Search Results (51)

To address the challenges posed by the difficulty of extracting fault features from rotating machinery with weak fault features, this paper proposes a rotating machinery structural faults feature enhancement and diagnosis method based on multi-sensor information fusion. Firstly, Savitzky–Golay filtering suppresses noise and enhances fault features. Secondly, the designed multi-sensor symmetric dot pattern (SDP) transformation method fuses multi-source information of the rotating machinery structural faults, providing more comprehensive and richer fault feature information for diagnosis. Finally, the ResNet18 model performs fault diagnosis. To validate the feasibility and effectiveness of the proposed method, two datasets verify its performance. The accuracy of the experimental results was 99.16% and 100%, respectively, demonstrating the feasibility and effectiveness of the proposed method. To further validate the superiority of the proposed method, it was compared with different 2D signal transformation methods. The comparison results indicate that the proposed method achieves the best fault diagnosis accuracy compared to other methods. Full article

In this study, β-cyclodextrin (β-CD) functionalized graphene quantum dots (GQDs) was employed to augment the array of chiral recognition sites, thereby enhancing the affinity of GQDs/β-CD composite for imprinting molecules and realizing heightened chiral selectivity. The incorporation of GQDs/β-CD into the synthesis of molecularly imprinted polymers (MIPs), synergizing with the host-guest inclusion properties of β-CD and the abundant carboxyl groups of GQDs, enhanced the chiral recognition capacity of MIPs materials. Consequently, a novel MIPs/(GQDs/β-CD) sensor with chiral recognition capabilities tailored for D-carnitine was successfully fabricated. The binding mechanism between GQDs/β-CD and D-carnitine was elucidated via Ultraviolet-visible spectroscopy and Fourier transform infrared spectroscopy. The variation in the response signal (ΔI) of the probe molecule exhibited a linear correlation with the logarithm of D-carnitine concentration (lgC) in the range of 1.0 × 10⁻¹² mol/L to 1.0 × 10⁻⁹ mol/L, and the detection limit (3δ/S) was calculated as 2.35 × 10⁻¹³ mol/L. These results underscore a 7.15-fold enhancement in the selectivity of MIPs/(GQDs/β-CD) sensor for D-carnitine recognition. Moreover, the sensor presented commendable efficacy in real-world scenarios, yielding recovery rates ranging from 98.5% to 103.0% during the determination of D-carnitine content in real samples. Full article

In hospitals, timely interventions can prevent avoidable clinical deterioration. Early recognition of deterioration is vital to stopping further decline. Measuring the way patients position themselves in bed and change their positions may signal when further assessment is necessary. While inertial measurement units (IMUs) have been used in health research, their use inside hospitals has been limited. This study explores the use of IMUs with machine learning to continuously capture, classify and visualise patient positions in hospital beds. The participants attended a data collection session in a simulated hospital bedspace and were asked to adopt nine positions. Movement data were captured using five IMU Xsens DOTs attached to the forehead, wrists and ankles. Support Vector Machine (SVM) and K-Nearest Neighbours classifiers were trained using five different combinations of sensors (e.g., right wrist only, right and left wrist) to determine body positions. Data from 30 participants were analysed. The highest accuracy (87.7%) was achieved by SVM using forehead and wrist sensors. Adding data from ankle sensors reduced the accuracy. To preserve patient privacy in a hospital setting, a 3D visualisation was developed in Unity, offering a non-identifiable representation of patient positions. This system could help clinicians monitor changes in position which may signal clinical deterioration. Full article

