Search Results (281)

Highly sensitive real-time detection of hydrogen sulfide (H₂S) is important for human health and environmental protection due to its highly toxic properties. The development of high-performance H₂S sensors remains challenging for poor selectivity, high limit detection and slow recovery from irreversible sulfidation. To solve these problems, we strategically prepared a layered structure of CuO-sensitized WO₃ flower-like hollow spheres with CuO as the sensitizing component. A 15 wt% CuO/WO₃ exhibits an ultra-high response (R_a/R_g = 571) to 10 ppm H₂S (131-times of pure WO₃), excellent selectivity (97-times higher than 100 ppm interference gas), and a low detection limit (100 ppb). Notably, its fast response (4 s) is accompanied by full recovery within 236 s. After 30 days of continuous testing, the response of 15 wt% CuO/WO₃ decreased slightly but maintained the initial response of 90.5%. The improved performance is attributed to (1) the p-n heterojunction formed between CuO and WO₃ optimizes the energy band structure and enriches the chemisorption sites for H₂S; (2) the reaction of H₂S with CuO generates highly conductive CuS, which significantly reduces the interfacial resistance; and (3) the hierarchical flowery hollow microsphere structure, heterojunction, and oxygen vacancy synergistically promote the desorption. This work provides a high-performance H₂S gas sensor that balances response, selectivity, and response/recovery kinetics. Full article

Mesoporous ZnO/ZnCo₂O₄ nanocomposites with excellent gas-sensing performance were synthesized using the Ostwald ripening method. The as-prepared ZnO/ZnCo₂O₄ comprised aggregated monodisperse nanoparticles, and the nanoparticle size grew with increasing thermal treatment temperature. Increasing the calcination temperature did not significantly change the overall size of the ZnO/ZnCo₂O₄ nanocomposites, but the pore size and specific surface area were noticeably affected. The gas-sensing results showed that ZnO/ZnCo₂O₄ composites calcined at 500 °C exhibited the highest response to H₂S at 200 °C, with a detection limit of 500 ppb. The ZnO/ZnCo₂O₄ composites also exhibited remarkable selectivity, response/recovery speed, and stability. Their excellent gas-sensing performance might be attributed to their porous structure, large specific surface area, and the heterogeneous interface between ZnO and ZnCo₂O₄. This work not only represents a new example of the Ostwald ripening-based formation of inorganic hollow structures in a template-free aqueous solution but also provides a novel and efficient sensing material for the detection of H₂S gas. Full article

Aiming at the complex internal working conditions of steel-reinforced concrete structures, this paper proposes an active detection method for the internal hollow defects of steel-reinforced concrete based on wave analysis by using the driving and sensing functions of piezoelectric ceramic materials. The feasibility was verified through the single-condition detection test, revealing the propagation and attenuation characteristics of the stress wave signal under various detection conditions, and it was applied to the damage identification of steel-reinforced concrete rectangular section columns. Combined with the wavelet packet energy theory, the data processing of the original detection signal is carried out based on composite weighting by energy distribution entropy. Finally, the analytic hierarchy process (AHP) was introduced to study the weight vectors of different damage metrics on the detection signal, and a linear regression model based on different damage metrics was proposed as the comprehensive defect evaluation index. The research results show that the detection of internal defects in steel-reinforced concrete structures based on piezoelectric technology is applicable to concrete of different strength grades. With the increase of the detection distance and the degree of damage, the energy of the stress wave signal decreases. For example, under defect-free conditions, the energy value of the stress wave signal with a detection distance of 400 mm decreases by 92.94% compared to that with a detection distance of 100 mm. Meanwhile, it can be known from the defect detection test results of steel-reinforced concrete columns that the wavelet packet energy value under the defect condition with three obstacles decreased by 85.42% compared with the barrier-free condition, and the defect evaluation index (DI) gradually increased from 0 to 0.859. The comprehensive application of piezoelectric technology and weight analysis methods has achieved qualitative and quantitative analysis of defects, providing reference value for the maintenance and repair of steel-reinforced concrete structures. Full article

