Theoretical study of molecular conduction: I. Effective Green's function based on perturbation theory

There have been many experimental and theoretical studies on molecular conduction, as it is a fundamental parameter in the study of molecular‐scale electronics. We have investigated the features of molecular conduction using a Green's function method, which has often been used to solve problems in quantum transport and is also effective in elucidating electron transport in molecules. We have obtained the novel effective Green's functions, including the first‐order energy corrections, by accommodating the self‐energy of the electrodes as perturbation terms. Although these approximate Green's functions only provide information on the first‐order energy corrections, they can involve the elementary properties of molecular conduction. We propose a scheme for the analysis of the relations between molecular orbitals and their roles in molecular conduction and present analytical calculations for normal and cyclic polyenes. © 2005 Wiley Periodicals, Inc. Int J Quantum Chem, 2006     

Characterization of chromate conversion coating on AA7075‐T6 aluminum alloy

Chromate conversion coatings (CCCs) on AA7075‐T6 were characterized using scanning electron microscopy, focused ion beam sectioning and scanning transmission electron microscopy with nano‐electron dispersive spectroscopy line profiling. The thickness and composition of the CCC was different at different locations on the heterogeneous microstructure of AA7075‐T6. The coating formed on the matrix phase was much thicker than that formed on the coarse Al–Cu–Mg, Al–Fe–Cu and Mg–Si intermetallic particles. Nano‐electron dispersive spectroscopy line profiling indicated that the coating on the Al–Fe–Cu particles was similar to the CCC formed on the phase matrix, primarily a chromium oxide. However, the coatings on the Al–Cu–Mg and Mg–Si particles were mixed Al/Mg/Cr oxide and Mg oxide, respectively. The growth of CCC followed a linear‐logarithmic kinetic rate law. The observations of this study support the sol‐gel model of CCC formation. Copyright © 2004 John Wiley & Sons, Ltd.     

Dynamic light scattering and cryogenic transmission electron microscopy investigations on metallo-supramolecular aqueous micelles: evidence of secondary aggregation

Metallo-supramolecular diblock copolymers consisting of a polystyrene (PS) block connected to a poly(ethylene oxide) (PEO) block by a bis(terpyridine)ruthenium complex (PS₂₀-[Ru]-PEO _y ) were used to prepare aqueous micelles. The length of the PS block was kept constant, while two PEOs of different molecular weight were used. The resulting hydrated micelles and aggregates were characterized by a combination of cryogenic transmission electron microscopy (cryo-TEM) and dynamic light scattering measurements. The results were compared to those obtained for a covalent counterpart (PS₂₂-b-PEO₇₀). Cryogenic transmission electron microscopy allowed visualization of the PS core of the micelles. Moreover, the aggregates result from clustering of individual micelles.     

Molecular structure and spectral properties of thionaphthalimides

The molecular structure and absorption spectra of monothio- and dithio-naphthalimides were compared to their naphthalimide analogues using AM1, PM3 and ZINDO/S semiempirical quantum chemical methods. The substitution of the 4R-naphthalimide oxygen atoms by sulphur atoms resulted in a red-shift of the absorption spectra by Δλ_max60-65 and 100-140 nm, respectively. The thionated naphthalimide derivatives do not show observable fluorescence due to intersystem crossing to the triplet -states localised at the CS groups. The -absorption bands of monothioimides are located at 525-580 nm (ε=60-80) and those for dithioimides at 535-560 nm (ε=140-390) and 628-686 nm (ε=34-68). None of these transitions are solvent sensitive. The -transitions of N-phenylthioimides have also a small contribution from -states due to a partial conjugation between CS group and π-electronic system of the N-phenyl ring. As a result, the bands of aromatic substituted N-phenylthioimides are red-shifted as compared to those of the aliphatic N-methyl-thioimides.     

Microstructural and physicochemical study of the buried Fe/AlGaAs(100) interface by transmission electron microscopy and x‐ray emission spectroscopy

Among the magnetic metal/semiconductor contacts, the Fe/GaAs system has been widely studied owing to its potential applications in electronic devices. In contrast, there are not many studies concerning the Fe/Al_xGa_1?xAs contact, and in particular there are no reports concerning the changes induced in the interfacial zone by the presence of Al. In this work, thin polycrystalline iron films were deposited by ion beam sputtering at room temperature on a 300 nm thick Al_0.25Ga_0.75As layer grown by molecular beam epitaxy onto GaAs(001). X‐ray diffraction analysis showed that the iron films are polycrystalline, and indications of a (002) texture of the film were observed. The fine scale analysis of the interface was achieved by high‐resolution transmission electron microscopy (HRTEM) observations, the results of which are compared with the physicochemical information obtained from electron‐induced x‐ray emission spectroscopy, by analysing the Al 3p valence states at the Fe/Al_xGa_1?xAs interface. The HRTEM experiments on cross‐section samples indicate that the interfacial zone between iron and AlGaAs is limited to <1.5 nm in thickness. X‐ray emission spectroscopy showed the presence of Al atoms in an FeAl‐like environment at the interface, and the existence of wrong bonds and point defects. The estimated width of the perturbed interface (2.0 ± 0.5 nm) is in agreement with the HRTEM results. Copyright © 2003 John Wiley & Sons, Ltd.     

