Advances in Chip-Based Quantum Key Distribution

Quantum key distribution (QKD), guaranteed by the principles of quantum mechanics, is one of the most promising solutions for the future of secure communication. Integrated quantum photonics provides a stable, compact, and robust platform for the implementation of complex photonic circuits amenable to mass manufacture, and also allows for the generation, detection, and processing of quantum states of light at a growing system’s scale, functionality, and complexity. Integrated quantum photonics provides a compelling technology for the integration of QKD systems. In this review, we summarize the advances in integrated QKD systems, including integrated photon sources, detectors, and encoding and decoding components for QKD implements. Complete demonstrations of various QKD schemes based on integrated photonic chips are also discussed.     

Bayesian Variable Selection and Estimation in Semiparametric Simplex Mixed-Effects Models with Longitudinal Proportional Data

In the development of simplex mixed-effects models, random effects in these mixed-effects models are generally distributed in normal distribution. The normality assumption may be violated in an analysis of skewed and multimodal longitudinal data. In this paper, we adopt the centered Dirichlet process mixture model (CDPMM) to specify the random effects in the simplex mixed-effects models. Combining the block Gibbs sampler and the Metropolis–Hastings algorithm, we extend a Bayesian Lasso (BLasso) to simultaneously estimate unknown parameters of interest and select important covariates with nonzero effects in semiparametric simplex mixed-effects models. Several simulation studies and a real example are employed to illustrate the proposed methodologies.     

DA-LSTM-VAE: Dual-Stage Attention-Based LSTM-VAE for KPI Anomaly Detection

To ensure the normal operation of the system, the enterprise’s operations engineer will monitor the system through the KPI (key performance indicator). For example, web page visits, server memory utilization, etc. KPI anomaly detection is a core technology, which is of great significance for rapid fault detection and repair. This paper proposes a novel dual-stage attention-based LSTM-VAE (DA-LSTM-VAE) model for KPI anomaly detection. Firstly, in order to capture time correlation in KPI data, long–short-term memory (LSTM) units are used to replace traditional neurons in the variational autoencoder (VAE). Then, in order to improve the effect of KPI anomaly detection, an attention mechanism is introduced into the input stage of the encoder and decoder, respectively. During the input stage of the encoder, a time attention mechanism is adopted to assign different weights to different time points, which can adaptively select important input sequences to avoid the influence of noise in the data. During the input stage of the decoder, a feature attention mechanism is adopted to adaptively select important latent variable representations, which can capture the long-term dependence of time series better. In addition, this paper proposes an adaptive threshold method based on anomaly scores measured by reconstruction probability, which can minimize false positives and false negatives and avoid adjustment of the threshold manually. Experimental results in a public dataset show that the proposed method in this paper outperforms other baseline methods.     

Magnetic Molecularly Imprinted Polymers for the Rapid and Selective Extraction and Detection of Methotrexatein Serum by HPLC-UV Analysis

In this work, novel selective recognition materials, namely magnetic molecularly imprinted polymers (MMIPs), were prepared. The recognition materials were used as pretreatment materials for magnetic molecularly imprinted solid-phase extraction (MSPE) to achieve the efficient adsorption, selective recognition, and rapid magnetic separation of methotrexate (MTX) in the patients’ plasma. This method was combined with high-performance liquid chromatography–ultraviolet detection (HPLC–UV) to achieve accurate and rapid detection of the plasma MTX concentration, providing a new method for the clinical detection and monitoring of the MTX concentration. The MMIPs for the selective adsorption of MTX were prepared by the sol–gel method. The materials were characterized by transmission electron microscopy, Fourier transform-infrared spectrometry, X-ray diffractometry, and X-ray photoelectron spectrometry. The MTX adsorption properties of the MMIPs were evaluated using static, dynamic, and selective adsorption experiments. On this basis, the extraction conditions were optimized systematically. The adsorption capacity of MMIPs for MTX was 39.56 mgg⁻¹, the imprinting factor was 9.40, and the adsorption equilibrium time was 60 min. The optimal extraction conditions were as follows: the amount of MMIP was 100 mg, the loading time was 120 min, the leachate was 8:2 (v/v) water–methanol, the eluent was 4:1 (v/v) methanol–acetic acid, and the elution time was 60 min. MTX was linear in the range of 0.00005–0.25 mg mL⁻¹, and the detection limit was 12.51 ng mL⁻¹. The accuracy of the MSPE–HPLC–UV method for MTX detection was excellent, and the result was consistent with that of a drug concentration analyzer.     

Comparison on Protein Bioaccessibility of Soymilk Gels Induced by Glucono-δ-Lactone and Lactic Acid Bacteria

In this study, the protein bioaccessibility of soymilk gels produced by the addition of glu-cono-δ-lactone (GDL) and fermentation with lactic acid bacteria (LAB) was examined using an in vitro gastrointestinal simulated digestion model. The in vitro protein digestibility, soluble protein content, free amino acids contents, degree of hydrolysis, electrophoretic patterns, and peptide content were measured. The results suggested that acid-induced soymilk gel generated by GDL (SG) showed considerably reduced in vitro protein digestibility of 75.33 ± 1.00% compared to the soymilk gel induced by LAB (SL) of 80.57 ± 1.53% (p < 0.05). During the gastric digestion stage, dramatically higher (p < 0.05) soluble protein contents were observed in the SG (4.79–5.05 mg/mL) than that of SL (4.31–4.35 mg/mL). However, during the later intestinal digestion phase, the results were the opposite. At the end of the gastrointestinal digestion phase, the content of small peptides was not significantly different (p > 0.05) between the SL (2.15 ± 0.03 mg/mL) and SG (2.17 ± 0.01 mg/mL), but SL showed higher content of free amino acids (20.637 g/L) than that of SG (19.851 g/L). In general, soymilk gel induced by LAB had a higher protein bioaccessibility than the soymilk gel coagulated by GDL.     

