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A method for the determination of nine biogenic amines (BAs) in wine was established using ion chromatography–tandem mass spectrometry (IC-MS/MS) without derivatization. BAs were separated by a cation exchange column (IonPac CG17, 50 mm × 4 mm, 7 µm) with a gradient aqueous formic acid elution. Good linearity was obtained for nine BAs with coefficients of determination (R2) > 0.9972 within the range of 0.01–50 mg/L. The limits of detection and quantification were within the ranges of 0.6–40 µg/L and 2.0–135 µg/L, respectively, with the exception of spermine (SPM). The recoveries were demonstrated within the range of 82.6–103.0%, with relative standard deviations (RSDs) of less than 4.2%. This simple method featuring excellent sensitivity and selectivity was suitable for the quantification of BAs in wines. The occurrence of BAs in 236 wine samples that are commercially available in China was determined. The BA levels in wines of different geographical origins varied significantly. The acute dietary exposure assessment of BAs was carried out by calculating the estimated short-term intake (ESTI) and comparing the acute reference dose (ARfD) specified by the European Food Safety Authority (EFSA). Results showed that the exposure to histamine (HIS) and tyramine (TYR) via the consumption of wines was much lower than the recommended ARfD level for healthy individuals. However, exposure could lead to symptoms in susceptible individuals. These results provided basic data regarding the occurrence and risk of BAs in wines for wine production, health guidance and consumer safety. [ABSTRACT FROM AUTHOR]

Organic single crystals with low defect density are key functional materials for next‐generation electronics. Classical crystallization commonly results in a high density of molecular steps, which are defects that can trap charge carriers and reduce the mobility of organic semiconductors. Herein, a novel nonclassical crystallization mechanism involving nucleation, fusion, and growth is designed. The introduction of the fusion stage changes the crystal growth mode from three‐dimensional island growth to two‐dimensional layer‐by‐layer growth and leads to large‐area two‐dimensional molecular crystals with almost no molecular steps. Therefore, the average mobility is observed to improve from 1.26 to 2.07 cm2 V−1 s−1. The nonclassical crystallization mechanism paves the way for atomically flat single crystals to probe the intrinsic optoelectronic properties of organic semiconductors. [ABSTRACT FROM AUTHOR]

Dynamic origin‐destination (OD) flow estimation is one of the most fundamental problems in traffic engineering. Despite numerous existing studies, the OD flow estimation problem remains challenging, as there is large dimensional difference between the unknown values to be estimated and the known traffic observations. To meet the needs of active traffic management and control, accurate time‐dependent OD flows are required to understand time‐of‐day traffic flow patterns. In this work, we propose a three‐dimensional (3D) convolution‐based deep neural network, "Res3D," to learn the high‐dimensional correlations between local traffic patterns presented by automatic vehicle identification observations and OD flows. In this paper, a practical framework combining simulation‐based model training and few‐shot transfer learning is introduced to enhance the applicability of the proposed model, as continuously observing OD flows could be expensive. The proposed model is extensively tested based on a realistic road network, and the results show that for significant OD flows, the relative errors are stable around 5%, outperforming several other models, including prevalent neural networks as well as existing estimation models. Meanwhile, corrupted and out‐of‐distribution samples are generated as real‐world samples to validate Res3D's transferability, and the results indicated a 60% improvement with few‐shot transfer learning. Therefore, this proposed framework could help to bridge the gaps between traffic simulations and empirical cases. [ABSTRACT FROM AUTHOR]