Novel aromatic carboxamides from dehydroabietylamine as potential fungicides: Design,synthesis and antifungal evaluation

The present study was carried out to design and synthesize a number of novel aromatic carboxamide derivatives of dehydroabietylamine. The preliminary antifungal assay indicated that most of title compounds displayed moderate to good antifungal activity toward the six fungal strains in vitro. Compounds 3i, 3q, 4b and 4d showed significant antifungal activity against Sclerotinia sclerotiorum, with EC₅₀ values ranging from 0.067 ～ 0.393 mg/L. Compounds 3i, 4b and 4d also showed pronounced mycelial growth inhibition activities against B. cinerea and A. solani. Furthermore, in the in vivo assay, compound 4b exhibited brilliant protective activity against S. sclerotiorum-infected rape leaves. Meanwhile, the in vivo bioassay on tomato plants infected by B. cinerea showed that compound 3i and 4d displayed excellent protective activity at 200 mg/L, which were near to boscalid. Primary mechanistic study revealed that 4b could inhibit sclerotia formation as well as reduce the exopolysaccharide level. SEM and TEM analysis indicated that 4b possessed a strong ability to destroy the surface morphology of mycelia, cell structure and seriously interfere with the growth of the fungal pathogen. In addition, 4b exhibited good inhibitory activity (IC₅₀ = 23.3 ± 1.6 μM) toward succinate dehydrogenase (SDH). Molecular modeling study confirmed the binding modes between compound 4b and SDH. The above antifungal results and fungicidal mechanism study revealed that this class of dehydroabietylamine derivatives could be potential SDH inhibitors and lead compounds for novel fungicides development.     

Zn-Al类水滑石的荧光性质

报道了一种新的Zn-Al类水滑石的荧光现象, 即在没有任何荧光物质插层的情况下, Zn-Al类水滑石本身就具有荧光性质, 其最大激发波长位于375 nm, 最大发射波长处于443 nm, 显示的荧光为蓝色可见光. 这一光学功能的发现将有助于类水滑石在光学材料领域中的进一步应用.     

AP Shadow Net: A Remote Sensing Shadow Removal Network Based on Atmospheric Transport and Poisson’s Equation

Shadow is one of the fundamental indicators of remote sensing image which could cause loss or interference of the target data. As a result, the detection and removal of shadow has already been the hotspot of current study because of the complicated background information. In the following passage, a model combining the Atmospheric Transport Model (hereinafter abbreviated as ATM) with the Poisson Equation, AP ShadowNet, is proposed for the shadow detection and removal of remote sensing images by unsupervised learning. This network based on a preprocessing network based on ATM, A Net, and a network based on the Poisson Equation, P Net. Firstly, corresponding mapping between shadow and unshaded area is generated by the ATM. The brightened image will then enter the Confrontation identification in the P Net. Lastly, the reconstructed image is optimized on color consistency and edge transition by Poisson Equation. At present, most shadow removal models based on neural networks are significantly data-driven. Fortunately, by the model in this passage, the unsupervised shadow detection and removal could be released from the data source restrictions from the remote sensing images themselves. By verifying the shadow removal on our model, the result shows a satisfying effect from a both qualitative and quantitative angle. From a qualitative point of view, our results have a prominent effect on tone consistency and removal of detailed shadows. From the quantitative point of view, we adopt the non-reference evaluation indicators: gradient structure similarity (NRSS) and Natural Image Quality Evaluator (NIQE). Combining various evaluation factors such as reasoning speed and memory occupation, it shows that it is outstanding among other current algorithms.     

An Efficient Supervised Deep Hashing Method for Image Retrieval

In recent years, searching and retrieving relevant images from large databases has become an emerging challenge for the researcher. Hashing methods that mapped raw data into a short binary code have attracted increasing attention from the researcher. Most existing hashing approaches map samples to a binary vector via a single linear projection, which restricts the flexibility of those methods and leads to optimization problems. We introduce a CNN-based hashing method that uses multiple nonlinear projections to produce additional short-bit binary code to tackle this issue. Further, an end-to-end hashing system is accomplished using a convolutional neural network. Also, we design a loss function that aims to maintain the similarity between images and minimize the quantization error by providing a uniform distribution of the hash bits to illustrate the proposed technique’s effectiveness and significance. Extensive experiments conducted on various datasets demonstrate the superiority of the proposed method in comparison with state-of-the-art deep hashing methods.     

A Spiking Neural Network Based on Retinal Ganglion Cells for Automatic Burn Image Segmentation

Burn is a common traumatic disease. After severe burn injury, the human body will increase catabolism, and burn wounds lead to a large amount of body fluid loss, with a high mortality rate. Therefore, in the early treatment for burn patients, it is essential to calculate the patient’s water requirement based on the percentage of the burn wound area in the total body surface area (TBSA%). However, burn wounds are so complex that there is observer variability by the clinicians, making it challenging to locate the burn wounds accurately. Therefore, an objective, accurate location method of burn wounds is very necessary and meaningful. Convolutional neural networks (CNNs) provide feasible means for this requirement. However, although the CNNs continue to improve the accuracy in the semantic segmentation task, they are often limited by the computing resources of edge hardware. For this purpose, a lightweight burn wounds segmentation model is required. In our work, we constructed a burn image dataset and proposed a U-type spiking neural networks (SNNs) based on retinal ganglion cells (RGC) for segmenting burn and non-burn areas. Moreover, a module with cross-layer skip concatenation structure was introduced. Experimental results showed that the pixel accuracy of the proposed reached 92.89%, and our network parameter only needed 16.6 Mbytes. The results showed our model achieved remarkable accuracy while achieving edge hardware affinity.