Ammonia (NH₃) gas is prevalent in industrial production as a health hazardous gas. Consequently, it is essential to develop a straightforward, reliable, and stable NH₃ sensor capable of operating at room temperature. This paper presents an innovative approach to modifying SnO₂ colloidal quantum dots (CQDs) on the surface of Ti₃C₂T_x MXene to form a heterojunction, which introduces a significant number of adsorption sites and enhances the response of the sensor. Zero-dimensional (0D) SnO₂ quantum dots and two-dimensional (2D) Ti₃C₂T_x MXene were prepared by solvothermal and in situ etching methods, respectively. The impact of the mass ratio between two materials on the performance was assessed. The sensor based on 12 wt% Ti₃C₂T_x MXene/SnO₂ composites demonstrates excellent performance in terms of sensitivity and response/recovery speed. Upon exposure to 50 ppm NH₃, the frequency shift in the sensor is −1140 Hz, which is 5.6 times larger than that of pure Ti₃C₂T_x MXene and 2.8 times higher than that of SnO₂ CQDs. The response/recovery time of the sensor for 10 ppm NH₃ was 36/54 s, respectively. The sensor exhibited a theoretical detection limit of 73 ppb and good repeatability. Furthermore, a stable sensing performance can be maintained after 30 days. The enhanced sensor performance can be attributed to the abundant active sites provided by the accumulation/depletion layer in the Ti₃C₂T_x/SnO₂ heterojunction, which facilitates the adsorption of oxygen molecules. This work promotes the gas sensing application of MXenes and provides a way to improve gas sensing performance. Full article

Prostate cancer is one of the most prevalent cancers afflicting men worldwide, often detected at advanced stages, leading to increased mortality rates. Addressing this challenge, we present an innovative approach employing electrochemical biosensing for early-stage prostate cancer detection. This study used Indium–Tin Oxide (ITO) as a substrate and a deposited gold–graphene quantum dot (Au–GQD) nanohybrid to establish electrochemical sensing platforms for DNA-hybridization assays. A capturing DNA probe, PCA3, was covalently immobilized on the surface of the Au–GQDs and deposited electrochemically onto the ITO electrode surface. The Au–GQDs enabled the capturing of the target PCA3 biomarker probe. The sensor achieved a limit of detection (LoD) of up to 211 fM and presented a linear detection range spanning 1 µM to 100 fM. A rapid 5-min response time was also achieved. The tested shelf life of the pre-immobilized sensor was approximately 19 ± 1 days, with pronounced selectivity for its intended target amidst various interferants. The sensing device has the potential to revolutionize prostate cancer management by facilitating early-stage detection and screening with enhanced treatment efficacy. Full article

Nitrobenzene (NB) is one of the nitro-aromatic compounds that is extensively used in various chemical industries. Despite its potential applications, NB is considered to be a toxic compound that has significant hazardous effects on human health and the environment. Thus, it can be said that the NB level should be monitored to avoid its negative impacts on human health. In this vein, the electrochemical method has emerged as one of the most efficient sensing techniques for the determination of NB. The sensing performance of the electrochemical techniques depends on the electro-catalytic properties and conductivity of the electrode materials. In the past few years, various electrode materials, such as conductive metal ions, semiconducting metal oxides, metal–organic frameworks, and two-dimensional (2D) materials, have been used as the electrode material for the construction of the NB sensor. Thus, it is worth summarizing previous studies on the design and synthesis of electrode materials for the construction of the NB sensor. In this mini-review article, we summarize the previous reports on the synthesis of various advanced electrode materials, such as platinum (Pt) nanoparticles (NPs), silver (Ag) NPs, carbon dots (CDs), graphene, graphitic carbon nitride (g-C₃N₄), zinc stannate (ZnSnO₃), cerium oxide (CeO₂), zinc oxide (ZnO), and so on. Furthermore, the impacts of different electrode materials are systematically discussed for the sensing of NB. The advantages of, limitations of, and future perspectives on the construction of NB sensors are discussed. The aim of the present mini-review article is to enhance the knowledge and overall literature, working towards the construction of NB sensors. Full article

