Anti-interference Capability Research Articles

Salivary glucose detection based on platinum metal hydrogel prepared mouthguard electrochemical sensor

A mouthguard electrochemical sensor for salivary glucose detection based on platinum metal hydrogel is proposed in this work. Conventional enzyme-based electrochemical glucose sensors are fraught with issues such as high cost, oxygen dependency, intricate immobilization procedures, and susceptibility to variations in temperature, pH, and so on. The detection of glucose in saliva, as a non-invasive sensing approach, presents a more convenient solution for diabetes monitoring. This study employs Pt metal hydrogel as the electrocatalytic material for glucose, with its microstructure characterized using scanning electron microscopy (SEM) and X-ray diffraction (XRD) techniques. The sensor's electrochemical properties, sensing performance, anti-interference capability, and stability were assessed through methods including cyclic voltammetry (CV), chronoamperometry, and electrochemical impedance spectroscopy (EIS). Under neutral pH phosphate buffer (PB) solution in the laboratory setting, the sensor demonstrated an outstanding linear range (0–40 mM) and a low detection limit (0.119 mM). Implemented in a wearable mouthguard format, this electrochemical sensor enables the detection of glucose in physiological environments, specifically saliva, exhibiting favorable detection characteristics: a linear range of 0.58–3.08 mM and a detection limit of 0.082 mM. This innovation thus offers a practical and efficacious tool for the non-invasive monitoring of glucose levels relevant to diabetes management.

Method of bed exit intention based on the internal pressure features in array air spring mattress

With the population ages, many patients are unable to receive comprehensive care, leading to an increase in hazardous incidents, particularly falls occurring after getting out of bed. To address this issue, this paper proposes a method for recognizing bed-exit intentions using an array air spring mattress. The method integrates convolutional neural networks with feature point matching techniques to identify both global and local features of the array air spring. For global features, a one-dimensional focal loss convolutional neural network (1D-FLCNN) model is employed to classify eight internal pressure time series and determine bed-exit status based on global features. For local features, the distribution matrix and feature point matrix of the internal pressure features are extracted to represent the spatial distribution of bed-exit postures. Euclidean distance is utilized to measure the similarity between these matrices and match bed-exit postures. Finally, the recognition results from both feature types are combined using a logical OR operation to produce the final result. Experimental validation confirms that the proposed method greatly improves the anti-interference capability and effectively avoids the problem of non-recognition due to body position and external environment.

Lighting up imidazole-based fluorescent chemosensor for selective discrimination of Hg2+ ions and its application in sequential detection of histidine, solid-supported extractant

A novel turn-on fluorogenic probe based on 4-(bis(2-((2-hydroxyethyl)thio)ethyl) amino)benzaldehyde the detection of Hg2+ ions has been designed and developed. The probe BMA showed high selectivity for Hg2+ ions over the series of other tested cations in an aqueous acetonitrile medium. Only the addition of Hg2+ ions to probe BMA in aqueous acetonitrile solution (CH3CN/H2O (7:3, v/v) could induce a remarkable red shift of about 20 nm in UV–vis absorbance spectra and a dramatic fluorescent enhancement with a blue shift of 10 nm in the emission spectra. With the help of a fluorescence spectrometer, the detection limit was calculated to be 73 nM. The binding stoichiometric ratio between BMA and Hg2+ ions was found to be 1:1 by Jobs plot, which was further confirmed by 1H NMR titration, FT-IR, HR-MS, and density functional theory calculations. The probe BMA can selectively detect Hg2+ ions, with several advantages, including ultra-fast response, low detection limit, conspicuous fluorescent changes, robust anti-interference capability, and exciting reversibility. The probe BMA can also serve as a portable extractant for removing Hg2+ ions in aqueous solution and act as an efficient and practical solid-state fluorescent sensor for mercury in on-field measurements. In addition, the in-situ formed BMA-Hg2+ ensemble may be used for fluorescent recognition of histidine over other common amino acids via an indicator displacement assay mechanism. The low detection limits of BMA-Hg2+ for histidine were calculated to be 0.91 µM.

