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As one of the most important components of a vehicle, the automotive coating system is typically a multi-layer polymeric structure with complex scratch damage patterns. This work is the first one to systematically investigate the scratch performance of an automotive coating system via experimental and numerical approaches. To achieve this end, uniaxial tensile tests are firstly carried out to acquire the basic mechanical properties of paint films at different strain rates. Then, a series of scratch tests are conducted in accordance with ASTM D7027/ISO 19252 standards, and the damage features are identified. In addition, the effects of scratch speed, loading rate, and stylus type are experimentally investigated. Afterwards, a finite element approach for scratch simulations is developed, which includes a rate-dependent constitutive model calibrated using the uniaxial tensile tests, a tied interface algorithm for efficient connection between coating and substrate, and an interior contact method to avoid excessive element distortions. Eventually, with the aid of the presented numerical approach, the damage patterns observed in scratch tests are numerically explained, and the scratch damage process is reasonably surmised. The presented approaches and findings can contribute to the development of anti-scratch automotive coating structures.

Abstract Powder crystallography is the experimental science of determining the structure of molecules provided in crystalline-powder form, by analyzing their x-ray diffraction (XRD) patterns. Since many materials are readily available as crystalline powder, powder crystallography is of growing usefulness to many fields. However, powder crystallography does not have an analytically known solution, and therefore the structural inference typically involves a laborious process of iterative design, structural refinement, and domain knowledge of skilled experts. A key obstacle to fully automating the inference process computationally has been formulating the problem in an end-to-end quantitative form that is suitable for machine learning, while capturing the ambiguities around molecule orientation, symmetries, and reconstruction resolution. Here we present an ML approach for structure determination from powder diffraction data. It works by estimating the electron density in a unit cell using a variational coordinate-based deep neural network. We demonstrate the approach on computed powder x-ray diffraction (PXRD), along with partial chemical composition information, as input. When evaluated on theoretically simulated data for the cubic and trigonal crystal systems, the system achieves up to 93.4% average similarity (as measured by structural similarity index) with the ground truth on unseen materials, both with known and partially-known chemical composition information, showing great promise for successful structure solution even from degraded and incomplete input data. The approach does not presuppose a crystalline structure and the approach are readily extended to other situations such as nanomaterials and textured samples, paving the way to reconstruction of yet unresolved nanostructures.

Solution-state NMR typically requires 100 μM to 1 mM samples. This limitation prevents applications to mass-limited and aggregationprone target molecules. Photochemically induced dynamic nuclear polarization was adapted to data collection on low-concentration samples by radiofrequency gating, enabling rapid 1D NMR spectral acquisition on aromatic amino acids and proteins bearing aromatic residues at nanomolar concentration, i.e., a full order of magnitude below other hyperpolarization techniques in liquids. Both backbone H1-C13 and side-chain resonances were enhanced, enabling secondary and tertiary structure analysis of proteins with remarkable spectral editing, via the 13C PREPRINT pulse sequence. Laser-enhanced 2D NMR spectra of 5 μM proteins at 600 MHz display 30-fold better S/N than conventional 2D data collected at 900 MHz. Sensitivity enhancements achieved with this technology, denoted as low-concentration photo- CIDNP (LC-photo-CIDNP), depend only weakly on laser intensity, highlighting the opportunity of safer and more cost-effective hypersensitive NMR applications employing low-power laser sources. [ABSTRACT FROM AUTHOR]

A new room-temperature ionic liquid (RTIL) consisting of a polyoxometalate (POM) anion and tri-block copolymer (P123)-functionalized imidazolium cation was synthesized and utilized as a halogen-free catalyst for esterification. The catalytic system was a homogeneous solution at the beginning of the reaction, but an emulsion formed during the course of the reaction, and a progressive phase separation of the catalyst occurred at 0 °C over the course of 3 h. Dynamic light scattering (DLS), transmission electron microscopy (TEM), and Fourier transform/infrared spectroscopy (FT/IR) have been used to characterize the properties of the IL during the reaction. The new IL catalyst was found to be highly efficient in the esterification of various alcohols and can be recycled at least seven times. [ABSTRACT FROM AUTHOR]

The study analyzes the impact of ownership structure on dividend smoothing via the lens of agency and information asymmetry theory. The study also investigates the impact of ownership on dividend smoothing in the unexamined asymmetric context Dividend smoothing is measured via speed of adjustment and relative volatility. The study documents that higher individual, management, and institutional ownerships are positively associated with increased dividend smoothing. Consistent with the rental hypothesis in foreign-owned firms smooth less also concentrated firms bear with cuts and omissions. Foreign ownership has the opposite impact on dividend smoothing in adjusting dividends from below and above i.e., always prefer high dividends. Individual ownership has also exhibited a different impact in smoothing from below and above. Institutional owners avoid cuts and omissions and negatively affect SOA (smooth more) in case of adjusting dividends from above. Ownership concentration is negatively associated with dividend smoothing irrespective of whether the firm is smoothing from above or below. In contrast, management ownership negatively affected SOA in adjusting from above or below. Family firms in Pakistan smooth more to win minor shareholders' trust and signal that they sacrifice their private benefits to reduce the type II agency problem. Finally, the authors found a negative association between dividend smoothing and corporate governance quality. Over all the findings of the current study provides insight to the investors and regulators by offering dividend smoothing as an alternative monitoring mechanism to corporate governance., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Ali, Hanming, Zhongxin and Ali.)