Strong Anti-interference Ability Research Articles

Highly-Efficient Selection of Aptamers for Quantitative Fluorescence Detecting Multiple IAV Subtypes.

Influenza A virus (IAV) can cause infectious respiratory diseases in humans and animals. IAVs mutate rapidly through antigenic drift and shift, resulting in the emergence of numerous IAV subtypes and significant challenges for IAV detection. Therefore, achieving the simultaneous detection of multiple IAVs is crucial. In this work, three specific aptamers targeting the hemagglutination (HA) protein of the influenza A H5N1, H7N9, and H9N2 viruses were screened using a multichannel magnetic microfluidic chip. The aptamers exhibit nanomolar affinity and excellent specificity for the HA protein of H5N1, H7N9, and H9N2 viruses. Furthermore, three specific aptamers were truncated and labeled with different fluorescence markers to realize fluorescence quantitative detection of influenza A H5N1, H7N9, and H9N2 viruses through an aptamer sandwich assay in 1 h. The limit of detection (LOD) of the developed method is 0.38 TCID50/mL for the H5N1 virus, 0.75 TCID50/mL for the H7N9 virus, and 1.14 TCID50/mL for the H9N2 virus. The detection method has excellent specificity, strong anti-interference ability, and good reproducibility. This work provides a sensitive quantitative detection method for the H5N1, H7N9, and H9N2 viruses, enabling quantitative fluorescence detection for multiple IAV subtypes.

Multitype view of knowledge contrastive learning for recommendation

Graph Neural Networks (GNNs) are playing an increasingly vital role in the field of recommender systems. To improve knowledge perception within GNNs, contrastive learning has been applied and has proven to be highly effective. GNNs have the ability to aggregate diverse knowledge and integrate topologies, while contrastive learning seeks supervisory signals from the model data. The combination of GNNs and contrastive learning can improve recommendations. However, thoughtless or incomplete contrastive learning settings limit the effectiveness of GNNs-based recommender systems in learning knowledge from knowledge and interaction graphs. To better exploit the valuable information within knowledge graphs, we propose a novel multitype view of knowledge contrastive learning for recommendations (MVKC) model.The MVKC model generates hierarchical views and augmented views in two modules, performing cross-hierarchical-view and cross-augmented-view contrastive learning and mining graph features in a self-supervised manner. The hierarchical views consist of global and local parts at multiple levels, while the augmented views are fused from the augmented knowledge graph and augmented interaction graph in our augmented processing. These features allow the MVKC model to alleviate the sparsity of user-item interaction graphs, suppress knowledge graph noise, and filter long-tail entities, which has been proven extremely important for a recommendation. The MVKC model also has strong anti-interference ability and robustness, which is crucial for a well-established model. Our experiments with three public datasets demonstrate that the MVKC model outperforms current state-of-the-art methods.

Novel electrochemical sensor based on MoS2NSs@AgNPs/MWCNTs-COOH nanocomposites for effective detection of Penciclovir

Abnormal levels of penciclovir (PCV) in humans may lead to inflammation and renal impairment, and its frequent detection in the aquatic environment poses environmental hazards. Consequently, timely detection and regulation of PCV concentrations in the human body are crucial for drug research, disease diagnosis, and environmental protection. In this study, a PCV electrochemical sensing platform based on MoS2NSs@AgNPs/MWCNTs-COOH was constructed by in situ reduction of silver nanoparticles (AgNPs) on MoS2 nanosheets (MoS2NSs) and complexed with carboxylated multi-walled carbon nanotubes (MWCNTs-COOH). The synergistic effect of highly conductive AgNPs, high surface area MoS2NSs, and MWCNTs-COOH increased the electron transfer rate at the electrode surface and thus effectively promoted the electrooxidation process of PCV. The electrochemical performance of the sensor was investigated and optimized by cyclic voltammetry (CV) and linear scanning voltammetry (LSV). The linear response of PCV under optimal conditions was in the range of 0.5–200.0 µM, with a limit of detection (LOD) of 0.039 µM (S/N = 3), and recoveries of 98.1–101.1 %, 95.6–101.6 %, 97.0–103.6 %, and 96.0–100.9 %. The results show that the constructed MoS2NSs@AgNPs/MWCNTs-COOH /GEC sensing platform has the advantages of high sensitivity, fast response speed, and strong anti-interference ability. This work is the first to develop an electrochemical sensing platform for PCV based on MoS2NSs@AgNPs/MWCNTs-COOH, providing a new PCV quantitative detection solution.