Closed-form BER analysis of FBMC-OQAM system over long-distance horizontal underwater acoustic transmission in shallow water

The filter bank multi-carrier with offset quadrature amplitude modulation (FBMC-OQAM) waveform demonstrates strong anti-interference capabilities due to its sub-carrier-level filtering. As a result, it is increasingly being adopted for underwater acoustic communication (UWAC) in challenging transmission environments. This paper presents a closed-form analytical expression for the bit error rate (BER) performance of FBMC-OQAM in shallow water, long-distance horizontal communication scenarios. The expression considers the residual Doppler frequency offset, multipath propagation, and ambient noise characteristic of underwater acoustic channels, accounting for the limitations of traditional non-uniform Doppler coarse compensation methods. Theoretical numerical results and Monte Carlo simulation results show that under any number of symbols, the derived closed-form expression consistently exhibits high computational accuracy, when facing the impact of Doppler spread or compression at any scale.

High sensitive and discriminating colorimetric assay for S2− overload in adaptogenic herbs utilizing ZnS nanoparticle-enhanced S, N-doped eggshell membrane-derived biochar

BackgroundWith the rapid development of industrialization, the excessive emission of S2− have become increasingly serious, leading to a surge in the content of S2− in nature. Rapid and accurate detection of S2− contamination in natural adaptogens is crucial for food safety. Annually, discarded eggshell waste, rich in organic and inorganic materials, poses environmental risks if landfilled. Utilizing waste eggshell membrane biomass for S2− detection is cost-effective, yet designing biochar materials for sensors requires balancing catalytic enhancement and anti-interference capabilities. Improving the catalytic performance of biochar for colorimetric S2− detection without metal ion interference presents a challenging issue. ResultsWe first modified biochar (EBc) derived from waste eggshell membranes using a combination of thiourea and ZnS nanoparticles, fabricating ZnS-decorated, S–N co-doped biochar (ZnS-SN-EBc) nanozymes, which were applied for the colorimetric assay detection of S2− contamination. The addition of thiourea significantly increases the proportion of pyridinic-N in biochar, enhancing the peroxidase-like activity of the nanozyme. The growth of ZnS nanoparticles on the biochar not only enhances the catalytic performance by increasing the S content but also reduces the content of oxidized S, thereby improving resistance to interference. The detection range for S2− was expanded from 0.1 to 45 μM for EBc to 0.05–225 μM for ZnS-SN-EBc, and the limit of detection improved to 0.0397 μM. Additionally, ZnS-SN-EBc significantly enhanced metal ion interference resistance. S2− detection in five types of adaptogenic herbs verified the accuracy and practicality of the colorimetric assay, with recovery rates comparable to national standards. SignificanceWe innovatively repurposed waste eggshell membranes to develop a selective and catalytic peroxidase-like nanozyme, ZnS-decorated S–N co-doped biochar (ZnS-SN-EBc). The developed colorimetric assay utilizing ZnS-SN-EBc demonstrates significant potential for the detection of sulfur ions in adaptogenic herbs, thus contributing to both waste resource utilization and the advancement of food safety detection technologies.

An adaptive online monitoring system using NFSOGI-PLL for three-phase voltage unbalance in grids

The extensive utilization of unbalanced and nonlinear loads has significantly impacted power quality in modern grids. Among various metrics, the three-phase voltage unbalance factor (TPVUF) stands out as a crucial indicator. To mitigate the challenges posed by harmonics and DC components that interfere with accurate TPVUF measurements, this paper introduces the Notch Filter-based Second-Order Generalized Integrator Phase-Locked Loop (NFSOGI-PLL) for the first time and designs an adaptive online monitoring system for TPVUF based on NFSOGI-PLL. To validate its performance, the hardware design and rapid software development of the NFSOGI-PLL-based TPVUF online monitoring system are thoroughly implemented, and the system is tested under various conditions. The results indicate that the proposed system not only provides real-time performance and high accuracy, complying with the rigorous standards set by GB/T 15543-2008 and IEC 61000-4-27, but also demonstrates remarkable stability under diverse influences. These findings underscore the exceptional filtering and tracking abilities of the NFSOGI-PLL in power systems, as well as the accuracy and anti-interference capabilities of the NFSOGI-based TPVUF online monitoring system, which holds great potential for widespread application in power system control and research.

Cadmium-Assisted Photochemical Vapor Generation of Arsenic from Low Concentration of Formic Acid Media.

