Showing papers by "Sheng Tang published in 2023"
TL;DR: Ionic liquids have gained widespread attention and popularity in many areas of analytical chemistry, including modern sample preparation technologies as mentioned in this paper , and are suitable for use in extraction or sample preparation procedures represent one such alternative.
Abstract: The development of preferably simple, fast, sensitive, selective and relatively inexpensive methods for the determination of pesticide residues in food and environmental samples is essential. The design of methods with the above attributes can pose challenges, especially for complex food and environmental matrices. In this sense, isolation and pre-concentration of pesticides in such matrices are required, and are considered an important step towards the determination of low levels of these analytes. Another characteristic of an analytical approach that is gaining increasing focus is the "greenness" of the overall method, most notably in the avoidance of the use of potentially harmful organic solvents. In this vein, scientists are looking for environmentally friendlier alternatives to organic solvents. Ionic liquids (ILs) suitable for use in extraction or sample preparation procedures represent one such alternative. A better understanding of the nature of the IL-based extractions and their impact on pesticide analysis is important for their successful applicability. Due to their unique properties such as polarity, adjustable viscosity, miscibility and surface tension, and their interactions thereof (with respect to sample matrices and analytes) ILs have gained widespread attention and popularity in many areas of analytical chemistry, including modern sample preparation technologies. This mini-review critically discusses recent and/or interesting applications of ILs in the microextraction of pesticides. The topics covered include a brief overview of ILs, their structures and interactions, and their applications as extractants in liquid-phase microextraction and solid-phase microextraction.
TL;DR: The use of 3D printed devices in the extraction of various analytes and in different methods to address, and improve upon some current extraction (and microextraction) problems, issues and concerns is discussed in this article .
19 Jan 2023
TL;DR: In this article , the authors established and evaluated a method of enriching bacteriophages in natural water based on ferric trichloride-polyvinylidene fluoride (FeCl3-PVDF)membrane filter.
Abstract: Objective: To establish and evaluate a method of enriching bacteriophages in natural water based on ferric trichloride-polyvinylidene fluoride (FeCl3-PVDF)membrane filter. Methods: Based on the principle of flocculation concentration, the method of recovering bacteriophage from water sample was established by using iron ion flocculation combined with membrane filter. The titer of phage was determined by Agar double layer method. The recovery efficiency of phage was detected by phage fluorescence staining and real-time fluorescence PCR reaction. Water samples from different sources were collected for simulation experiment to evaluate the enrichment effect. At the same time, the sewage discharged from hospitals was taken as the actual water sample, and the common clinical drug-resistant bacteria were used as the host indicator bacteria to further analyze the enrichment effect of FeCl3-PVDF membrane filter rapid enrichment method on the bacteriophage in natural water samples. Results: The method of enrichment of bacteriophages in natural water by iron ion concentration 50 mg/L and PVDF membrane filter was established. The recovery rate of this method for bacteriophage was 93%-100%. Under the multi-functional microscope, it was found that the bacteriophage of the enriched water sample increased significantly and the fluorescence value of the enriched water sample determined by the enzyme labeling instrument was about 13 times as high as that before enrichment. After concentration of the actual water samples from the hospital drainage, the positive rate of bacteriophage isolation in the concentrated group and the non-concentrated group was 23% and 4%, and the fluorescence value in the concentrated group was 2-24 times as high as that of the non-concentrated group. Conclusion: The method of FeCl3-PVDF membrane filter is a simple, efficient and rapid method for enriching bacteriophages in different water samples.
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TL;DR: In this article , a broadband compact small active magnetic antenna used to receive Loran-C signal outdoors and indoors is designed, which can be divided into a magnetic antenna part and an active circuit part.
