Chloridobis(1,10-phenanthroline-κ2 N,N′)copper(I) dichloridocopper(II)

The asymmetric unit of the title compound, [CuCl(C12H8N2)2]·[CuCl2], contains two complex Cu(II) cations and two cuprate(I) anions. The Cu(II) atom is coordinated by two phenanthroline (phen) mol­ecules and one chloride anion in a distorted trigonal–bipyramidal geometry. The Cu(II) complex cations form layers through π–π stacking [interplanar distance = 3.481 (2) Å]. The dichloridocuprate(I) anions are located between the layers, forming a sandwich-like structure

MLF-DET: Multi-Level Fusion for Cross-Modal 3D Object Detection

In this paper, we propose a novel and effective Multi-Level Fusion network, named as MLF-DET, for high-performance cross-modal 3D object DETection, which integrates both the feature-level fusion and decision-level fusion to fully utilize the information in the image. For the feature-level fusion, we present the Multi-scale Voxel Image fusion (MVI) module, which densely aligns multi-scale voxel features with image features. For the decision-level fusion, we propose the lightweight Feature-cued Confidence Rectification (FCR) module which further exploits image semantics to rectify the confidence of detection candidates. Besides, we design an effective data augmentation strategy termed Occlusion-aware GT Sampling (OGS) to reserve more sampled objects in the training scenes, so as to reduce overfitting. Extensive experiments on the KITTI dataset demonstrate the effectiveness of our method. Notably, on the extremely competitive KITTI car 3D object detection benchmark, our method reaches 82.89% moderate AP and achieves state-of-the-art performance without bells and whistles

Biomineralized Nano-Assemblies of Poly(Ethylene Glycol) Derivative with Lanthanide Ions as Ratiometric Fluorescence Sensors for Detection of Water and Fe³⁺ Ions

An effective strategy was developed to fabricate novel lanthanide ions–pyromellitic acid–methoxy poly(ethylene glycol) (Ln-PMA-MPEG) nano-assemblies. The amphiphilic partially esterified derivative (PMA-MPEG) of pyromellitic acid with methoxy poly(ethylene glycol) was designed and synthesized via the coupling reaction. Ln-PMA-MPEG nano-assemblies were rapidly fabricated using PMA-MPEG as a polymer ligand with Eu3+ ions or mixed Eu3+/Tb3+ ions through biomimetic mineralization in neutral aqueous systems. The size of the as-prepared materials could be designed in the range 80–200 nm with a uniform distribution. The materials were readily dispersed in various solvents and displayed visible color variations and different photoluminescent properties for solvent recognition. The mixed Eu/Tb-PMA-MPEG nanomaterials were investigated as ratiometric sensors for the detection of trace water in DMF and Fe3+ ions in aqueous solutions. The sensor materials can quantitatively detect trace water in DMF from 0% to 10% (v/v). The resultant materials also display a strong correlation between the double luminescence intensity ratios (ITb/IEu) and Fe3+ concentration, with a good linear detection concentration in the range of 0–0.24 mM and a limit of detection of 0.46 μM, and other metal ions did not interfere with the sensing mechanism for Fe3+ ions. The novel nano-assemblies have potential applications as ratiometric fluorescent nanosensors in the chemical industry as well as in biomedical fields

Regular Neural Network Based Machining Parameters Optimization Model Of NC Machine Tool

Energy consumption process is the basis for energy efficiency improvement of machine tools. Most of the existing researches focus on the static modelling of energy consumption of a machine tool; however, there are a few studies that paid attention to that how process parameters influence the energy consumption of machine tools during processing. It is noted that the process parameters can be selected to reduce energy consumption during machining processes without additional investment. In this paper, a characteristic energy consumption model for NC machine tool was proposed. Then, the mapping rule between process parameters and energy consumption of machine tool was studied, and the model was solved with the regular neural network (RNN). Finally, the result was verified with an experiment of milling the surface of aluminium block, which can effectively improve the energy efficiency of machine tool. The experiment results are shown that regular neural network is used to optimize the process parameters and process the same machining characteristics; we analyze the in machining process of machine tool based on the three cutting parameters, and then, a model of energy consumption. We employ to learn, and use this trained model to select optimal parameters

Stimulus-Responsiveness of Thermo-Sensitive Polymer Hybridized with N-Doped Carbon Quantum Dots and Its Applications in Solvent Recognition and Fe3+ Ion Detection

To fabricate N-CQDs hybrid thermo-sensitive polymer (poly-N-CQDs), N-doped carbon quantum dots (N-CQDs) with strong blue fluorescence and poly(N-isopropylacrylamide-co-acrylic acid) (poly(NIPAAm-co-AAc)) copolymer with thermo-sensitivity were synthesized, respectively. Subsequently, the coupling reaction between. the -COOH groups of poly(NIPAAm-co-AAc) and the -NH2 groups on the surface of the N-CQDs was carried out. The fluorescence spectra show that the coil-globule transition of the poly-N-CQDs coincided with intensity changes in the scattering peak at excitation wavelength with the temperature variations. The phase transition temperature and the fluorescent intensity of poly-N-CQDs can be regulated by modulating the composition and concentration of poly-N-CQDs as well as the temperature and pH of the local medium. The thermo-sensitivity and fluorescent properties of the poly-N-CQDs displayed good stability and reversibility. The fluorescence intensity and emission wavelengths of the poly-N-CQDs significantly changed in different solvents for solvent recognition. The poly-N-CQDs was employed as a fluorescent probe for Fe3+ detection ranging from 0.025 to 1 mM with a limit of detection (LOD) of 9.49 μM. The hybrid polymer materials have the potential to develop an N-CQDs-based thermo-sensitive device or sensor

Novel Surface-Enhanced Raman Spectroscopy Techniques for DNA, Protein and Drug Detection

Surface-enhanced Raman spectroscopy (SERS) is a vibrational spectroscopic technique in which the Raman scattering signal strength of molecules, absorbed by rough metals or the surface of nanoparticles, experiences an exponential growth (103–106 times and even 1014–1015 times) because of electromagnetic or chemical enhancements. Nowadays, SERS has attracted tremendous attention in the field of analytical chemistry due to its specific advantages, including high selectivity, rich informative spectral properties, nondestructive testing, and the prominent multiplexing capabilities of Raman spectroscopy. In this review, we present the applications of state-of-the-art SERS for the detection of DNA, proteins and drugs. Moreover, we focus on highlighting the merits and mechanisms of achieving enhanced SERS signals for food safety and clinical treatment. The machine learning techniques, combined with SERS detection, are also indicated herein. This review concludes with recommendations for future studies on the development of SERS

A new species of beaded lacewings (Neuroptera, Berothidae) from mid-Cretaceous Myanmar amber

A new species of Berothidae, Jersiberotha musivum sp. nov., is described and illustrated from mid-Cretaceous (lowest Cenomanian) Myanmar amber. It is easily distinguished from other species of Berothidae by the configuration of the wing venation including: forewing with distinct areas of infuscation surrounding cross-veins and vein forks, all cross-veins simple prior to ScP-RA fusion, presence of two cross-veins ra-rp; absence of inner or outer graduate series of cross-veins; RP with three branches; and absence of ma-mp cross-veins and cua-cup cross-veins; while hind wing has cross-vein 1r-m absent. The previous diagnoses of Iceloberotha Grimaldi, 2000 and Jersiberotha Grimaldi, 2000 are quite unclear because some characters occur mosaically in both genera. In order to solve this problem and distinguish J. musivum from other species in the family, a new key to species of Berothidae from Myanmar amber has been provided and the diagnoses of Iceloberotha and Jersiberotha have been revised