A highly sensitive method for the determination of total arsenic (As) has been developed using photochemical vapor generation (PVG) coupled with inductively coupled plasma mass spectrometry (ICP MS). The efficient PVG of As is reported for the first time in the presence of Cd(II) in diluted formic acid (FA) medium. The PVG efficiency of 93 ± 1% can be achieved in the system with 0.15% (v/v) FA and 40.0 mg L-1 Cd(II) under 100 s UV irradiation. The limit of detection (LOD, 3σ) for As was determined to be 0.2 ng L-1, which was enhanced about 43-fold over the traditional solution nebulization method. The anti-interference capability against sample matrices was enhanced compared to previous PVG systems that used diluted formic acid alone or a combination of sodium formate and sodium sulfite as the medium. The established method was applied for the analysis of natural waters and certified reference materials (CRMs) of rice with satisfactory results. The mechanism of the PVG system was investigated, and the generation of volatile As species as AsH3 was found in all investigated systems, including monomethylarsonic acid (MMA), dimethylarsinic acid (DMA), As (III), and As(V). Additionally, volatile cadmium species were also generated simultaneously. The efficient reduction of As in diluted FA observed in this study is beneficial to understanding the interaction between As and Cd in the photochemical process.

Dynamic Surface Intelligent Robust Control of Nonlinear Systems With Fixed-Time Sliding-Mode Observer.

The high tracking control precision and fast finite-time convergence for nonlinear systems is a significant challenge due to complex nonlinearity and unknown disturbances. To address this challenge, a dynamic surface intelligent robust control strategy with fixed-time sliding-mode observer (DSIRC-SMO) is proposed to improve the tracking control performance in a finite time. First, adaptive fuzzy neural network based on a predictor (P-AFNN) is designed to imitate the complex nonlinearity. In particular, the weight adaptive law is formulated by utilizing the prediction error information, which improves the accuracy of approximating the nonlinear system. Second, the fixed-time sliding-mode observer (SMO) is integrated into the dynamic surface control technique to deal with unknown disturbances and modeling errors in a fixed time. This integration allows for timely updates the dynamic surface using observation information, thereby enhancing the system's anti-interference capability. Then, the fixed-time convergence of SMO is proven. Third, the proposed DSIRC-SMO can be effectively implemented and the finite-time convergence of DSIRC-SMO is proven in detail based on the fixed-time convergence of SMO. Finally, numerical simulation and actual wastewater treatment processes simulation are conducted to validate the effectiveness of DSIRC-SMO.

Simultaneous Determination of Phenolic Pollutants based on Electrochemical Oxidation by Nanoporous Gold

Phenolic compounds, as highly toxic pollutants, can cause great harm to human beings and the environment even at very low concentrations. Therefore, the simultaneous detection of multiple phenolic pollutants is critically valuable for environmental monitoring. Here, an electrochemical sensor based on a glassy carbon electrode (GCE) modified by nanoporous gold (NPG) was successfully developed for the determination of phenolic pollutants including phenol (Ph), hydroquinone (HQ), catechol (CT), and o-nitrophenol (ONP), which realized not only the sensitive individual detection of each phenolic pollutant but also the sensitive simultaneous detection of these four phenolic pollutants. For the simultaneous detection, the limits of detection of Ph, HQ, CT, and ONP were 0.85, 0.17, 0.19, and 1.30 μM as well as the sensitivities of 0.24, 1.17, 1.08, and 0.16 μA μM−1 cm−2, respectively. Additionally, the NPG/GCE electrochemical sensor exhibited good stability and anti-interference capability. The recovery rates of Ph, HQ, CT, and ONP in seawater samples and wastewater samples ranged from 94.64% to 105.87%. These results indicated that the prepared NPG/GCE electrochemical sensor may be an ideal choice for the reliable simultaneous detection of multiple phenolic pollutants.