Ammonia (NH₃) sensing is crucial for environmental safety, necessitating the development of efficient NH₃ sensors. In this study, an efficient NH₃ sensor based on CdS quantum dots (QDs) decorated with ZnO (CdS/ZnO) covering optical fiber was successfully fabricated. The CdS/ZnO was first synthesized by a hydrothermal method, featuring an n-n heterojunction in the composite material. The optimal sensor with 10 wt% CdS QDs exhibits efficient performance, with a response sensitivity of 0.9 × 10⁻³ dB/ppm and R² = 0.9858. Additionally, it demonstrates excellent selectivity and repeatability. Mechanistic insights for the NH₃ sensor were elucidated through X-ray photoelectron spectroscopy, energy-dispersive X-ray spectroscopy, scanning electron microscopy, and transmission electron microscopy. These results confirm that the enhancement in NH₃ sensing performance is attributed to the formation of well-defined n-n heterojunctions. This study contributes to the advancement of gas-sensing technology, particularly in the detection of harmful gases, such as NH₃. Full article

The development of fluorescent molecular imprinting sensors for direct, rapid, and sensitive detection of small organic molecules in aqueous systems has always presented a significant challenge in the field of detection. In this study, we successfully prepared a hydrophilic colloidal molecular imprinted polymer (MIP) with 2,4-dichlorophenoxyacetic acid (2,4-D) using a one-pot approach that incorporated polyglycerol methacrylate (PGMMA-TTC), a hydrophilic macromolecular chain transfer agent, to mediate reversible addition-fragmentation chain transfer precipitation polymerization (RAFTPP). To simplify the polymerization process while achieving ratiometric fluorescence detection, red fluorescent CdTe quantum dots (QDs) and green fluorescent nitrobenzodiazole (NBD) were introduced as fluorophores (with NBD serving as an enhancer to the template and QDs being inert). This strategy effectively eliminated background noise and significantly improved detection accuracy. Uniform-sized MIP microspheres with high surface hydrophilicity and incorporated ratiometric fluorescent labels were successfully synthesized. In aqueous systems, the hydrophilic ratio fluorescent MIP exhibited a linear response range from 0 to 25 μM for the template molecule 2,4-D with a detection limit of 0.13 μM. These results demonstrate that the ratiometric fluorescent MIP possesses excellent recognition characteristics and selectivity towards 2,4-D, thus, making it suitable for selective detection of trace amounts of pesticide 2,4-D in aqueous systems. Full article

A coenzyme A (CoA-SH)-responsive dual electrochemical and fluorescence-based sensor was designed utilizing an MnO₂-immobilized-polymer-dot (MnO₂@D-PD)-coated electrode for the sensitive detection of osteoarthritis (OA) in a peroxisomal β-oxidation knockout model. The CoA-SH-responsive MnO₂@D-PD-coated electrode interacted sensitively with CoA-SH in OA chondrocytes, triggering electroconductivity and fluorescence changes due to cleavage of the MnO₂ nanosheet on the electrode. The MnO₂@D-PD-coated electrode can detect CoA-SH in immature articular chondrocyte primary cells, as indicated by the significant increase in resistance in the control medium (R_24h = 2.17 MΩ). This sensor also sensitively monitored the increase in resistance in chondrocyte cells in the presence of acetyl-CoA inducers, such as phytol (Phy) and sodium acetate (SA), in the medium (R_24h = 2.67, 3.08 MΩ, respectively), compared to that in the control medium, demonstrating the detection efficiency of the sensor towards the increase in the CoA-SH concentration. Furthermore, fluorescence recovery was observed owing to MnO₂ cleavage, particularly in the Phy- and SA-supplemented media. The transcription levels of OA-related anabolic (Acan) and catabolic factors (Adamts5) in chondrocytes also confirmed the interaction between CoA-SH and the MnO₂@D-PD-coated electrode. Additionally, electrode integration with a wireless sensing system provides inline monitoring via a smartphone, which can potentially be used for rapid and sensitive OA diagnosis. Full article

