Square ion mobility spectrometers (IMSs) are structurally compatible with printed circuit board (PCB) and micro-electro-mechanical system (MEMS) fabrication and are therefore promising for portable detection devices. However, because geometric discontinuities are unavoidable at the corners of square structures, local field distortion can easily arise under discrete voltage-dropping conditions, thereby degrading the ideal drift environment. Previous numerical studies have shown that discrete electrodes and insulating gaps inevitably introduce local edge-field perturbations that reduce resolving power. To address this issue, a three-dimensional COMSOL model coupling electrostatics and charged-particle tracing was established, and a continuous-potential-boundary method was proposed to represent the high-impedance voltage-dropping structure on the sidewalls of the drift region. By replacing the conventional discrete boundary with a continuous one, periodic potential perturbation is suppressed and a stable axial field can be maintained over a 100 mm drift region. Combined with a field-trajectory decoupling strategy, separation simulations were performed for macromolecular ions with different effective mobilities. The peak-center arrival times were 0.043 s and 0.058 s, and the full widths at half maximum were 0.00353 s and 0.0047 s, yielding a resolution of 2.15 and demonstrating baseline separation capability under the present simulation conditions. The results show this treatment provides a structural and numerical optimization method for square IMS design, mitigating field distortions without altering fundamental resolving limitations.

Accurate and rapid ammonia detection is critical for environmental monitoring and clinical diagnosis of chronic kidney disease. Herein, a highly sensitive room-temperature ionic liquid (RTIL) in electrochemical ammonia sensor based on a Pt-Ru electrode was developed for detecting ammonia concentrations ranging from 0 to 100 ppm. To achieve prolonged operational life and stability, 1-(2-hydroxyethyl)-3-methylimidazolium tetrafluoroborate ([HOETMIM][BF4]) was utilized as the electrolyte. Nickel cobaltite (NiCo2O4) and gamma-MnO2 were synthesized separately via hydrothermal methods. The effects of modified ionic liquids on the sensor's performance were investigated by incorporating different mass fractions of NiCo2O4 and gamma-MnO2. Comprehensive analyses, including X-ray diffraction (XRD), scanning electron microscopy (SEM), Mapping, energy-dispersive spectroscopy (EDS), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS), confirmed the successful and rational synthesis of the samples. The ammonia sensing performance of the ILs/NiCo2O4/gamma-MnO2 sensor was studied at room temperature (25 degrees C) using constant potential polarization (it) and electrochemical impedance spectroscopy (EIS) techniques. The testing data demonstrated that the optimized ILs/NiCo2O4/gamma-MnO2 sensor(5 wt% composite, 1:1 mass ratio) exhibited good sensitivity of 0.10 +/- 0.02 mu A/ppm, a limit of detection(LOD) of 1 +/- 0.2 ppm, and a limit of quantification(LOQ) of 3 +/- 0.5 ppm. The response and recovery times show clear difference among different sysrems: pure IL system exhibited 510 s/530 s, 5 wt% composite modified system achieved 12.22 s/3.6 s, and the fastest recorded values in batch tests were 17 s/14.5 s. Compared to traditional ionic liquid sensors, the ILs/NiCo2O4/gamma-MnO2 sensor demonstrated a significant improvement in ammonia sensing performance, with a selectivity ratio > 10 against common interfering gases.

Infrared Optical Gas Imaging (OGI) is a critical technology for monitoring industrial gas leaks. However, automated detection remains challenging due to the low signal-to-noise ratio of uncooled sensors and the amorphous nature of gas plumes. To address these issues, this paper proposes IGSG-DETR, a real-time detection framework based on the RT-DETR architecture. We introduce three key innovations: a Multi-Scale Spatial-Frequency Module (MS-SDFM) that amplifies weak plume signals by decomposing frequency bands; an Enhanced Context-Aware Fusion Block (E-CAFB) that refines blurred boundaries through cross-scale feature interaction; and an Adaptive Balanced Head (ABH) that stabilizes the learning of classification and localization tasks. Experimental results on a custom sulfur hexafluoride (SF6) dataset demonstrate that IGSG-DETR achieves a mean Average Precision (mAP@0.5) of 58.1% at an inference speed of 15 FPS. These metrics significantly outperform state-of-the-art baselines, proving the framework’s potential for reliable, automated industrial safety monitoring.

