrared (O-PTIR) spectroscopy can detect submicron-sized environmental microplastics (0.5–5 μm) intermingled with a substantial amount of biological matrix; the resulting spectra are searchable in infrared databases without the influence of fluorescence interference, but the process would need to be fully automated.

The aim of this study was to investigate the influence of fineness and heat-treatment on the pozzolanic and engineering properties of volcanic ash. To this end, two different fineness levels of volcanic ash, ultra-fine (VAF) and fine (VA), without and after heat treatment at different temperatures (VA550, VA650, and VA750), were partially substituted for cement. In addition to the control (100% cement), five binary mortar mixes, each containing 20% of the different types of volcanic ash (VAF and VA; heat-treated and not), were prepared. First, X-ray fluorescence (XRF), X-ray powder diffraction (XRD), particle size analysis, and modified Chappelle tests were used to characterize the material. All mortar mixes were then tested for compressive strength development, water absorption, and apparent porosity. Finally, the microstructure of each of the mixes was evaluated by performing XRD, thermogravimetric analysis (TGA), and Fourier transform infrared spectroscopy (FTIR) analyses on paste samples at 91 days post-formation. The XRD and Chappelle reactivity results revealed increased pozzolanic reactivity with increasing volcanic ash fineness. In contrast, heat treatment adversely affected the pozzolanic reactivity of the volcanic ash due to the formation of crystalline phases at high temperatures. The mortars containing VAF20 (VAF, no heat, at 20%) showed slightly improved compressive strength (69.6 MPa) than the control (68.1 MPa) and all other binary mixes (66.7, 63.5, 64.2, and 63.9 MPa for VA20, VA20-550, VA20-650, and VA20-750, respectively) at 91 days. The mortar containing VAF20 demonstrated the lowest level of water absorption (9.3%) and apparent porosity (19.1%) of all mixes, including the control. The XRD results for the paste samples show that both VA and VAF showed the least intensity of portlandite phase, as compared to the control and other binary mixes. TGA results also show that binary mixes of VA and VAF have a reduced amount of portlandite, resulting in the densification of the mixes’ microstructures. With the addition of VAF, there is a significant shift in the FTIR band from 980 to 992 cm−1, which causes the formation of additional C–S–H gels that lead to the densification of the paste matrix. These results demonstrate that VAF exhibits high pozzolanic reactivity, making it suitable for use as a natural pozzolan that can partially substitute cement in the production of strong, durable, and environmentally friendly concrete.

In order to assess the human exposure risks from the release of contaminants from water pipes made of polyvinyl chloride (PVC), experiments were carried out by subjecting the PVC pipe material to burning and leaching conditions followed by analysis of the emission and leachate samples. The emissions of burning pipes were analyzed by both infrared spectrometry and gas chromatography-mass spectrometry (GC-MS). The emission test results indicate the presence of chlorinated components including chlorine dioxide, methyl chloride, methylene chloride, allyl chloride, vinyl chloride, ethyl chloride, 1-chlorobutane, tetrachloroethylene, chlorobenzene, and hydrogen chloride were detected in the emissions of burning PVC pipes. Furthermore, the concentrations of benzene, 1,3-butadiene, methyl methacrylate, carbon monoxide, acrolein, and formaldehyde were found at levels capable of affecting human health adversely. The analysis of PVC pipe leachates using GC-MS shows that there are 40–60 tentatively identified compounds, mostly long-chain hydrocarbons such as tetradecane, hexadecane, octadecane, and docosane, were released when the burned PVC materials were soaked in deionized water for one week. Quantitative analysis shows that 2-butoxyethanol, 2-ethyl-1-hexanol, and diethyl phthalate were found in the burned PVC polymer at the average levels of 2.7, 14.0, and 3.1 micrograms per gram (μg/g) of pipe material. This study has significant implications for understanding the benzene contamination of drinking water in the aftermath of wildfires that burned polymer pipes in California.