Objectives: To introduce a novel method for apical lesion sampling using a protected paper point device and to evaluate its effectiveness and robustness during the sampling process in vitro. Methods: A prototype for apical sample collection was developed as an adaptation of the Micro-Apical Placement System—the device features a highly tapered screw head with a thin, hollow, stainless-steel tube and an internal wire piston. Standardized 5 mm paper points (ISO 10; PD Dental, Switzerland) served as carrier material. The prototype was tested using 30 × 3D-printed, single-rooted tooth models inoculated using two bacterial strains (Staphylococcus epidermidis and Escherichia coli) to simulate apical and intraradicular bacterial infections, respectively. The sampling process involved collecting and analyzing samples at specific timepoints, focusing on the presence or absence of E. coli contamination. Following sample collection, cultural detection of bacterial presence was performed by incubating the samples on agar plates to confirm the presence of E. coli. Samples were collected as follows: S0 (sterility control of the prototype), P0 (sterility control of the tooth model), P1 (apical sample collected with the CAPS (controlled apical sampling) device, and P2 (contamination control sample to check for the presence of E. coli inside the root canal). Results: Handling of the CAPS prototype was straightforward and reproducible. No loss of paper points or complications were observed during sample collection. All sterility samples (P0, S0) were negative for tested microorganisms, confirming the sterility of the setup. P2 samples confirmed the presence of E. coli in the root canal in all trials. The P1 samples were free from contamination in 86.67% of trials. Conclusions: The CAPS method for apical sampling demonstrated advances in the successful and precise sample collection of apically located S. epidermidis and will be a useful tool for endodontic microbiological analysis. Its user-friendly design and consistent performance highlight its potential for clinical application, contributing to more accurate microbial diagnostics and later patient-specific therapeutic approaches in endodontic treatments. Full article

Concrete dams are prone to various hidden dangers after long-term operation and may lead to significant risk if failed to be detected in time. However, the existing hollowing detection techniques are few as well as inefficient when facing the demands of comprehensive coverage and intelligent management for regular inspections. Hence, we proposed an innovative, non-destructive infrared inspection method via constructed dataset and proposed deep learning algorithms. We first modeled the surface temperature field variation of concrete dams as a one-dimensional, non-stationary partial differential equation with Robin boundary. We also designed physics-informed neural networks (PINNs) with multi-subnets to compute the temperature value automatically. Secondly, we obtained the time-domain features in one-dimensional space and used the diffusion techniques to obtain the synthetic infrared images with dam hollowing by converting the one-dimensional temperatures into two-dimensional ones. Finally, we employed adaptive joint learning to obtain the spatio-temporal features. We designed the experiments on the dataset we constructed, and we demonstrated that the method proposed in this paper can handle the low-data (few shots real images) issue. Our method achieved 94.7% of recognition accuracy based on few shots real images, which is 17.9% and 5.8% higher than maximum entropy and classical OTSU methods, respectively. Furthermore, it attained a sub-10% cross-sectional calculation error for hollowing dimensions, outperforming maximum entropy (70.5% error reduction) and OTSU (7.4% error reduction) methods, which shows our method being one novel method for automated intelligent hollowing detection. Full article

In this paper, an all-fiber light-induced thermoelastic spectroscopy (LITES) sensor based on hollow-core anti-resonant fiber (HC-ARF) and self-designed low-frequency quartz tuning fork (QTF) is reported for the first time. By utilizing HC-ARF as both the transmission medium and gas chamber, the laser tail fiber was spatially coupled with the HC-ARF, and the end of the HC-ARF was directly guided onto the QTF surface, resulting in an all-fiber structure. This design eliminated the need for lens combinations, thereby enhancing system stability and reducing cost and size. Additionally, a self-designed rectangular-tip QTF with a low resonant frequency of 8.69 kHz was employed to improve the sensor’s detection performance. Acetylene (C₂H₂), with an absorption line at 6534.37 cm⁻¹ (1.53 μm), was chosen as the target gas. Experimental results clearly demonstrated that the detection performance of the rectangular-tip QTF system was 2.9-fold higher than that of a standard commercial QTF system. Moreover, it exhibited an outstanding linear response to varying C₂H₂ concentrations, indicating its high sensitivity and reliability in detecting C₂H₂. The Allan deviation analysis was used to assess the system’s stability, and the results indicated that the system exhibits excellent long-term stability. Full article