Signal Fusion of Multi-Frequency Continuous Wave Ground Penetrating Radar

Abstract Carrier-less pulse radar, with its short pulse duration, low average power, and weak anti-interference capability, poses significant limitations to the application of ground penetrating radar (GPR). Fusing continuous wave signals from multiple frequency components forms the basis for the design and data interpretation of continuous wave radar. We firstly introduce two fusion methods: the application of the Inverse Fast Fourier Transform (IFFT) to multi-frequency signals and direct superposition. And we demonstrate these methods by synthesizing three basic sinusoidal signals and analyse their time-frequency relationship. Furthermore, we perform Fast Fourier Transform (FFT) on the transmit waveform of pulse radar to obtain a set of frequency components. By superimposing three sets of frequency components with different numbers of frequency points, we obtain the fused waveform and compare it with the original Ricker wavelet. The results show that while reducing the number of frequency points may increase the error in the synthesized signal slightly, it remains capable of accurately synthesizing the Ricker wavelet. However, this reduction also decreases the time window of the synthesized signal and may result in temporal overlapping. These findings offer insights into selecting parameters for frequency components in multi-frequency continuous wave radar and lay a foundation for signal fusion in such radar systems.

High-performance biochar derived from the leaves of Quercus dentata thunb for triclosan removal

BackgroundTriclosan (TCS) is a common antimicrobial additive. Despite its low concentration, its frequent use and widespread application result in significant amounts of TCS entering the environment. In the environment, TCS undergoes photodegradation, producing harmful byproducts such as dioxin-like substances and chloroform, which are highly toxic. Due to TCS's lipophilic nature, it easily accumulates in animal tissues, ultimately posing a threat to human health. Therefore, there is an urgent need for a powerful and efficient method to remove TCS from water bodies. MethodsUsing discarded traditional Chinese medicine as raw material and KOH as an activator, biochar was prepared. The material was characterized, and its adsorption capacity for TCS under various conditions was evaluated. Significant FindingsCompared to the existing biochar materials' TCS adsorption capacity (13.9 mg/g-395.2 mg g-1), the KOH-modified biochar (KMBC) in this study demonstrated an exceptional adsorption capacity of 639.13 mg g-1 for TCS. KMBC can swiftly remove 98 % of TCS from water within 30 min, even with a minimal biochar concentration of 0.225 g/L, and it exhibits strong anti-interference capabilities. Most importantly, water treated with KMBC is virtually free of TCS, effectively mitigating its impact on environmental organisms.

Convenient electrochemiluminescence biosensor for acute myeloid leukemia biomarker based on hyperbranched rolling circle amplification and vertically-ordered mesoporous silica film modified electrode

Vertically-ordered mesoporous silica film (VMSF), with large amount of small pores and abundant negative charges, had been modified on the surface of indium tin oxide electrodes and applied to develop an electrochemiluminescence (ECL) biosensor for acute myeloid leukemia biomarker hsa-miR-10a-5p (chosen as a model target). The small ECL indicator (Ru(phen)32+) can be enriched in the pores through electrostatic adsorption and then diffuse to the electrode surface to produce strong ECL signals. The presence of target can trigger hyperbranched rolling circle amplification (HRCA) and produce a large amount of double-stranded DNA (dsDNA). Ru(phen)32+ can embed into the groove structure of dsDNA and forms negatively charged large-sized complexes, which cannot pass through the narrow pores of VMSF to reach the electrode. This led to a significant decrease of ECL signal of the system. The change of the ECL intensity of the system had a linear relationship with logarithm of target concentration within the range of 1 fM–1 nM. The limit of detection was 1.23 fM. The developed biosensor had been applied to determine the target substance in serum samples with satisfied results without complicated sample pretreatment. The introduction of VMSF endows the biosensor with superior anti-interference capability and simpler operational steps, promising applications in the diagnosis of acute myeloid leukemia and potential extension to the analysis of other target molecules through primer design.

Research and Design of BPM Shortwave Time Signal Modulation Technology Based on Chirp

The shortwave time service system is a vital land-based wireless time service solution, serving as a supplement and backup to the global navigation satellite system. It ensures that time users can access reliable timings, especially in extreme situations. However, the current BPM shortwave time service signal in China faces issues such as insufficient anti-interference reception capabilities and poor timing accuracy. This paper capitalizes on the advantages of Chirp signals and explores a new modulation technology for BPM shortwave time signals that is compatible with the existing modulation system. A Dual Chirp Time-Division Combined Modulation (DCTDCM) scheme is proposed for broadcasting two time signals: Coordinated Universal Time (UTC) and Universal Time 1 (UT1). Furthermore, an in-depth study of the receiving method for this scheme is conducted, with detailed design of its parameters. The designed DCTDCM signals offer a spread spectrum gain of 24 dB and a multipath resolution capability of at least 125 μs, significantly enhancing the anti-interference reception and anti-multipath attenuation capabilities of shortwave time signals. As a result, the availability and timing accuracy of shortwave time signals are substantially improved. Finally, extensive comparative experiments on reception performance validate the effectiveness of this approach.

