Building Hierarchical Micro-Structure on the Carbon Fabrics to Improve Their Reinforcing Effect in the CFRP Composites

Nano-fibers grafted on carbon fibers (CFs) has been of one of the most popular methods used for the carbon fibers surface treatment, which could significantly influence the interfacial properties between polymer matrix and carbon fibers in composites. This chapter demonstrated three novel carbon fibers surface treatment methods, they are carbon nanotubes (CNTs) grafted on CFs using catalysts formed in an ethanol flame, carbon fiber forests (CFFs) by carbon fiber surface brushing and abrading and ZnO nanowire grown onto CFs though a facile hydrothermal method respectively. Based on metal catalyst particles or dopamine-based functionalization formed onto the nano-fiber/CF interface, a good interfacial bonding strength between the nano-fiber and CFs was observed by an instrumented tip of an atomic force microscope and further improvement of interfacial shear strength with epoxy as measured by the single fiber pull out/microbond test was realized. The hierarchical micro-fibers on CF fabrics were then utilized to fabricate the laminates to characterize anti-delamination capacity (the mode I and mode II interlaminar fracture toughness) of these composite laminates, wherein carbon fiber fabrics were grafted with CNTs, short CFs and ZnO nanowires respectively

Robotic Planning under Hierarchical Temporal Logic Specifications

Past research into robotic planning with temporal logic specifications, notably Linear Temporal Logic (LTL), was largely based on singular formulas for individual or groups of robots. But with increasing task complexity, LTL formulas unavoidably grow lengthy, complicating interpretation and specification generation, and straining the computational capacities of the planners. In order to maximize the potential of LTL specifications, we capitalized on the intrinsic structure of tasks and introduced a hierarchical structure to LTL specifications. In contrast to the "flat" structure, our hierarchical model has multiple levels of compositional specifications and offers benefits such as greater syntactic brevity, improved interpretability, and more efficient planning. To address tasks under this hierarchical temporal logic structure, we formulated a decomposition-based method. Each specification is first broken down into a range of temporally interrelated sub-tasks. We further mine the temporal relations among the sub-tasks of different specifications within the hierarchy. Subsequently, a Mixed Integer Linear Program is utilized to generate a spatio-temporal plan for each robot. Our hierarchical LTL specifications were experimentally applied to domains of robotic navigation and manipulation. Results from extensive simulation studies illustrated both the enhanced expressive potential of the hierarchical form and the efficacy of the proposed method.Comment: 8 pages, 4 figure

Variational Quantum Circuits Enhanced Generative Adversarial Network

Generative adversarial network (GAN) is one of the widely-adopted machine-learning frameworks for a wide range of applications such as generating high-quality images, video, and audio contents. However, training a GAN could become computationally expensive for large neural networks. In this work, we propose a hybrid quantum-classical architecture for improving GAN (denoted as QC-GAN). The performance was examed numerically by benchmarking with a classical GAN using MindSpore Quantum on the task of hand-written image generation. The generator of the QC-GAN consists of a quantum variational circuit together with a one-layer neural network, and the discriminator consists of a traditional neural network. Leveraging the entangling and expressive power of quantum circuits, our hybrid architecture achieved better performance (Frechet Inception Distance) than the classical GAN, with much fewer training parameters and number of iterations for convergence. We have also demonstrated the superiority of QC-GAN over an alternative quantum GAN, namely pathGAN, which could hardly generate 16$\times$16 or larger images. This work demonstrates the value of combining ideas from quantum computing with machine learning for both areas of Quantum-for-AI and AI-for-Quantum

One-shot ultraspectral imaging with reconfigurable metasurfaces

One-shot spectral imaging that can obtain spectral information from thousands of different points in space at one time has always been difficult to achieve. Its realization makes it possible to get spatial real-time dynamic spectral information, which is extremely important for both fundamental scientific research and various practical applications. In this study, a one-shot ultraspectral imaging device fitting thousands of micro-spectrometers (6336 pixels) on a chip no larger than 0.5 cm$^2$, is proposed and demonstrated. Exotic light modulation is achieved by using a unique reconfigurable metasurface supercell with 158400 metasurface units, which enables 6336 micro-spectrometers with dynamic image-adaptive performances to simultaneously guarantee the density of spectral pixels and the quality of spectral reconstruction. Additionally, by constructing a new algorithm based on compressive sensing, the snapshot device can reconstruct ultraspectral imaging information ($\Delta\lambda$/$\lambda$~0.001) covering a broad (300-nm-wide) visible spectrum with an ultra-high center-wavelength accuracy of 0.04-nm standard deviation and spectral resolution of 0.8 nm. This scheme of reconfigurable metasurfaces makes the device can be directly extended to almost any commercial camera with different spectral bands to seamlessly switch the information between image and spectral image, and will open up a new space for the application of spectral analysis combining with image recognition and intellisense