Retina-like photoimaging devices with features such as a wide-field-of-view and high spatial resolution have wide application prospects in retinal prosthetics and remote sensing. However, the fabrication of flexible and conformal surfaces is hindered by the incompatible microfabrication processes of traditional rigid, silicon-based substrates. A kirigami strategy for hemispherical surface assembly is proposed to construct a MoS₂-based retina-like photodetector array. The device is first fabricated on a flat polyimide (PI) substrate and then tailored using a laser. By approximating the spherical surface using planar sectors, the laser-cut PI film can tightly adhere to the PDMS spherical shell without significant wrinkles. The responsivity and specific detectivity of our conformal photodetector can reach as high as 247.9 A/W and 6.16 × 10¹¹ Jones, respectively. The array integrates 180 pixels on a spherical crown with a radius of 11 mm, and a hollow letter “T” is successfully recognized. Comprehensive experimental results in this work reveal the utility of our device for photoelectric detection and imaging. We believe that our work provides a new methodology for the exploitation of 2D material-based retinal image sensors. Full article

Background/Objectives: Dermal interstitial fluid (dISF) is probably the most interesting biofluid for biomarker analysis as an alternative to blood, enabling higher patient comfort and closer or even continuous biomarker monitoring. The prerequisite for dISF-based analysis tools is having convenient access to dISF, as well as a better knowledge of the presence, concentration, and dynamics of biomarkers in dISF. Hollow microneedles represent one of the most promising platforms for access to pure dISF, enabling the mining of biomarker information. Methods and Results: Here, a microneedle-based method for dISF sampling is presented, where a combination of hollow microneedles and sub-pressure is used to optimize both penetration depth in skin and dermal interstitial fluid sampling volumes, and the design of an open, prospective, exploratory, and interventional study to examine the detectability of inflammatory and cardiocirculatory biomarkers in the dISF of heart failure patients, the relationship between dISF-derived and blood-derived biomarker levels, and their kinetics during a cardiopulmonary exercise test (CPET) is introduced. Conclusions: The dISF sampling method and study presented here will foster research on biomarkers in dISF in general and in heart failure patients in particular. The study is part of the European project DIGIPREDICT—Digital Edge AI-deployed DIGItal Twins for PREDICTing disease progression and the need for early intervention in infectious and cardiovascular diseases beyond COVID-19. Full article

Hollow-dependent wildlife has been declining globally due to the removal of hollow-bearing trees, yet these trees are often unaccounted for in habitat mapping. As on-ground field surveys are costly and time-consuming, we aimed to develop a simple, accessible and transferrable geospatial approach using freely accessible LiDAR to refine habitat mapping by identifying high densities of potential hollow-bearing trees. We assessed if LiDAR from 2009 could be accurately used to detect tree heights, which would correlate to tree diameter at breast height (DBH), which in turn would identify trees that are more likely to be hollow-bearing. Here, we use habitat mapping of greater gliders (Petauroides spp.) in the Fraser Coast region of Australia as a case study. Across four sites, field surveys were conducted in 2023 to assess the tree height and density of large trees (>50 cm DBH per 1 km²) at 19 transects (n = 91). This was compared to outputs from individual tree detection derived from unsupervised classification using a local maximal filter and variable window size to identify treetops in freely available LiDAR. Tree height was measured with an accuracy of RMSE 5.75 m, and we were able to identify transects with large trees (>50 cm DBH), which were more likely hollow bearing. However, there was no statistical evidence to suggest that transects with a high density of large trees could be accurately identified based on LiDAR alone (>50 cm DBH p 0.2298). Despite this, we have demonstrated that freely accessible LiDAR and unsupervised machine learning techniques can be utilised to identify large, potentially hollow-bearing trees on a broad scale to refine habitat mapping for hollow-dependent species. It is important to develop geospatial analysis methods that are more accessible to land managers, as deep machine learning methods and current LiDAR can be computationally intensive and expensive. We propose a workflow using free and accessible geospatial analysis methods to identify large, potentially hollow-bearing trees and determine how to address some limitations in this geospatial approach. Full article