H-Bonding Room Temperature Phosphorescence Materials via Facile Preparation for Water-Stimulated Photoluminescent Ink

Pure organic room-temperature phosphorescence (RTP) materials built upon noncovalent interactions have attracted much attention because of their high efficiency, long lifetime, and stimulus-responsive behavior. However, there are limited reports of noncovalent RTP materials because of the lack of specific design principles and clear mechanisms. Here, we report on a noncovalent material prepared via facile grinding that can emit fluorescence and RTP emission differing from their components’ photoluminescent behavior. Exciplex can be formed during the preparation process to act as the minimum emission unit. We found that H-bonds in the RTP system provide restriction to nonradiative transition but also enhance energy transformation and energy level degeneracy in the system. Moreover, water-stimulated photoluminescent ink is produced from the materials to achieve double-encryption application with good resolution.     

Thermostability Improvement of L-Asparaginase from Acinetobacter soli via Consensus-Designed Cysteine Residue Substitution

To extend the application range of L-asparaginase in food pre-processing, the thermostability improvement of the enzyme is essential. Herein, two non-conserved cysteine residues with easily oxidized free sulfhydryl groups, Cys8 and Cys283, of Acinetobacter soli L-asparaginase (AsA) were screened out via consensus design. After saturation mutagenesis and combinatorial mutation, the mutant C8Y/C283Q with highly improved thermostability was obtained with a half-life of 361.6 min at 40 °C, an over 34-fold increase compared with that of the wild-type. Its melting temperature (T_m) value reaches 62.3 °C, which is 7.1 °C higher than that of the wild-type. Molecular dynamics simulation and structure analysis revealed the formation of new hydrogen bonds of Gln283 and the aromatic interaction of Tyr8 formed with adjacent residues, resulting in enhanced thermostability. The improvement in the thermostability of L-asparaginase could efficiently enhance its effect on acrylamide inhibition; the contents of acrylamide in potato chips were efficiently reduced by 86.50% after a mutant C8Y/C283Q treatment, which was significantly higher than the 59.05% reduction after the AsA wild-type treatment. In addition, the investigation of the mechanism behind the enhanced thermostability of AsA could further direct the modification of L-asparaginases for expanding their clinical and industrial applications.     

Front Transparent Passivation of CIGS-Based Solar Cells via AZO

We report a novel strategy for the front passivation of solar cells via aluminum-doped zinc oxide (AZO) films in the case of CIGS solar cells, leading to the highest efficiency of 15.07% without alkali metal post treatment and anti−reflective layer. The good passivation of CIGS solar cells via AZO films is attributed to the field passivation simulated by the SCAPS−1D software. The AZO films also exhibit high optical transparency both in visible and near infrared wavelength region, high conductivity, and cost−effective fabrication advantage. Importantly, the AZO films are deposited at room temperature via radio−frequency magnetron sputtering, showing that the AZO films are also applicable to other solar cells such as perovskite solar cells. Our work is of significance for advancing the development of CIGS−based photovoltaics devices by the well front passivation of AZO. The wide application of AZO in other solar cells such as perovskite solar cells and related tandem solar cells may also accelerate the development of these solar cells because of potential passivation of AZO, low deposition temperature, and high optical transparency of AZO.     

Enhanced Visible-Light-Driven Photocatalytic Activity by Fe(Ш)-Doped Graphitic C3N4

Fe(Ш)-doped graphitic carbon nitride (Fe(Ш)-CN) photocatalysts with various Fe(Ш) ions content were prepared via ultrasonic method. Detailed physical characterization indicated that Fe(Ш) ions had been successfully doped into the frame of g-C₃N₄. The photocatalytic activities were investigated, and methyl orange (MO) and tetracycline hydrochloride (TC) were used as the targeted pollutants. The as-prepared Fe(Ш)-CN materials exhibited higher photocatalytic activities than those of the pure g-C₃N₄. Specifically, the degradation rate of 2Fe(Ш)-CN under visible light was 2.06 times higher for MO and 2.65 times higher for TC than that of g-C₃N₄. The increased photocatalytic activities of Fe(Ш)-CN were mainly attributed to the enhanced light absorption ability and the rapid separation of photogenerated carriers. Moreover, the importance of active species during the reaction process was also explored, and the results indicated that •O₂⁻ is the main active species.     

High-Throughput Preparation and Machine Learning Screening of a Blue-Phase Liquid Crystal Based on Inkjet Printing

Blue-phase liquid crystal (BPLC) is considered as the next-generation liquid crystal display material, but its practical application is seriously affected by a narrow temperature range and a long research period. In this paper, we used inkjet printing technology to prepare BPLC materials with high throughput, and try to use machine vision technology to test BPLC with high throughput. The “standard curve method” for establishing each printing channel and the “vector matching method” for searching the chromaticity value of the minimum distance were proposed to improve the accuracy of inkjet printing BPLC materials. For a large number of sample-phase images, we propose a machine learning method to identify the liquid crystal phase. In this paper, for the first time, the high-throughput preparation and high-throughput detection of 1080 BPLC samples with five common components by a comprehensive experimental method has been successfully realized. The results are helpful to improve the research efficiency of blue-phase materials and provide a theoretical basis and practical guidance for rapid screening of multi-component BPLC materials.