Facile coupling of plasmonic Au-NPs on ZnS NFs as a robust SERS substrate for toluidine blue detection and degradation

BackgroundThe robustness and sensitivity of the surface-enhanced Raman spectroscopy (SERS) technique heavily relies on the development of SERS active materials. A hybrid of semiconductor and plasmonic metals is highly effective as a SERS substrate, which enables the trace level detection of various organic pollutants. ResultsThis approach demonstrates the photodeposition of plasmonic gold nanoparticles (Au-NPs) on the surface of semiconductor-zinc sulfide nanoflowers (ZnS NFs), grown via the hydrothermal route. The synergistic contribution of the charge-transfer phenomenon and localized surface plasmon resonance of the Au-NPs/ZnS NFs makes it an ideal SERS substrate for the detection of organic pollutants, toluidine blue (TB). The proposed material has a high SERS enhancement factor (109), low limit of detection (10−11 M), good reproducibility, selectivity and strong anti-interference ability. Furthermore, the practicability of the Au-NPs/ZnS NFs is explored in real-time water samples, which are obtained with the satisfactory recovery rates. Additionally, the UVC light illumination on the Au-NPs/ZnS NFs has efficiently degraded TB within a time period of 150 min. Significance and noveltyThese finding demonstrate the significance of the proposed Au-NPs/ZnS NFs for SERS based detection and degradation of organic pollutants in real-time samples, highlighting their potential in monitoring and treating water pollutants in wastewater.

Research on Distributed Dual-Wheel Electric-Drive Fuzzy PI Control for Agricultural Tractors

In order to solve the problem that, when the vehicle speed of an agricultural distributed dual-wheel electric-drive tractor changes or the system is disturbed by off-load, the traditional PI control cannot be adjusted in time, resulting in the overshoot of steering control or control delay, meaning it then cannot travel along the target trajectory quickly and accurately, a parameter-adaptive dual-dimensional fuzzy PI speed and steering adjustment controller was proposed, which can adjust the PI parameters in real time based on the deviation between vehicle speed, steering, and reference value, as well as the rate of deviation change. Firstly, based on the operational characteristics of agricultural tractors, a dynamic model of a distributed dual-wheel tractor was established, and a hardware-in-the-loop (HIL) test bench was set up. Fuzzy PI controller algorithms for vehicle speed and steering were designed and developed. In addition, simulations and tests were carried out under no-load and off-load tractor operating conditions with MATLAB/Simulink, respectively. The results indicate that, compared with a traditional PI controller, the fuzzy PI controller exhibits a faster control response and better robustness, reducing overshoot by approximately 60% and the steady-state response time by approximately 25%. When subjected to off-load disturbances, the maximum trajectory offset is controlled within 0.08 m, and the maximum trajectory offset is reduced by 45% compared with a traditional PI controller; therefore, the fuzzy PI control algorithm proposed in this paper makes the tractor’s running trajectory more stable and has stronger anti-interference ability towards off-load disturbances.