Gas-sensing technology has witnessed significant advancements that have been driven by the emergence of graphene quantum dots (GQDs) and their tailored nanocomposites. This comprehensive review surveys the recent progress made in the construction methods and applications of functionalized GQDs and GQD-based nanocomposites for gas sensing. The gas-sensing mechanisms, based on the Fermi-level control and charge carrier depletion layer theory, are briefly explained through the formation of heterojunctions and the adsorption/desorption principle. Furthermore, this review explores the enhancements achieved through the incorporation of GQDs into nanocomposites with diverse matrices, including polymers, metal oxides, and 2D materials. We also provide an overview of the key progress in various hazardous gas sensing applications using functionalized GQDs and GQD-based nanocomposites, focusing on key detection parameters such as sensitivity, selectivity, stability, response and recovery time, repeatability, and limit of detection (LOD). According to the most recent data, the normally reported values for the LOD of various toxic gases using GQD-based sensors are in the range of 1–10 ppm. Remarkably, some GQD-based sensors exhibit extremely low detection limits, such as N-GQDs/SnO₂ (0.01 ppb for formaldehyde) and GQD@SnO₂ (0.10 ppb for NO₂). This review provides an up-to-date perspective on the evolving landscape of functionalized GQDs and their nanocomposites as pivotal components in the development of advanced gas sensors. Full article

This paper proposes a novel phase-resolved partial discharge (PRPD) sensor embedded in a MV-class bushing for high-accuracy insulation analysis. The design, fabrication, and evaluation of a PRPD sensor embedded in a MV-class bushing aimed to achieve the detection of partial discharge (PD) pulses that are phase-synchronized with the applied primary HV signal. A prototype PRPD sensor was composed of a flexible printed circuit board (PCB) with dual-sensing electrodes, utilizing a capacitive voltage divider (CVD) for voltage measurement, the D-dot principle for PD detection, and a signal transducer with passive elements. A PD simulator was prepared to emulate typical PD defects, i.e., a metal protrusion. The voltage measurement precision of the prototype PRPD sensor was satisfied with the accuracy class of 0.2 specified in IEC 61869-11, as the maximum corrected voltage error ratios and corrected phase errors in 80%, 100%, and 120% of the rated voltage (13.2 kilovolts (kV)) were less than 0.2% and 10 min, respectively. In addition, the prototype PRPD sensor had good linearity and high sensitivity for PD detection compared with a conventional electrical detection method. According to performance evaluation tests, the prototype PRPD sensor embedded in the MV-class bushing can measure PRPD patterns phase-synchronized with the primary voltage without any additional synchronization equipment or system. Therefore, the prototype PRPD sensor holds potential as a substitute for conventional commercial PD sensors. Consequently, this advancement could lead to the enhancement of power system monitoring and maintenance, contributing to the digitalization and minimization of power apparatus. Full article

Point cloud data generated by LiDAR sensors play a critical role in 3D sensing systems, with applications encompassing object classification, part segmentation, and point cloud recognition. Leveraging the global learning capacity of dot product attention, transformers have recently exhibited outstanding performance in point cloud learning tasks. Nevertheless, existing transformer models inadequately address the challenges posed by uncertainty features in point clouds, which can introduce errors in the dot product attention mechanism. In response to this, our study introduces a novel global guidance approach to tolerate uncertainty and provide a more reliable guidance. We redefine the granulation and lower-approximation operators based on neighborhood rough set theory. Furthermore, we introduce a rough set-based attention mechanism tailored for point cloud data and present the rough set transformer (RST) network. Our approach utilizes granulation concepts derived from token clusters, enabling us to explore relationships between concepts from an approximation perspective, rather than relying on specific dot product functions. Empirically, our work represents the pioneering fusion of rough set theory and transformer networks for point cloud learning. Our experimental results, including point cloud classification and segmentation tasks, demonstrate the superior performance of our method. Our method establishes concepts based on granulation generated from clusters of tokens. Subsequently, relationships between concepts can be explored from an approximation perspective, instead of relying on specific dot product or addition functions. Empirically, our work represents the pioneering fusion of rough set theory and transformer networks for point cloud learning. Our experimental results, including point cloud classification and segmentation tasks, demonstrate the superior performance of our method. Full article

