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Mass Transport-Dependent C–C Bond Formation for CO Electroreduction with Alkali Cations
The association between body mass index and asthma in children: a cross‑sectional study from NHANES 1999 to 2020
Coherent Vibrational Dynamics in an Isolated Peptide Captured with Two-Dimensional Infrared Spectroscopy
Comprehensive analysis of senescence-related genes identifies prognostic clusters with distinct characteristics in glioma
C–C Cleavage/Cross-Coupling Approach for the Modular Synthesis of Medium-to-Large Sized Rings: Total Synthesis of Resorcylic Acid Lactone Natural Products
Leaf rust resistance in wheat and interpretation of the antifungal activity of silver and copper nanoparticles
Abstract Wheat production is jeopardized by Puccinia triticina, the pathogen responsible for wheat leaf rust. This study assessed the impact of silver (Ag) and copper (Cu) nanoparticles (NPs) on the control of wheat leaf rust disease and the underlying mechanisms of disease resistance. The application of the two nanoparticles resulted in a reduction of spore germination and an extension of both incubation and latent periods. A common type of infection resulted in a reduction in both the length and width of pustules. It reduced receptivity value (number of pustule cm2) compared to untreated wheat plants by altering the physiological and biochemical responses of wheat plants and cell walls’ physical and mechanical strength. The application of Ag + Cu NPs stimulates the biosynthesis of defense-related molecules crucial for P. triticina inoculation and latent periods. Furthermore, molecular docking studies were conducted to assess the effects of Cu-chitosan nanoparticles (Ag & CuNp) and their mechanisms in disease management.
Less-Dominant Resonance Configuration of Propargyl Radical Leads to a Growth Mechanism for Polycyclic Aromatic Hydrocarbons that Preserves the Cyclopenta Ring
Assessment of surface sediment properties and heavy metal contamination in typical urban areas of the Yellow River, China
Structure and pH Dependence of Membranolytic Mechanisms by Truncated Oxidized Phospholipids
Investigation of risk-aware dynamic accident monitoring and early warning technologies for chemical production processes
Off-Equilibrium Hydrothermal Synthesis of High-Entropy Alloy Nanoparticles
Author Correction: Complete chloroplast genomes of 13 species of the Impatiens genus for genomic features and phylogenetic relationships studies
Solution Synthesis of Single Crystalline Zinc Nanowires
Dynamic sealing simulation and performance optimization of conical rubber core in rotary blowout preventer
Impact of Reaction Environment on Photogenerated Charge Transfer Demonstrated by Sequential Imaging
Insights into GLP-1 and insulin secretion mechanisms in pasireotide-induced hyperglycemia highlight effectiveness of Gs-targeting diabetes treatment
Design of thin, wideband electromagnetic absorbers with polarization and angle insensitivity using deep learning
Abstract Metamaterial-based electromagnetic absorbers, despite being thin and lightweight, typically suffer from narrow-band frequency bandwidth and sensitivity to polarization and incident angle due to their resonant nature. Previous methods to increase bandwidth have shown improvements but have not fully succeeded in developing wide-band, thin metamaterial-based absorbers suitable for mass production. In this study, we introduce a novel approach that leverages artificial intelligence to design a thin, wideband metamaterial-based absorber covering the entire frequency range of 8-12 GHz. The proposed method utilizes a Generative Adversarial Network (GAN), given the need for precise structural details and computational efficiency, which globally outperform variational autoencoders (VAEs) and diffusion models, for parameter estimation and a Multi-Layer Perceptron (MLP) network as a simulator to predict the electromagnetic response of the designed absorber and provide feedback to the generative network. Numerical full-wave electromagnetic simulations serve as the training data and ground truth for both the GAN and MLP networks. This training enables the generative network to produce structures with high absorption, while the MLP predicts the corresponding absorbance value for each structure. This approach allows for the rapid design of various real-world structures, quick calculation of their absorption values using the MLP network, and selection of the most optimal structures for fabrication. The performance of the designed metamaterial-based absorber is verified both numerically and experimentally. Results show an absorption rate above 90% for all frequencies in the range of 8-12 GHz. The structure also operates effectively for both TE and TM polarizations and for all incident angles between 0-45 degrees. Additionally, the designed structure can be easily fabricated using printed circuit board (PCB) technology, making it practical and suitable for mass production.