Regulation on the ligand fluorescence in UiO-66-Y metal-organic frameworks for effective detection of nitro-aromatic explosives

Rapid, sensitive and highly selective detection of nitro-aromatic explosives (NAEs) has become one of the most pressing environmental and safety issues. Metal-organic frameworks (MOFs) offer new possibilities for the development of new photoactive materials with excellent sensing properties. In this paper, three robust UiO-66-type fluorescent MOFs were prepared from the self-assembly of yttrium cations (Y3+) and linear dicarboxylic acid ligands, named terephthalic acid (BDC), 2-hydroxyterephthalic acid (OHBDC) and 2,5-dihydroxyterephthalic acid (DHBDC). The solid and solution fluorescence properties of three MOFs were successfully modulated by regulating the number of –OH functional groups from zero to two. At 370 nm excitation, Y-BDC MOF without –OH group was almost non-fluorescent, Y-OHBDC MOF with one –OH group emitted blue fluorescence, and Y-DHBDC MOF with two –OH groups emitted green fluorescence. Thanks to the suitable porous structures and stable frameworks, Y-OHBDC and Y-DHBDC MOFs both can quantificationally detect at least seven nitro-aromatic explosives (NAEs) by a fluorescence quenching process. Remarkably, the detection performance for 2,4,6-trinitrophenol (TNP) is the most outstanding with the KSV values of 8.4 × 104 M−1 and 2.1 × 105 M−1. Fast response, high sensitivity, strong anti-interference ability and good reusability make UiO-66-Y MOFs potential fluorescent sensors for TNP.

Activation of peroxymonosulfate by β-FeOOH@Cia-MoS2 for enhancing degradation of tetracycline: Significant roles of surface functional groups and Fe/Mo redox reactions

Vertically oriented interstitial atom carbon-anchored molybdenum disulfide (Cia-MoS2) nanospheres loaded with iron oxyhydroxide (β-FeOOH) were proposed for modulating the surface catalytic activity and stability of the unsaturated catalytic system. The β-FeOOH@Cia-MoS2 efficiently activated peroxymonosulfate (PMS) to degrade 95.4% of tetracycline (TC) within 30 min, owing to the more sulfur vacancies, higher surface hydroxyl density, redox ability and electronic transmission rate of β-FeOOH@Cia-MoS2. According to the characterization and analysis data, the multiple active sites (Fe, Mo and S sites) and oxygen-containing functional groups (CO, –OH) of β-FeOOH@Cia-MoS2 could promote the activation of PMS to form reactive oxygen species (ROS). The oxidation cycle of Fe(II)/Fe(III) and Mo(IV)/Mo(VI), the electron transfer mediator of rich sulfur vacancies, as well as oxygen-containing functional groups on the surface of β-FeOOH@Cia-MoS2 synergistically promoted the formation of ROS (1O2, FeIVO, SO4•- and •OH), among which 1O2 was the main active oxidant. In particular, the β-FeOOH@Cia-MoS2/PMS system could still degrade pollutants efficiently and stably after five recycling cycles. Furthermore, this system had a strong anti-interference ability in the actual water body. This study provided a promising strategy for the removal of difficult-to-degrade organic pollutants.

Including the rare cubane cluster cobalt coordination polymer as the fluorescent sensing material for selectively and sensitively detecting the nitrofurantoin antibiotic

More and more attention has been paid to food safety. Due to the overuse and misuse of antibiotics, the problem of antibiotic residues in animal food is one of the important challenges to ensure food safety. The development of a feasible strategy to detect antibiotic residues in animal food has become desirable. In this paper, we creatively synthesize a water-stable fluorescence sensing material, namely, Co(Ⅱ)-Coordination polymer [Co2(CA) (L)0.5 (H2O)3] n (L = 1,4-bis(imidazole-1-ylmethyl) benzene, CA= Citric acid). The single crystal X-ray diffraction shows that it crystallizes in tetragonal space group I-4. It is worth mentioning that there exists the rare Co4(μ3-O)4 cubane cluster structure and Co8 cluster units. Those adjacent Co8 cluster units are connected into an infinite two-dimensional net structure by four flexible bridged L ligands. Finally, the Co(Ⅱ)-Coordination polymer (CP) further develops into the three-dimensional supramolecular structure via the hydrogen bonds of O–H⋯O and C–H⋯O. It could selectively detect the antibiotic-nitrofurantoin (NFT) residue by way of fluorescence quenching, Co-CP for the detection of NFT shows broad linearity from 0 to 200 μM, with a detection limit of 0.13 μM and strong anti-interference ability. It is used to detect the NFT residual of tap water and milk with a spiked recovery of 86.35–112.47 %.