Research on obstacle avoidance of underactuated autonomous underwater vehicle based on offline reinforcement learning

Abstract The autonomous navigation and obstacle avoidance capabilities of autonomous underwater vehicles (AUVs) are essential for ensuring their safe navigation and long-term, efficient operation. However, the complexity of the marine environment poses significant challenges to safe and effective obstacle avoidance. To address this issue, this study proposes an AUV obstacle avoidance control algorithm based on offline reinforcement learning. This method adopts the Conservative Q-learning (CQL) algorithm, which is based on the Soft Actor-Critic (SAC) framework. It learns from obtained historical obstacle avoidance data and ultimately achieves a favorable obstacle avoidance control strategy. In this method, PID and SAC control algorithms are utilized to generate expert obstacle avoidance data to construct a diversified offline database. Additionally, based on the line-of-sight (LOS) guidance method and artificial potential field (APF) method, information regarding the distance and orientation of targets and obstacles is incorporated into the state space, and heading and obstacle avoidance reward terms are integrated into the reward function design. The algorithm successfully guides the AUV in autonomous navigation and dynamic obstacle avoidance in three-dimensional space. Furthermore, the algorithm exhibits a certain degree of anti-interference capability against uncertain disturbances and ocean currents, enhancing the safety and robustness of the AUV system. Simulation results fully demonstrate the feasibility and effectiveness of the intelligent obstacle avoidance method based on offline reinforcement learning. This study highlights the profound significance of offline reinforcement learning in enabling robust and reliable control systems for AUVs, paving the way for enhanced operational capabilities in challenging marine environments.

Acriflavine-modified UIO-66 ratiometric fluorescent sensor for highly selective and fast detection of hypochlorite in water

Hypochlorite (ClO−) as a kind of highly toxic pollutant has garnered significant interest in detection methods, highlighting the pressing need to develop intelligent functional materials for the qualitative and quantitative analysis of ClO− in aqueous solutions. Herein, a ratiometric fluorescent sensor was prepared by the combination of acriflavine (Acr) and UIO-66 via a post-synthetic modification strategy. Acr/UIO-66 exhibited both high crystallinity typical of metal–organic frameworks and demonstrated good fluorescent and thermal stability. Additionally, Acr/UIO-66 functioned effectively as a dual-responsive fluorescent platform for detecting ClO− in domestic drinking and surface water samples. This material displayed high sensitivity, exceptional selectivity, and superior anti-interference capabilities, along with fast respond time (60 s), a wide pH range (4.0–7.0), high recoveries (94.46–118.00 %), a broad linear range (0–28 µmol L−1) and low detection limits (0.74 µmol L−1). This study broadened the potential applications of fluorescent metal–organic frameworks and presented a feasible solution for water quality monitoring.

Exploring the Drive Motor of Electric Vehicles: Structure, Temperature Rises, and Operational Control of Permanent Magnet Motors

The drive motor is a critical component of electric vehicles. The design of its structure, along with the regulation of temperature rises and operational control, significantly impact the overall performance of electric vehicles, affecting aspects such as dynamic response, anti-interference capabilities, speed stability, and efficiency. This paper provides a review and comprehensive analysis of the Special Issue titled “Temperature Field, Electromagnetic Field, and Operation Control of Permanent Magnet Motor for Electric Vehicles”, and discusses the future development directions of permanent magnet motors in electric vehicles. The analysis reveals that the structural design is crucial for both the static and dynamic performance of permanent magnet motors. Additionally, it is imperative to identify an optimal trade-off among various performance parameters in these motors. The paper also verifies several future development directions for permanent magnet motors, including high-speed control, the use of advanced silicon steel sheets, power quality, battery charging efficiency, and energy feedback.