Variational quantum circuit learning of entanglement purification in multi-degree-of-freedom

Quantum entanglement purification (EP) is a crucial technique for promising the effective function of entanglement channel in noisy large-scale quantum network. The previous EP protocols lack of a general circuit framework and become complicated to design in high-dimensional cases. In this paper, we propose a variational quantum circuit framework and demonstrate its feasibility of learning optimal protocols of EP in multi-degree-of-freedom (DoF). By innovatively introducing the additional circuit lines for representing the ancillary DoFs, e.g. space and time, the parameterized quantum circuit can effectively simulate the scalable EP process. As examples, well-known protocols in linear optics including PSBZ, HHSZ+ and etc., are learnt successfully with high fidelities and the alternative equivalent operations are discovered in low-depth quantum circuit. Our work pays the way for exploring the EP protocols with multi-DoF by quantum machine learning.Comment: 8 pages, 5 figures, comments are welcome

Advanced Research on cis-Neonicotinoids

cis-Neonicotinoids are a type of neonicotinoid, in which the nitro or the cyano group are in cis-configuration relative to heteroaromatic moiety, which show excellent activities against a range of insect species. This review covers cis-neonicotinoids with commercialization perspectives, structural optimization (phenylazoneonicotinoids and chlorothiazolyl analogues of Paichongding), modes of action studies, radiao-synthesis of Paichongding and Cycloxaprid, and photostability of neonicotinoids

Developing a class of dual atom materials for multifunctional catalytic reactions

Dual atom catalysts, bridging single atom and metal/alloy nanoparticle catalysts, offer more opportunities to enhance the kinetics and multifunctional performance of oxygen reduction/evolution and hydrogen evolution reactions. However, the rational design of efficient multifunctional dual atom catalysts remains a blind area and is challenging. In this study, we achieved controllable regulation from Co nanoparticles to CoN4 single atoms to Co2N5 dual atoms using an atomization and sintering strategy via an N-stripping and thermal-migrating process. More importantly, this strategy could be extended to the fabrication of 22 distinct dual atom catalysts. In particular, the Co2N5 dual atom with tailored spin states could achieve ideally balanced adsorption/desorption of intermediates, thus realizing superior multifunctional activity. In addition, it endows Zn-air batteries with long-term stability for 800 h, allows water splitting to continuously operate for 1000 h, and can enable solar-powered water splitting systems with uninterrupted large-scale hydrogen production throughout day and night. This universal and scalable strategy provides opportunities for the controlled design of efficient multifunctional dual atom catalysts in energy conversion technologies

Modulation of Metabolome and Bacterial Community in Whole Crop Corn Silage by Inoculating Homofermentative Lactobacillus plantarum and Heterofermentative Lactobacillus buchneri

The present study investigated the species level based microbial community and metabolome in corn silage inoculated with or without homofermentative Lactobacillus plantarum and heterofermentative Lactobacillus buchneri using the PacBio SMRT Sequencing and time-of-flight mass spectrometry (GC-TOF/MS). Chopped whole crop corn was treated with (1) deionized water (control), (2) Lactobacillus plantarum, or (3) Lactobacillus buchneri. The chopped whole crop corn was ensiled in vacuum-sealed polyethylene bags containing 300 g of fresh forge for 90 days, with three replicates for each treatment. The results showed that a total of 979 substances were detected, and 316 different metabolites were identified. Some metabolites with antimicrobial activity were detected in whole crop corn silage, such as catechol, 3-phenyllactic acid, 4-hydroxybenzoic acid, azelaic acid, 3,4-dihydroxybenzoic acid and 4-hydroxycinnamic acid. Catechol, pyrogallol and ferulic acid with antioxidant property, 4-hydroxybutyrate with nervine activity, and linoleic acid with cholesterol lowering effects, were detected in present study. In addition, a flavoring agent of myristic acid and a depression mitigation substance of phenylethylamine were also found in this study. Samples treated with inoculants presented more biofunctional metabolites of organic acids, amino acids and phenolic acids than untreated samples. The Lactobacillus species covered over 98% after ensiling, and were mainly comprised by the L. acetotolerans, L. silagei, L. parafarraginis, L. buchneri and L. odoratitofui. As compared to the control silage, inoculation of L. plantarum increased the relative abundances of L. acetotolerans, L. buchneri and L. parafarraginis, and a considerable decline in the proportion of L. silagei was observed; whereas an obvious decrease in L. acetotolerans and increases in L. odoratitofui and L. farciminis were observed in the L. buchneri inoculated silage. Therefore, inoculation of L. plantarum and L. buchneri regulated the microbial composition and metabolome of the corn silage with different behaviors. The present results indicated that profiling of silage microbiome and metabolome might improve our current understanding of the biological process underlying silage formation