A dual-reaction sites fluorescent probe for accurate detection of benzoyl peroxide in food

Benzoyl peroxide (BPO) is widely used as a whitening agent in flour, but excessive intake of BPO will severely endanger human health. To quickly and accurately detect BPO on-site, we have rationally designed a novel fluorescent probe PTPY-BE with dual-reaction sites. PTPY-BE underwent a specific cascade reaction with BPO to achieve high-contrast fluorescence turn-on response along with significant achromic reaction. The probe has high sensitivity, excellent selectivity, strong anti-interference ability and low detection limit (LOD = 0.83 mg·kg−1) for BPO. Furthermore, a portable detection platform was fabricated, which offers the portability and color visualization of traditional test strips and the color recognition of a smart device, enabling on-site visualization and quantitative detection of BPO. This platform has been successfully used to determine BPO in real food samples with good recoveries (93.59% - 107.13%). Therefore, this platform possessed great prospect and potential application for the determination of BPO in food.

Ferrate (VI) oxidation of sulfamethoxazole enhanced by magnetized sludge-based biochar: Active sites regulation and degradation mechanism analysis

Developing non radical systems for antibiotic degradation is crucial for addressing the inefficiency of conventional radical systems. In this study, novel magnetic-modified sludge biochar (MASBC) was synthesized to significantly enhance the oxidative degradation of sulfamethoxazole (SMX) by ferrate (Fe (VI)). In the Fe (VI)/MASBC system, 90.46% of SMX at a concentration of 10 μM and 49.34% of the total organic carbon (TOC) could be removed under optimal conditions of 100 μM of Fe (VI) and 0.40 g/L of MASBC within 10 min. Furthermore, the Fe (VI)/MASBC system was demonstrated with broad-spectrum removal capability towards sulfonamides in single or mixture. Quenching experiments, EPR analyses, and electrochemical experiments revealed that direct electron transfer (DET) and •O2− were mainly responsible for the removal of SMX, with functional groups (e.g., -OH, C=O) and Fe-O (redox of Fe (III)/Fe (II)) acting as the active sites, while the probe experiments showed that Fe (IV)/Fe (V) made a minor contribution to the degradation of SMX. Benefiting from the DET, the Fe (VI)/MASBC system exhibited a wide pH adaptation range (e.g., from 5.0 to 10.0) and strong anti-interference ability. The N atoms and their neighboring atoms in SMX were the prior degradation sites, with the cleavage of bond and ring opening. The degradation products showed low or non-toxicity according to ECOSAR program assessment. The removal of SMX remained within a reasonable range of 71.33%–90.46% over five consecutive cycles. Also, the Fe (VI)/MASBC system was demonstrated to be effectively applied for successful SMX removal in various water matrices, including ultrapure water, tap water, lake water, Yangtze River water, and wastewater. Therefore, this study offered new insights into the mechanism of Fe (VI) oxidation and would contribute to the efficient treatment of organic pollutants.

Unveiling the versatile performance of transition metal sulfides in peroxymonosulfate activation