Traditional metal oxide semiconductor (MOS) methane sensors rely on high-temperature thermal excitation to drive reactions, leading to safety hazards and poor device stability. Replacing thermal excitation with ultraviolet (UV) light excitation has been proposed as an effective strategy to mitigate these issues. However, current research on UV light excitation primarily focuses on the UVA–UVC bands, with limited studies on higher-energy vacuum ultraviolet (VUV) excitation. To address this research gap, this study innovatively employs 116.5 nm VUV light as the excitation source and constructs a room-temperature methane sensor using a Pt–ZnSnO₃–rGO (reduced graphene oxide) ternary composite material as the sensing layer. The performance of the resulting sensor was systematically evaluated, along with an in-depth elucidation of its sensing mechanism. The experimental results indicate that the fabricated sensor exhibits excellent performance at room temperature, with a response value of 58.1

This study proposes a semi-open low-temperature plasma ambient ionization source coupled with ion mobility spectrometry for in situ analysis of multiphase samples. For the first time, a bidirectional plasma jet configuration is introduced into the IMS system, enabling the dielectric barrier discharge device to simultaneously generate an ionization jet and a transport jet. The ionization jet efficiently ionizes external samples, while the transport jet stably delivers the ionized products into the reaction region of the ion mobility spectrometer. The coordinated action of the two jets creates a plasma-rich ionization and transfer environment between the sample surface and the IMS inlet, effectively suppressing ion neutralization and ion loss, thereby enhancing signal intensity and detection sensitivity. To further optimize ion transmission, the semi-open ionization source is vertically coupled to the reaction region of the ion mobility spectrometer, which helps reduce direct intrusion of neutral gas into the drift region and improves signal stability. The system enables in situ detection without complex pretreatment, reducing background interference and improving the signal-to-noise ratio. Experiments demonstrate rapid detection of analytes including triethylamine, dimethylformamide, acetaminophen, caffeine, acetone, and ethanol, with clear separation between the reactant ion peaks and the analyte peaks. These results confirm the feasibility and application potential of the semi-open bidirectional low-temperature plasma dielectric barrier discharge ion mobility spectrometry system for rapid in situ detection of multiphase samples, providing a promising approach for environmental monitoring, public safety, food safety, and pharmaceutical analysis.

To address the core requirements of portable low-field Nuclear Magnetic Resonance (NMR) spectrometers for magnetic field strength and long-term stability, this study developed a complete measurement platform integrating a nested Halbach magnet array and a high-precision temperature control system. Firstly, through parametric simulation optimization, a double-layer nested Halbach magnet with a height of 150 mm was designed, comprising an outer array of 4 NdFeB magnets (Mandhalas-shaped cross-sections) and an inner array of 4 SmCo magnets (trapezoidal cross-sections). Simulations indicated a central field strength of approximately 1.4 T and a homogeneity of about 360 ppm within a 5 mm diameter spherical volume (DSV). Experiments showed that under stable temperature control at 21.00°C, the measured central magnetic field reached 1.17765 T. Secondly, addressing the strong thermal sensitivity of the magnet and the thermal coupling between multiple loops, a four-input, six-output dual-loop water bath temperature control system was developed. This system combines decentralized, segmented PID control with a static feedforward decoupling algorithm based on Relative Gain Array (RGA) identification. It achieved stable control of the magnet temperature at 21.00 ± 0.02°C (over 12 hours) amidst wide-range ambient temperature fluctuations (16-35°C). Building upon this, the temperature control hardware was further upgraded into a full-featured NMR measurement platform. By integrating a custom probe (incorporating sample tube positioning, shim coils, and an RF interface) with the temperature-controlled medium chamber, and optimizing the temperature control circuit board, a compact temperature-control-measurement integrated module was constructed. Through standardized interfaces, the platform was connected to an existing transceiver system, completing the platform integration. Final experiments successfully acquired spin-echo signals from deionized water, achieving full functional verification from precise temperature control and field stabilization to NMR signal acquisition. This work marks a significant step for portable low-field NMR technology, transitioning from modular research to a fully integrated, sample-ready practical platform.

Tobacco mosaic virus (TMV) is one of the most intensively studied plant viruses, and the development of easy-to-prepare, economical, sensitive, and reliable TMV RNA (TRNA) detection technologies hold great significance for plant virus diagnosis and treatment. In this study, a label-free electrochemical biosensor was constructed for the first time to detect TRNA based on target-driven multicomponent deoxyribonuclease (MNAzyme) amplifier and DNA tetrahedral (TDN) ordered modification. The detection process begins with the target RNA triggering the assembly of MNAzyme, which then cleave the hairpin DNA (HP) to yield biotin-labeled ssDNA, leading to cyclic amplification. The TDN attached to the electrode ligates ssDNA via a top capture probe, generating an enhanced current signal through the specific binding of biotin and streptavidin. By utilizing the dual signal amplification capabilities of TDN and MNAzyme, the fabricated sensor achieved a low limit of detection (LOD) of 0.17 pM and a linear range of 1 pM to 10nM under optimized conditions. The results demonstrate that the sensor has significant potential for application in complex biological environments, offering a promising tool for the detection of plant viruses.