Toxic and harmful gases, particularly volatile organic compounds like triethylamine, pose significant risks to human health and the environment. As a result, metal oxide semiconductor (MOS) sensors have been widely utilized in various fields, including medical diagnostics, environmental monitoring, food processing, and chemical production. Extensive research has been conducted worldwide to enhance the gas-sensing performance of MOS materials. However, traditional MOS materials suffer from limitations such as a small specific surface area and a low density of active sites, leading to poor gas sensing properties—characterized by low sensitivity and selectivity, high detection limits and operating temperatures, as well as long response and recovery times. To address these challenges in triethylamine detection, this paper reviews the synthesis of nano-microspheres, porous micro-octahedra, and hollow prism-like nanoflowers via chemical solution methods. The triethylamine sensing performance of MOS materials, such as ZnO and In₂O₃, can be significantly enhanced through nano-morphology control, electronic band engineering, and noble metal loading. Additionally, strategies, including elemental doping, oxygen vacancy modulation, and structural morphology optimization, have been employed to achieve ultra-high sensitivity in triethylamine detection. This review further explores the underlying mechanisms responsible for the improved gas sensitivity. Finally, perspectives on future research directions in triethylamine gas sensing are provided. Full article

Traditional emitters used for downhole acoustic detection have limited radiation frequency and energy, making it difficult to transmit high-precision acoustic signals over long distances. This paper presents a plasma emitter in which high-pressure discharge generates a powerful spherical impulse wave with a wide frequency range. First, the discharge characteristics of the plasma needle-plate emitter are analyzed using high-voltage discharge experiments and discharge simulation models for underwater emitters. Subsequently, advanced modifications are made to the structure of the needle–plate emitter to meet the requirements of downhole detection. A new type of hollow needle–plate emitter with a spherical tip is developed. The results show that the structural optimization of the hollow needle–plate emitter with a spherical tip resulted in a 27.2% increase in impulse wave amplitude, a 28.1% improvement in electro-acoustic conversion efficiency, and a radiation frequency band covering up to 100 kHz. This development is conducive to more accurate and longer-range downhole structure detection. The detection range outside the borehole can reach tens to hundreds of meters. This enables the precise control of the wellbore path and reduces the demands on the rig’s build rate. The emitter has significant application potential in areas such as onshore and offshore oil and gas exploration, unconventional resource detection, impulse wave fracturing and wellbore clearance, and rescue and U-well drilling. Full article

A multi-gas sensing system based on mid-infrared spectral absorption was developed for the detection of CH₄, C₂H₆, and C₂H₄. The system utilized a broadband infrared source, a hollow waveguide (HWG) absorption cell, and a tunable Fabry–Pérot (FP) detector. The limits of detection (LODs) of CH₄, C₂H₆, and C₂H₄ were 7.33 ppm, 2.13 ppm, and 8.09 ppm, respectively. For multi-gas measurements, the support vector machine (SVM) algorithm model was employed to calculate the concentration of each component. The root mean square error of prediction (RMSEP) values for CH₄, C₂H₆, and C₂H₄ were 15.91 ppm (1.26%), 1.64 ppm (0.57%), and 6.95 ppm (0.55%), respectively. The generation of stimulated absorption spectra of mixed gases was realized, and the sample selection of measurement for accurate concentration calculation of each gas was optimized. The system proposed in this work provides a simple, miniaturized, and cost-effective solution for multi-gas sensing. Full article

