Elemental composition was used to characterize and differentiate 14 wines made from the identical clone of Vitis vinifera cv. Pinot noir (clone 667). The vineyards span distances which range from several hundred meters to 1540 km and their elevations vary from near sea level to nearly 500 m. Twenty-seven elements were observed above the limit of quantitation by using inductively coupled plasma mass spectrometry (ICP-MS) in the wines from at least half of the 14 sites. Concentrations of several elements, including Mo, Er, Na, Li, Cs and Pb, varied by 10-fold across the 14 wines. Multiple factor analysis (MFA) of elemental composition with juice chemistry and site characterization show associations consistent with expectations, such as high Ca with high clay content. These results demonstrate that even when grapevine clone and winemaking protocol are controlled, composition differences in wines produced from sites are mediated by diverse soil and microclimate conditions.
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http://dx.doi.org/10.1016/j.foodchem.2020.127386 | DOI Listing |
ACS EST Air
January 2025
Environmental Engineering Program, University of Colorado Boulder, 1111 Engineering Drive, Boulder, Colorado 80309-0428, United States.
Quantifying changes in the properties of smoke aerosols under varying conditions is important for understanding the health and environmental impacts of exposure to smoke. Smoke composition, aerosol liquid water content, effective density (ρ), and other properties can change significantly as smoke travels through areas under different ambient conditions and over time. During this study, we measured changes in smoke composition and physical properties due to oxidative aging and exposure to humidity.
View Article and Find Full Text PDFLoading with non-metal cocatalysts to regulate interfacial charge transfer and separation has become a prominent focus in current research. In this study, g-CN/CNT composites loaded with non-metallic cocatalysts were prepared through pyrolysis using urea and CNTs. Various characterization techniques, including transmission electron microscopy (TEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), ultraviolet-visible diffuse reflectance spectroscopy (UV-vis DRS), photoelectrochemical (PEC) analysis, fluorescence lifetime spectroscopy (TRPL), electron paramagnetic resonance spectroscopy (ESR), and photoluminescence (PL) spectroscopy, were employed to analyze the sample's microstructure, phase composition, elemental chemical states, and photoelectronic properties.
View Article and Find Full Text PDFJ Hazard Mater
January 2025
State Key Lab of Geohazard prevention & Geoenvironment protection, College of Materials and Chemistry & Chemical Engineering, Chengdu University of Technology, Chengdu 610059, China. Electronic address:
Sulfur nanoparticles (SNPs) and their composites are promising for heavy metal adsorption, yet current SNPs often lack surface S, leading to low affinity toward heavy metal and ease of aggregation. Here, we report a simple light-driven method for facile prepare SNPs with surfaces enriched with S and in-situ load them onto graphene oxide (GO) to fabricate GO-S composites. Under illumination, the O generated by photosensitizer phloxine B was able to oxidize S into elemental SNPs.
View Article and Find Full Text PDFPhys Med Biol
January 2025
Graduate School of Medicine, Osaka University, 2-2 Yamada-oka, Suita, Osaka, 565-0871, JAPAN.
Accurate dose predictions are crucial to maximizing the benefits of carbon-ion therapy. Carbon beams incident on the human body cause nuclear interactions with tissues, resulting in changes in the constituent nuclides and leading to dose errors that are conventionally corrected using conventional single-energy computed tomography (SECT). Dual-energy computed tomography (DECT) has frequently been used for stopping power estimation in particle therapy and is well suited for correcting nuclear reactions because of its detailed body-tissue elemental information.
View Article and Find Full Text PDFiScience
January 2025
School of Mathematics and Statistics, Zhengzhou University, Zhengzhou 450001, China.
This study introduces a hybrid network model for phase classification, integrating quantum networks and complex-valued neural networks. This architecture uses elemental composition as its only input, eliminating complex feature engineering. Parameterized quantum networks handle sparse elemental data and convert data from real to complex domains, increasing information dimensionality.
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