Hydrogen sulfide (H2S) is a highly toxic gas commonly encountered in industrial settings, posing substantial health risks to workers. Therefore, the development of sensors with optimized operating temperatures, rapid detection speeds, and broad measurement ranges is of paramount engineering significance. In this study, ZnFe2O4/MoO2 nanocomposites were synthesized using a hydrothermal method, and, for the first time, MEMSbased gas sensors utilizing ZnFe2O4/MoO2 nanocomposite films were employed for hydrogen sulfide detection. The structural composition and morphology of the ZnFe2O4/MoO2 (2:1) samples were characterized through Xray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS). The gas sensitivity of ZnFe2O4/MoO2 toward HAS was systematically investigated. Gas sensitivity tests demonstrated that the ZnFe2O4/MoO2 composite sensor exhibited a response value of 19.177 at 5 ppm hydrogen sulfide, which is 5.2 times greater than that of ZnFe2O4, the current dominant material for HAS detection. Furthermore, the ZnFe2O4/MoO2 composite sensor demonstrated a detection limit as low as 1 ppm and exhibited significantly faster response and recovery times (13 s and 11 s, respectively, for 5 ppm HAS). The ZnFe2O4/MoO2 (2:1) nanocomposite sensor exhibited excellent stability, sensitivity, and remarkable selectivity. This enhanced gas-sensing performance is attributed to the formation of n-n heterojunctions between ZnFe2O4 and MoO2, as well as the optimized nanomorphology of the composite. Therefore, the ZnFe2O4/MoO2 sensor presents a promising solution for hydrogen sulfide detection in industrial applications.

According to a survey, local defects are the primary cause of permanent cable faults. In addition to causing power outages, these faults can also cause loss of control. It is necessary to identify local defects to prevent such incidents. However, there are some limitations to the current detection methods. This paper proposes an innovative method based on Born iteration to detect local defects in cables. The Green's function solution to the transmission line equation is first obtained. An impedance spectrum calculation method based on Green's function is therefore developed to study the effects of local defects on the impedance spectrum. Next, to convert the impedance spectrum data into contrast parameters along the cable, the BI method is developed. The contrast parameters characterize the variation in the propagation constant along the cable so that defects can be located. Simulation and experiment results indicate that both the impedance spectrum calculation method based on Green's function and the BI method perform well. The impedance spectrum calculation method based on Green's function has a 3% calculation error. The BI method can not only detect two defects as small as 10 cm on a 300 m cable but also indicate the severity of the defects.

In this study, BMIMPF6 was modified by adding BCNT/Co to it. The BCNT/Co + BMIMPF6 electrolyte system reduced the sensor response time to 16 s and showed good linearity in the range of 0–60% oxygen concentration.

In order to assess the health condition of machines, it is necessary to construct a suitable health indicator (HI) for detecting the initial degeneration point (IDP). However, there are two limitations aimed at existing HIs: 1) manually extracted feature-based construction methods strongly rely on the expert knowledge and experience and 2) most HIs are insensitive to the early faults of machines the fault component is unobvious and even overwhelmed by noises whether in the time domain or the frequency domain. For addressing these problems, a deep metric learning-based HI construction method is proposed for detecting IDPs of machines. The proposed method is mainly comprised of three steps. First, a multiscale deep metric learning model with self-attention modules is built to extract features according to an improved contrastive loss (ICL). Then, the relative similarity of extracted features between the baseline sample data and the currently acquired sample data is calculated as the current HI to represent the health condition of machines. Finally, an optimization algorithm, namely particle swarm optimization (PSO)-genetic algorithm (GA), is designed to search for the optimal model parameters. The performance of the proposed method is verified in two different experiments and compared with other methods. Results show that this method is able to identify IDPs more precisely for machines.