Advanced oxidation processes (AOPs) are promising treatment options for environmental remediation to reduce chemical oxygen demand (COD) and emerging contaminants. Transition metal sulfides (TMSs) are a class of common catalysts in AOPs, but actually, they also widely exist in the environment in the form of sulfide minerals and seriously contaminated water bodies as black-odorous substances. Therefore, it is crucial to find out their roles during water decontamination using AOPs. This study investigated different mechanisms and performance of four common TMSs (FeS, CuS, CoS and MnS) in peroxymonosulfate (PMS)-based AOPs for the removal of a representative micropollutant tetracycline (TC). The results showed that CoS/PMS was the most efficient with 100 % TC removal within 1 min, followed by FeS/PMS (100 %, 10 min) with the apparent rate constant (kobs) of 0.45 min−1. TC removal by CuS (kobs = 0.10 min−1) and MnS (kobs = 0.02 min−1) took 30 min with 96 % and 82 % removal, respectively. Interestingly, the dominant reactive species were different in these systems, i.e., high-valent iron (Fe(IV)) in FeS/PMS, mainly radicals (•OH, SO4•−, O2•−) and singlet oxygen (1O2) in CuS/PMS, and Co(IV) and 1O2 in CoS/PMS. In comparison, TC was removed in MnS/PMS mainly through cooperative adsorption, reduction and oxidation by MnS. Low valent sulfur species, such as S2− and S22−, may participate in the valence cycles of metals or TC reduction. The above four systems had a wide pH (3.6–11.1) adaptability and a strong anti-interference ability to co-existing Cl−, SO42− and NO3−, but could be hindered by CO32−, PO43− and humic acid. This study provides novel insights into the TMS-based catalysts in AOPs, which will pave the way for advancing AOP technology.

Prepared Sandwich structure WS2/ag@MIP composite for ultrasensitive SERS detection of trace 17β-estradiol in food

17β-E2 is used in animal growth regulation and agricultural fertilizer, and even ng L−1 mass concentration levels can show biological effects. In this work, Ag NPs was used as surface-enhanced Raman spectroscopy (SERS) source and WS2 was synthesized by a simple method to provide a uniform distribution platform for Ag NPs. The MIP was the shell, which can selectively enrich the target molecule, pull the distance between the target molecule and SERS source, and protect Ag NPs. A cyclable SERS substrate with high sensitivity for detecting 17β-E2 in food was constructed. The optimized WS2/Ag@MIP as SERS substrate has the advantages of high Enhanced Factor (EF = 2.78 × 109), low detection limit (LOD = 0. 0958 pM), strong anti-interference ability, and good recycling performance. Moreover, the detection of 17β-E2 in real samples still has good accuracy. This work provides a new possibility for the trace detection of 17β-E2 in food.

Self-adaptive selection graph pooling based fault diagnosis method under few samples and noisy environment

Neural network (NN)-based methods are extensively used for intelligent fault diagnosis in industrial systems. Nevertheless, due to the limited availability of faulty samples and the presence of noise interference, most existing NN-based methods perform limited diagnosis performance. In response to these challenges, a self-adaptive selection graph pooling method is proposed. Firstly, graph encoders with sharing parameters are designed to extract local structure-feature information (SFI) of multiple sensor-wise sub-graphs. Then, the temporal continuity of the SFI is maintained through time-by-time concatenation, resulting in a global sensor graph and reducing the dependency on data volume from the perspective of adding prior knowledge. Subsequently, leveraging a self-adaptive node selection mechanism, the noise interference of redundant and noisy sensor-wise nodes in the graph is alleviated, allowing the networks to concentrate on the fault-attention nodes. Finally, the local max pooling and global mean pooling of the node-selection graph are incorporated in the readout module to get the multi-scale graph features, which serve as input to a multi-layer perceptron for fault diagnosis. Two experimental studies involving different mechanical and electrical systems demonstrate that the proposed method not only achieves superior diagnosis performance with limited data, but also maintains strong anti-interference ability in noisy environments. Additionally, it exhibits good interpretability through the proposed self-adaptive node selection mechanism and visualization methods.