Graphene quantum dots (GQDs), as 0D graphene nanomaterials, have aroused increasing interest in chemiresistive gas sensors owing to their remarkable physicochemical properties and tunable electronic structures. Research on GQDs has been booming over the past decades, and a number of excellent review articles have been provided on various other sensing principles of GQDs, such as fluorescence-based ion-sensing, bio-sensing, bio-imaging, and electrochemical, photoelectrochemical, and electrochemiluminescence sensing, and therapeutic, energy and catalysis applications. However, so far, there is no single review article on the application of GQDs in the field of chemiresistive gas sensing. This is our primary inspiration for writing this review, with a focus on the chemiresistive gas sensors reported using GQD-based composites. In this review, the various synthesized strategies of GQDs and its composites, gas sensing enhancement mechanisms, and the resulting sensing characteristics are presented. Finally, the current challenges and future prospects of GQDs in the abovementioned application filed have been discussed for the more rational design of advanced GQDs-based gas-sensing materials and innovative gas sensors with novel functionalities. Full article

Thin-film photodiodes (TFPD) monolithically integrated on the Si Read-Out Integrated Circuitry (ROIC) are promising imaging platforms when beyond-silicon optoelectronic properties are required. Although TFPD device performance has improved significantly, the pixel development has been limited in terms of noise characteristics compared to the Si-based image sensors. Here, a thin-film-based pinned photodiode (TF-PPD) structure is presented, showing reduced kTC noise and dark current, accompanied with a high conversion gain (CG). Indium-gallium-zinc oxide (IGZO) thin-film transistors and quantum dot photodiodes are integrated sequentially on the Si ROIC in a fully monolithic scheme with the introduction of photogate (PG) to achieve PPD operation. This PG brings not only a low noise performance, but also a high full well capacity (FWC) coming from the large capacitance of its metal-oxide-semiconductor (MOS). Hence, the FWC of the pixel is boosted up to 1.37 Me- with a 5 μm pixel pitch, which is 8.3 times larger than the FWC that the TFPD junction capacitor can store. This large FWC, along with the inherent low noise characteristics of the TF-PPD, leads to the three-digit dynamic range (DR) of 100.2 dB. Unlike a Si-based PG pixel, dark current contribution from the depleted semiconductor interfaces is limited, thanks to the wide energy band gap of the IGZO channel material used in this work. We expect that this novel 4 T pixel architecture can accelerate the deployment of monolithic TFPD imaging technology, as it has worked for CMOS Image sensors (CIS). Full article

Salmonella is one of the leading causes of foodborne illnesses worldwide, with poultry products being a major source of contamination. Thus, the detection of salmonella in commercial poultry products is crucial to minimize the effects on public health. Electrochemical sensors are promising tools for bacteria detection due to their sensitivity, simplicity, and potential for on-site analysis. In this work, a three-dimensional (3D) printed electrochemical immunosensor for the determination of Salmonella typhimurium in fresh chicken through a sandwich immunoassay employing biotinylated anti-S. typhimurium antibody followed by streptavidin labeled with Cd/Se ZnS quantum dots (QDs) is presented. The device features three carbon-black polylactic acid electrodes and a holder, and the quantification of S. typhimurium is performed by anodic stripping voltametric (ASV) determination of the Cd(II) released after acidic dissolution of the QDs. To enhance sensitivity, an electroplated bismuth film was deposited on the working electrode, achieving a detection limit of 5 cfu/mL in a total assay time of 25 min, whereas 5 h of sample pre-enrichment was required for the detection of 1 cfu/25 mL of chicken rinse and chicken broth. The method is accurate, with %recovery values ranging from 93.3 to 113% in fresh chicken samples, and repeatable with intra- and inter- assay coefficient of variations <2 and 5%, respectively, indicating the suitability of the proposed immunosensor for the detection of S. typhimurium at the point-of-need. Full article