Electrochemical sensor for dopamine detection based on multiwalled carbon nanotube/molybdenum disulfide quantum dots with machine learning integration and anti-interference capability

Electrochemical sensor for dopamine detection based on multiwalled carbon nanotube/molybdenum disulfide quantum dots with machine learning integration and anti-interference capability

An efficient state-of-health estimation method for lithium-ion batteries based on feature-importance ranking strategy and hybrid kernel extreme learning machine algorithm

The safe and reliable operation of battery systems depends critically on accurately and dependably estimating the state of health (SOH) of lithium-ion batteries (LIBs). However, accurate prediction of SOH still needs improvement due to the complex battery aging mechanism. This paper introduces a hybrid kernel extremum learning machine (HKELM) for estimating battery SOH. To improve estimation accuracy, we enhance the standard dung beetle optimization algorithm (DBO) with a new initialized population, adaptive weights method, and improved population diversification. We then use the enhanced IDBO algorithm to optimize the parameters of HKELM. This study examines two distinct anode types of LIBs from NASA and Oxford datasets. Fifty significant features are extracted from charging voltage, current, temperature, and incremental capacity (IC) curves. The importance indices of these features are then computed using statistical analyses, including Pearson's correlation coefficient and Spearman's rank correlation coefficient, followed by optimal ranking. The optimal number of final features is determined by comparing various combinations of high-level features, thus reducing model complexity and improving estimation accuracy. This paper proposes the IDBO-HKELM algorithm for SOH estimation. Compared to other methods, the algorithm shows superior estimation performance and anti-interference capability. Both B0007 and Cell8 estimate SOH with MAE < 0.15, RMSE <0.2, and R2 > 99.9 %. Experimental results demonstrate the robustness of the proposed method, achieving accurate and noise-immune SOH estimation.

Stress Evaluation of Ferromagnetic Materials Based on a New Barkhausen Noise Sensor Composed by High Entropy Alloy Magnetic Core

Abstract During the service of ferromagnetic structural steel materials, stress should be evaluated accurately. Although the magnetic Barkhausen noise (MBN) testing has the ability to sense stress, it can be easily interfered with by environment. In this paper, a new MBN sensor is fabricated by selecting FeCoNi(AlMn)0.25 high entropy alloy (HEA) as the case of magnetic core to improve the accuracy of stress evaluation. The process optimization results show that the stability of MBN signal characteristics is the largest when the excitation frequency is 4 Hz and the voltage is 6 V. The signal-to-noise ratio of MBN indicates that the HEA and Ni-Zn ferrite probes have better anti-interference capability. The MBN signal characteristic values peak voltage and root mean square measured by the HEA probe can linearly quantify the stress level with higher efficiency, stability, and accuracy. The underlying reason of high sensitivity of HEA probe to the variation of MBN signals is revealed based on the magnetic properties. The microstructure and the thermodynamic parameters are analyzed to clarify whether the additions of Al and Mn atoms can affect the short-ranged magnetic exchange interaction and lattice distortion, which affects the magnetization behavior of HEA. Finally, the availability of MBN sensor with HEA magnetic core to the stress evaluation on the retired slide rails of car seats is conducted, which demonstrates its great application value.

Aluminum dust concentration detection based on LSTM-Kalman filter

Industrial dust emissions present serious hazards, including respiratory issues and explosion risks. Traditional dust concentration detection methods are often compromised by environmental factors. This study introduces a novel FFT-KF-LSTM-NET model to predict high-concentration aluminum powder levels, utilizing 128 sets of electrostatic induction measurement data. By applying the Fast Fourier Transform (FFT) to eliminate low-frequency interference, integrating Long Short-Term Memory (LSTM) networks for advanced time series analysis, and using the Kalman Filter (KF) for rapid model convergence, this approach significantly enhances prediction accuracy. The model achieves an 82% improvement in Mean Squared Error (MSE), reducing it to 0.1336, outperforming traditional methods. Furthermore, by modeling the voltage signal generated by charged dust particles in the electrostatic induction sensor's sensing area and using the first derivative of the voltage signal as a learning feature, the model's prediction speed is increased from 1.5 s-2 s–0.5 s, with improved anti-interference capabilities. These advancements position the FFT-KF-LSTM-NET model as a crucial tool for real-time, reliable dust concentration detection in industrial environments.