Abstract: The Long-Range Navigation-C (Loran-C) system is an internationally standardized positioning, navigation, and timing service system based on a 100-kHz carrier. The development of the system antenna has always been one of the key research objects in the development of its receiver. In this article, a broadband compact small active magnetic antenna used to receive Loran-C signal outdoors and indoors is designed, which can be divided into a magnetic antenna part and an active circuit part. In the antenna part, this article designs a new “H”-shaped magnetic antenna with a high quality factor from two aspects: material selection and simulation of antenna shape. In the active circuit part, the amplifier and filter are designed by an operational amplifier, and the frequency selection is realized by a single-tube double tuning circuit to improve the signal-to-noise ratio (SNR). Through the above designs, the effective reception of the Loran-C signal is realized outdoors and indoors. Finally, the whole Loran-C active magnetic antenna is processed and tested. It can completely receive a Loran-C signal, with a clear pulse group, and the frequency point of the signal is 99.21 kHz. The length of the antenna is 2.75 cm, and the size of the circuit board is $10\times3$ cm. The SNR of the outdoor environment is 12.04 dB and that of the indoor is 16.48 dB.
TL;DR: The Light charged Particle Detector Array (LPDA) as mentioned in this paper was designed for the study of (n, lcp) reactions at Back-n white neutron source at the China Spallation Neutron Source (CSNS).
Abstract: The Back-n white neutron source at the China Spallation Neutron Source (CSNS) provides neutrons in the continuous energy region from 0.5 eV to 200 MeV. A spectrometer named Light charged Particle Detector Array (LPDA) is designed for the study of (n, lcp) reactions at Back-n. The main detector of the LPDA spectrometer, a 16-unit ΔE-ΔE-E telescope array, is composed of two arrays of 8-unit ΔE-ΔE-E telescope. Each telescope unit consists of a Low-Pressure Multi-Wire Proportional Chamber (LPMWPC), a Si-PIN detector, and a CsI(Tl) scintillator detector. In 2021, a neutron-proton (n-p) scattering reaction cross-section measurement experiment was accomplished as the first experiment of the telescope array. Protons can be clearly identified in the ΔE-E spectrum (LPMWPC + Si-PIN) and the ΔE-E spectrum (Si-PIN + CsI(Tl)). Cross sections of the n-p scattering reaction in the neutron energy range of several MeV are extracted. The ΔE-E method also provides new measurement opportunities for many-body neutron induced light charged-particle emission reactions.
TL;DR: Zhang et al. as discussed by the authors proposed a partially-shared multi-task representation learning method, which jointly preserves complementary and sharable knowledge between discriminative and semantic-relevant representations for generalized zero-shot learning.
Abstract: Generalized Zero-Shot Learning (GZSL) holds significant research importance as it enables the classification of samples from both seen and unseen classes. A prevailing approach for GZSL is learning transferable representations that can generalize well to both seen and unseen classes during testing. This approach encompasses two key concepts: discriminative representations and semantic-relevant representations. “Semantic-relevant” facilitates the transfer of semantic knowledge using pre-defined semantic descriptors, while “discriminative” is crucial for accurate category discrimination. However, these two concepts are arguably inherently conflicting, as semantic descriptors are not specifically designed for image classification. Existing methods often struggle with balancing these two aspects and neglect the conflict between them, leading to suboptimal representation generalization and transferability to unseen classes. To address this issue, we propose a novel partially-shared multi-task representation learning method, termed PS-GZSL, which jointly preserves complementary and sharable knowledge between these two concepts. Specifically, we first propose a novel perspective that treats the learning of discriminative and semantic-relevant representations as optimizing a discrimination task and a visual-semantic alignment task, respectively. Then, to learn more complete and generalizable representations, PS-GZSL explicitly factorizes visual features into task-shared and task-specific representations and introduces two advanced tasks: an instance-level contrastive discrimination task and a relation-based visual-semantic alignment task. Furthermore, PS-GZSL employs Mixture-of-Experts (MoE) with a dropout mechanism to prevent representation degeneration and integrates a conditional GAN (cGAN) to synthesize unseen features for estimating unseen visual features. Extensive experiments and more competitive results on five widely-used GZSL benchmark datasets validate the effectiveness of our PS-GZSL.