In the drilling and exploitation for natural gas hydrate, the decomposition of combustible ices creates the interface between their solid state and liquid state, which would play an important role in the dynamic moni-toring while exploitation. In this paper, a monitoring method based on the thermoacoustic effect is proposed to detect the position of the interface. The mechanism of the thermoacoustic coupling is introduced into the natural gas hydrate, that is, the physical model of thermoacoustic effect is established and detailed when the hydrate is excited by a strong electrical pulse. Two kinds of acoustic sources, the thermoelastic source and the phase transition acoustic source, are proposed to explain the obviously enhanced acoustic signals. By modeling and simulating, it is verified that the acoustic signals can be generated by the phase transition around the interface in hydrate. At the same time, many influencing factors on the thermoacoustic signals are researched based on simulations, such as the size of hydrate, the latent heat absorption coefficient and the amplitude of the transient electrical pulse. Finally, the experimental system both for hydrate generation and for thermoacoustic monitoring is built. We prepared carbon dioxide hydrate in a high-pressure reactor at an initial pressure of 5 MPa and temperature of-5 degrees C. Then take the hydrate into the environment of room temperature and standard pressure, apply the transient electrical pulse to the hydrate and use the ultrasonic probe to detect the acoustic signals. The results show that acoustic signals emitted from the position of the solid-liquid interface can be employed to monitor the hydrate decomposition experimentally and actually.

Magneto-acoustic tomography with current injection (MAT-CI) is a type of hybrid imaging; under the excitation of the static magnetic field, the thermoacoustic effect and the Lorentz force effect will exist at the same time. Therefore, the detected signal is a mixed signal generated by the simultaneous action of the two effects, but the influence of excitation parameters on the two effects is different. In this paper, for objects with different conductivity, the proportion of thermoacoustic signal (TA) and magneto-acoustic signal (MA) in the mixed signal is quantitatively analyzed in terms of three aspects: the magnetic induction intensity, pulse excitation and injection current polarity. Experimental and simulation analyses show that the intensity ratio of MA to TA is not affected when the conductivity varies from 0.1 S/m to 1.5 S/m and other conditions remain unchanged. When the amplitude of the pulse excitation and the strength of the magnetic induction are different, the growth rates of MA and TA are different, which has a significant impact on the proportion of the two signals in the mixed signal. At the same time, due to the Lorentz force effect, MA is affected by the polarity of the injected current and the direction of the static magnetic field. The combination of the static magnetic field and the injected current can not only distinguish the two signals in the mixed signal, but also effectively enhance the intensity of the mixed signal and improve the quality of the reconstructed image.

A discharge in contact with water in atmospheric air and argon (Ar) was presented. The electrical and optical characteristics were experimentally investigated and compared in different voltage ranges. The waveforms of voltage and current revealed this discharge didn't happen symmetrically in each period due to the asymmetric geometry of the reactor. The OH intensity obtained by Optical Emission Spectrometer (OES), discharge power P and transfer charge Q analyzed by the Lissajous method, and rotational temperature T-r and vibrational temperature T-v fitted by LIFBASE software based on OH spectra were bigger in Ar-H2O dielectric barrier discharge (DBD) than in air-H2O at 11 and 12 kV, except only T-v of Ar-H2O DBD was a little bit smaller than that of air-H2O DBD at 12 kV. T-r and T-v indicated these two gas-H2O DBDs were non thermal-equilibrium discharges. This experimental work is helpful in comprehensively understanding the discharge manner of gas-liquid at electrical, physical, and chemical aspects.

Magneto-acoustic tomography with current injection (MAT-CI) belongs to hybrid imaging, under the excitation of the static magnetic field, the thermoacoustic effect and the Lorentz force effect will exist at the same time. Therefore, the detected signal is a mixed signal generated by the simultaneous action of two effects, but the influence of excitation parameters on the two effects is different. In this paper, for different conductivity objects, the proportion of thermoacoustic signal (TA) and magneto-acoustic signal (MA) in mixed signal is quantitatively analyzed from the three aspects: the magnetic induction intensity, pulse excitation and injection current polarity. Experimental and simulation analysis show that the intensity ratio of MA to TA is not affected when the conductivity varies from 0.1S/m to 1.5S/m and other conditions remain unchanged. When the amplitude of the pulse excitation and the strength of the magnetic induction are different, the growth rates of MA and TA are different, which has a significant impact on the proportion of the two signals in the mixed signal. At the same time, due to the Lorentz force effect, MA is affected by the polarity of the injected current and the direction of the static magnetic field. The combination of the static magnetic field and the injected current can not only distinguish the two signals in the mixed signal, but also effectively enhance the intensity of the mixed signal, and improve the quality of the reconstructed image.