Nonlinear adaptive robust control with continuous error reduction mapping

Abstract Achieving high stability and high-precision control by traditional control algorithms is difficult due to the strong nonlinearity, parameter uncertainty, and random disturbance characteristics of electro-hydraulic proportional servo systems. Herein, we developed a nonlinear adaptive control algorithm based on Lyapunov functions. By estimating the parameters and converging their errors, this algorithm solved the parameter adaptation problem. Additionally, based on convergence inference from the nonnegativity of the derivative of Lyapunov functions, the algorithm designed a disturbance mapping through the derived function. The random disturbance was reduced through the mapping, improving the stability of the system under random disturbance. Finally, we carried out continuous derivation and moved the mapping relationship out of the intermediate temporary controller. This reduced the complexity of the calculation during controller design, prevented performance degradation of the error convergence algorithm after complex calculation, and enhanced the robustness of the algorithm. The algorithm was verified through the simulation model established by Simulink and experience. Results showed that the nonlinear adaptive control algorithm demonstrates fast response, high accuracy, and strong anti-interference ability compared with the high-gain robust control algorithm. It realizes the adaptation of unknown parameters and reduces the control error introduced by random disturbance.

Photoelectrochemical sensor for hypochlorous acid detection based on the MWNTs doped PEDOT loaded with BP5 functionalized gold nanoparticles

Hypochlorous acid concentration anomaly may cause many serious diseases, so the development method to detect HClO high sensitivity and selectivity is of great significance in clinical diagnosis. Among many methods for detecting HClO, photoelectric chemical sensor has the advantages of high sensitivity and strong anti-interference ability, and has been widely used in the field of detecting HClO. Herein, a simple and specific photoelectrochemical sensor was constructed using A1/B1-type pillar[5]arene functionalized Au NPs and multi-walled carbon nanotube-polythiophene composites for the detection of hypochlorous acid. This approach was based on a sulfhydryl-borate ester modified A1/B1 type pillar[5]arene (BP5) as the main body, hypochlorous acid (HClO) as the guest, multi-walled carbon nanotube (MWNTs) conducting polymers doped with poly(3,4-ethylenedioxythiophene) (PEDOT) as the transport bridge and signal amplifier, as well as spherical gold nanoparticles act as the signal reporter. Specifically, the photoelectrochemical efficiency was further enhanced by the localized surface plasmon resonance effect (LSPR) of gold nanoparticles (Au NPs) and the host-guest complexation between BP5 and HClO. The large specific surface area of MWNTs doped with PEDOT significantly enhanced the electrical conductivity and chemical stability of PEDOT, facilitating accelerated electron transfer and mitigating the recombination of the photogenerated electron-hole pairs. Benefiting from the combination of Au NPs, BP5, MWNTs and PEDOT, the sensor exhibited excellent sensitivity with detection range of 0.5–340 μM and a detection limit of 0.17 μM, and distinguished selectivity against 6 interfering substances. Due to the adsorption and removal effects of pillar[5]arenes on heavy metal ions and pollutants in water, as well as their effective catalytic performance, we anticipate that this composite material also has great potential in photoelectrochemical detection, catalysis, and adsorption.

Dynamic response and stability analysis of vehicle systems with double time-delay feedback control

In this paper, the vehicle suspension dynamic response of time delay feedback control is analyzed considering the suspension of the seat. An active suspension vibration reduction control method based on double time-delay feedback control is proposed to further improve the ride comfort. Firstly, the double time-delay dynamic response equation of the system with three degrees of freedom is established. Then, the root mean square values of seat acceleration and body acceleration are used as the objective functions. Meanwhile, the RMS values of suspension dynamic deflection and tire dynamic displacement are used as constraints, and the optimal feedback parameters are obtained using particle swarm optimisation. The Routh-Hurwitz criterion and frequency domain scanning methods are used to verify the stability of the double time-delay system. Finally, simulation in the time domain and frequency domain is accomplished. It is verified that this feedback control method has strong robustness and anti-interference ability.