A novel hollow capacitive wind pressure sensor is for the first time proposed. The sensing element of the proposed sensor uses a non-parallel plate variable capacitor, whose movable electrode plate uses a transversely uniformly loaded annular conductive membrane with a fixed outer edge and a rigid inner edge (acting as the wind pressure sensitive element of the sensor). Due to the unique hollow configuration of the proposed sensor, it can be used alone to detect the pressure exerted by fast-moving air in the atmosphere or by fast-moving air or gas, etc., in pipes, but it also can be used in pairs to measure the flow rate of fast-moving air or gas, etc., in pipes. The analytical solution of the large deflection elastic behavior of the transversely uniformly loaded annular conductive membrane is derived by using a new set of membrane governing equations. The effectiveness of the new analytical solution is analyzed. The new membrane governing equations are compared with the previous ones to show the differences between them. The superiority of the new analytical solution over the existing ones is analyzed. An example is given to demonstrate the numerical design and calibration of the proposed sensor and the effect of changing design parameters on the important capacitance–pressure (C–q) analytical relationship of the proposed sensor is investigated comprehensively. Finally, an experimental verification of the analytical solution derived is carried out. Full article

This study investigated the morphological dependence of ZnO nanostructures, specifically nanotube- and nanorod-based electrodes, on their electrochemical performance for the detection of lead ions (Pb²⁺) in aqueous solutions. The results demonstrate that ZnO nanotubes exhibit significantly enhanced sensitivity compared to nanorods during CV measurements. During SWV measurements, the sensitivity (116.79 mA·mM⁻¹) and a lower limit of detection of 0.0437 μM were determined. The hollow, high-aspect-ratio structure of nanotubes provides a larger active surface area and facilitates better ion accessibility, resulting in superior electron transfer efficiency and catalytic activity. These results underscore the critical role of morphology in optimizing ZnO-based sensors. Analysis of real water samples from various natural reservoirs revealed no detectable lead, while lead was identified exclusively in artificially prepared samples containing water exposed to lead hunting shot. Over a 30-day period, the sensor retained over 95% of its initial performance when stored under vacuum conditions, demonstrating minimal signal degradation. Under ambient conditions, stability loss was attributed to moisture adsorption on the porous nanostructure. The sensor also displayed outstanding reproducibility, with current response variations across multiple probes remaining within 4%. The cost-effective and simple fabrication process of ZnO nanostructures further highlights their potential for scalable production, environmental monitoring, and integration into portable sensing devices. Full article

Researchers have already used vibration data and deep learning methods, such as Convolutional Neural Networks (CNNs), to detect structural damage. Moreover, some researchers have employed image-based displacement sensors (such as the template matching and edge detection methods) to obtain structural vibration information. It is necessary to verify whether deep learning methods can detect minor damage inside beams, for example, small hollowing in concrete. In addition, there is an urgent need to develop an effective image-based displacement sensor that can simultaneously detect a large number of reliable vibration data from different measurement points. In this study, the vibration data of two beam-ABAQUS models were used as the input data for a newly designed deep learning-based structural health monitoring method. There were 500 vibration samples for each case, and the peak of vibrations was several millimeters. The proposed CNN model can locate damage positions in beams with high accuracy (close to 100%), and the damage sizes are 3 cm and 6 cm. Laboratory experiments were carried out on four beams with different damage. The optimized displacement sensor developed based on the edge detection method was used to detect the displacement of the beams. Each beam had 200 vibration data, and there were 800 vibration data in total. These vibration data were used as input data to train the proposed deep learning architecture, and satisfactory accuracy was achieved in detecting the damage of the beams with an accuracy of 97%. The training process is satisfactory in that the training loss and validation loss dropped very quickly. Full article