Near-infrared (NIR) spectroscopy is a powerful analytical method for rapid, non-destructive and label-free assessment of biological materials. Compared to mid-infrared spectroscopy, NIR spectroscopy excels in penetration depth, allowing intact biological tissue assessment, albeit at the cost of reduced molecular specificity. Furthermore, it is relatively safe compared to Raman spectroscopy, with no risk of laser-induced photothermal damage. A typical NIR spectroscopy workflow for biological tissue characterization involves sample preparation, spectral acquisition, pre-processing and analysis. The resulting spectrum embeds intrinsic information on the tissue’s biomolecular, structural and functional properties. Here we demonstrate the analytical power of NIR spectroscopy for exploratory and diagnostic applications by providing instructions for acquiring NIR spectra, maps and images in biological tissues. By adapting and extending this protocol from the demonstrated application in connective tissues to other biological tissues, we expect that a typical NIR spectroscopic study can be performed by a non-specialist user to characterize biological tissues in basic research or clinical settings. We also describe how to use this protocol for exploratory study on connective tissues, including differentiating among ligament types, non-destructively monitoring changes in matrix formation during engineered cartilage development, mapping articular cartilage proteoglycan content across bovine patella and spectral imaging across the depth-wise zones of articular cartilage and subchondral bone. Depending on acquisition mode and experiment objectives, a typical exploratory study can be completed within 6 h, including sample preparation and data analysis. This protocol describes how to perform near-infrared spectroscopy and imaging of connective tissues. Detailed guidelines are provided for sample preparation, spectral acquisition and data pre-processing and analysis, with example applications.

In this study, epoxy resin (EP) composites were prepared by the incorporation of hydroquinone bis(diphenyl phosphate) (HDP) and tris(2-hydroxyethyl) isocyanurate (THEIC), and the synergistic effects of HDP and THEIC on the flame retardancy of EP were investigated. The flame retardancy of EP composites was systematically evaluated by measuring limiting oxygen index (LOI), UL-94 vertical burning tests and a cone calorimeter. The results indicate that the synergistic effect of the addition of 15% HDP and 15% THEIC in the composite EP-4 is the best. Compared with neat EP, the LOI of EP-4 has increased from 21.7 to 27.5%, and UL-94 reached V-0. The heat release rate (HRR), total heat release (THR), smoke production rate (SPR) and total smoke production (TSP) of EP-4 in the cone calorimeter test decreased by 81.14, 41.42, 72.57 and 53.61%, respectively. The release rates of CO 2 and CO were also significantly reduced, indicating a good synergistic effect between HDP and THEIC. Furthermore, the synergistic flame-retardant mechanism between HDP and THEIC has been thoroughly studied by scanning electron microscopy (SEM) and thermogravimetric analysis/infrared analysis (TG-IR). The results show that HDP/THEIC can effectively strengthen the carbon layer structure resulted from the composite after combustion and increase the flame retardancy of the condensed phase. Meanwhile, PO, PO 2 radicals and NH 3 produced by the degradation of HDP and THEIC are also beneficial to improve the flame retardancy of the gas phase.

The identification of microplastics becomes increasingly challenging with decreasing particle size and increasing sample heterogeneity. The analysis of microplastic samples by Fourier transform infrared (FTIR) spectroscopy is a versatile, bias-free tool to succeed at this task. In this study, we provide an adaptable reference database, which can be applied to single-particle identification as well as methods like chemical imaging based on FTIR microscopy. The large datasets generated by chemical imaging can be further investigated by automated analysis, which does, however, require a carefully designed database. The novel database design is based on the hierarchical cluster analysis of reference spectra in the spectral range from 3600 to 1250 cm−1. The hereby generated database entries were optimized for the automated analysis software with defined reference datasets. The design was further tested for its customizability with additional entries. The final reference database was extensively tested on reference datasets and environmental samples. Data quality by means of correct particle identification and depiction significantly increased compared to that of previous databases, proving the applicability of the concept and highlighting the importance of this work. Our novel database provides a reference point for data comparison with future and previous microplastic studies that are based on different databases.