Gap measurement method based on projection lines and convex analysis of 3D points cloud

Abstract Accurate measurement of the gap between the lower surface of the relay and the ground is critical for ensuring the quality of the finished product. Traditional gap measurement methods have some shortcomings, such as low accuracy, poor robustness, and loss of depth clues in obscured areas. In this study, a novel gap measurement method based on computer vision is proposed, which includes a projection line model based on guided filtering and a 3D surface point cloud model based on a three dimensional plane reference.- The relay gap was measured by calculating the projection lines of the upper and lower surfaces of the gap with an error of ±0.016 mm. A 3D point cloud model captures the key features of the underside of the relay through image processing techniques, and combines convex hull and centroid estimation to construct a three-dimensional reference plane for the gap, which could achieve high-precision, real-time measurement of the gap (with an error less than ±0.0087 mm). The experimental measurement results show that the proposed method is better than the SelfConvNet method, which has a high measurement accuracy and strong anti-interference ability, and an accuracy rate of up to 99.5% in factory relay quality inspection experiments.

Low-coordinated Mn–N2 sites in graphene oxide induce peroxydisulfate activation for tetracycline degradation: Process optimization and theoretical calculation

Atom-dispersed low-coordinated transition metal-Nx catalysts exhibit excellent efficiency in activating peroxydisulfate (PDS) for environmental remediation. However, their catalytic performance is limited due to metal-N coordination number and single-atom loading amount. In this study, low-coordinated nitrogen-doped graphene oxide (GO) confined single-atom Mn catalyst (Mn-SA/NGO) was synthesized by molten salt-assisted pyrolysis and coupled to PDS for degradation of tetracycline (TC) in water. Aberration-corrected high-angle annular dark-field scanning transmission electron microscopy (AC-HAADF-STEM) and X-ray absorption fine structure spectroscopy (XAFS) analysis showed the successful doping of single-atom Mn (weight percentage 1.6%) onto GO and the formation of low-coordinated Mn–N2 sites. The optimized parameters obtained by Box-Behnken Design achieved 100% TC removal in both prediction and experimental results. The Mn-SA/NGO + PDS system had strong anti-interference ability for TC removal in the presence of anions. Besides, Mn-SA/NGO possessed good reusability and stability. O2•−, •OH, and 1O2 were the main active species for TC degradation, and the TC mineralization reached 85.1%. Density functional theory (DFT) calculations confirmed that the introduction of single atoms Mn could effectively enhance adsorption and activation of PDS. The findings provide a reference for the synthesis of high-performance single-atom catalysts for effective removal of antibiotics.

An Analysis of Traditional Methods and Deep Learning Methods in SSVEP-Based BCI: A Survey

The brain–computer interface (BCI) is a direct communication channel between humans and machines that relies on the central nervous system. Neuroelectric signals are collected by placing electrodes, and after feature sampling and classification, they are converted into control signals to control external mechanical devices. BCIs based on steady-state visual evoked potential (SSVEP) have the advantages of high classification accuracy, fast information conduction rate, and relatively strong anti-interference ability, so they have been widely noticed and discussed. From k-nearest neighbor (KNN), multilayer perceptron (MLP), and support vector machine (SVM) classification algorithms to the current deep learning classification algorithms based on neural networks, a wide variety of discussions and analyses have been conducted by numerous researchers. This article summarizes more than 60 SSVEP- and BCI-related articles published between 2015 and 2023, and provides an in-depth research and analysis of SSVEP-BCI. The survey in this article can save a lot of time for scholars in understanding the progress of SSVEP-BCI research and deep learning, and it is an important guide for designing and selecting SSVEP-BCI classification algorithms.

Fluorescence Recognition of Hydrazine Driven by Neighboring Group Participation.

Hydrazine (N2H4) has toxic effects on the environment. Although a variety of reactive probes have been used to identify hydrazine, practical applications required continuous development of hydrazine fluorescent probes with improved performance. Here, we applied the neighboring group participation (NGP) to the design of a fluorescent probe for hydrazine. The probe exhibited a rapid response to N2H4 and strong anti-interference ability, with detection limited to 0.031μmol/L. Theoretical calculation showed that the energy barrier could be reduced by NGP. The cyclic intermediate formed by the indole ring and the α-ester carbonyl group significantly reduced the activation energy of the reaction. Practically, the probe could detect hydrazine in actual water samples.