Herein, an electroluminescence (ECL) biosensor was constructed by combining click chemistry with activators regenerated by electron transfer-atom transfer radical polymerization (ARGET-ATRP) to sensitively assay tobacco mosaic virus (TMV) RNA for the first time. First, hairpin DNA (hDNA) was self-assembled on the gold electrode surface through Au-S bonding. The hDNA hybridized with the tDNA to form tRNA/hDNA hybrids in the presence of TMV RNA (tRNA), so that the azide group labelled at the end of the hDNA was kept away from the electrode surface. Subsequently, the initiator for the ARGET-ATRP reaction was modified on the electrode surface by chemical bonds via click chemistry. Then, N-acryloxysuccinimide (NAS)-labelled polymer chains were successfully formed on the electrode surface by ARGET-ATRP. Under the optimized conditions, a good linear relationship existed with the ECL signal and the logarithm of tRNA concentration in the range of 0.1 pM-10 nM, and the limit of detection was 2.61 fM. In addition, this strategy can identify mismatched bases and performs well in recovery assays in real samples. For its high sensitivity, selectivity, simplicity and economy, the ECL biosensor shows great potential for practical applications.

以潜油电泵机组的运行电流为主要判别依据,将长短时记忆神经网络应用于潜油电泵运行状态预测中,对于特征不明显的故障类型,利用潜油电泵井运行电压、运行电流、功率、油压、井口温度和瞬时流量数据预测下一时刻的电流值,并利用单分类支持向量机模型来预判潜油电泵机组的运行状态,从而实现潜油电泵的故障预警.最后,利用实际生产数据对模型进行验证.结果表明,所提方法预测准确度较高,可将报警时间提前1 h,实现故障的预警及诊断.

Efficient and safe nanopesticides play an important role in pest control due to enhancing target efficiency and reducing undesirable side effects, which has become a hot spot in pesticide formulation research. However, the preparation methods of nanopesticides are facing critical challenges including low productivity, uneven particle size and batch differences. Here, we successfully developed a novel, versatile and tunable strategy for preparing buprofezin nanoparticles with tunable size via anodic aluminum oxide (AAO) template-assisted method, which exhibited better reproducibility and homogeneity comparing with the traditional method. The storage stability of nanoparticles at different temperatures was evaluated, and the release properties were also determined to evaluate the performance of nanoparticles. Moreover, the present method is further demonstrated to be easily applicable for insoluble drugs and be extended for the study of the physicochemical properties of drug particles with different sizes.

Up to date, compression packers have been widely utilized to seal annular space between tubing and casing in the fracturing process of low permeability reservoirs. However, packer end damage under great annulus pressure difference and internal shear failure caused by excessive pressure in tubing are the main failure reasons. Current research works mainly focus on the theoretical model of sealing property with contact stress as evaluation index and less consideration was given to pressure-bearing property in rubber. This paper established a novel analytical model which took into account material properties, geometric dimension and borehole expansion ratio setting the maximum shear stress as evaluation criteria, which can investigate the distribution of shear stress along the axial diameters on compression packer under one end loaded and other end fixed. Also, the accuracy of the analytical model was verified by FEM. Main results illustrated that a) the analytical results can describe the mechanical behavior of the compression process well and the recommended setting pressure was less than 40 MPa; b) with the materials hardened, the compression distance reduced smoothly and the maximum shear stress almost kept unchanged; c) the compression deformation and maximum shear stress rose significantly as the inner diameter of the open hole well increased; d) the height of the packer played a negative role both in maximum compression distance and maximum shear stress of the compression packer.

Applied Current Thermoacoustic Imaging (ACTAI) is a new imaging method which combines electromagnetic excitation with ultrasound imaging, and takes ultrasonic signal as medium and biological tissue conductivity as detection target. Taking the high contrast advantage of Electrical Impedance Tomography (EIT) and high resolution advantage of ultrasound imaging, ACTAI has broad application prospects in the field of biomedical imaging. Although ACTAI has high excitation efficiency and strong detectable Signal-to-Noise Ratio, yet while under low frequency electromagnetic excitation, it is still a big challenge to reconstruct a high-resolution image of target conductivity. This paper proposes a new method for reconstructing conductivity based on Generative Adversarial Network, and it consists of three main steps: firstly, use Wiener filtering deconvolution to restore the electrical signal output by the ultrasonic probe to a real acoustic signal. Then obtain the initial acoustic source image with filtered backprojection technology. Finally, match the conductivity image with the initial sound source image, which are used as training samples for generating the adversarial network to establish a deep learning model for conductivity reconstruction. After theoretical analysis and simulation research, it is found that by introducing machine learning, the new method can dig out the inverse problem solving model contained in the data, which further reconstruct a high-resolution conductivity image and has strong anti-interference characteristics. The new method provides a new way to solve the problem of conductivity reconstruction in Applied Current Thermoacoustic Imaging.