IET Nanodielectrics最新文献

In this study, various concentrations of high-temperature vulcanised silicone rubber composites filled with nano/micro silica and alumina were manufactured. In this work, all test specimens were subjected to a variety of environmental stresses as well as DC voltage for 5000 h. Then, different diagnostic methods were used to look at the changes that happened on their surfaces and in their bulk properties. These included hydrophobicity classification, X-ray photoelectron spectroscopy (XPS) analysis, Fourier transform infrared spectroscopy (FTIR) analysis, thermogravimetric analysis (TGA) analysis, leakage current analysis and mechanical strength analysis. The composite with 2% nano silica and 10% micro alumina had the smoothest surface and the best hydrophobicity (HC-3). It also had the lowest leakage current (3.1 μA), the least amount of strength loss (31.3%), and good thermal stability compared to the other samples that were studied. Aged samples show a considerable increase in the concentration of the O element and a significant drop in the proportion of the Si component relative to the virgin specimen, which points to the oxidation of chemical bonds during HTV SR and their composites during ageing but with different concentrations. However, two samples (SP2 and SP3) showed comparatively lower concentrations of oxygen degradation in Si contents. This can be attributed to the strong molecular interaction between the fillers and the base matrix.

In this study, a magnetic disk was prepared using nanoparticles with a diameter of less than 15 nm. The morphological and structural characteristics of these nanoparticles were systematically examined using X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and alternating force gradient magnetometry (AGFM). XRD analysis confirmed that the average diameter of the copper–magnesium ferrite nanoparticles doped with cadmium was approximately 12 nm, consistent with TEM results, which also showed uniform particle distribution and a tendency to form clusters in powdered form. AGFM measurements revealed that the magnetic property of the powder sample was 15.83 emu/g, which increased to 22.70 emu/g after compression, highlighting the influence of particle density and morphology on magnetic behaviour. Gas sensing tests demonstrated that the fabricated sensors achieved exceptional sensitivity, particularly to acetonitrile, with a maximum sensitivity of 92.3%. A hybrid deep learning model, Bi-LSTM, was utilised to enhance the precision of gas classification. The proposed methodology was benchmarked against traditional machine learning models, including LSTM and RNN, and demonstrated superior performance. The accuracy of gas detection reached an impressive 99.89%, as validated by ROC analysis, underscoring the efficacy of the deep learning-based approach. These findings highlight the potential of cadmium-doped ferrite nanoparticles for high-performance gas sensing applications, suitable for both industrial and medical uses.

Fuel adulteration involving the illicit mixing of substances such as kerosene and diesel with petrol poses significant risks to engine performance, environmental safety and consumer health. This paper presents a novel HC-PCF sensor designed to accurately detect and identify adulterants in petroleum-based fuels with unprecedented sensitivity and selectivity. The proposed HC-PCF sensor features a unique circular core structure surrounded by a carefully engineered square cladding region, enabling highly sensitive detection of refractive index changes caused by the presence of adulterants. Through rigorous numerical simulations and optimisation, our design achieves remarkable maximum relative sensitivities of 98.56%, 98.95%, and 99.32% for petrol, kerosene, and diesel, respectively, outperforming many previously reported techniques. A comprehensive analysis of the sensor's performance reveals an ultra-low confinement loss of 4.08 × 10⁻¹⁰ dB/m, 1.08 × 10⁻¹³ dB/m, and 2.95 × 10⁻¹² dB/m and effective material loss of 0.0040 cm⁻¹, 0.0036 cm⁻¹, and 0.0034 cm⁻¹, highlighting its exceptional light-guiding capabilities and sensitivity. The sensor's high responsiveness facilitates the detection of even trace levels of adulterants by capturing minute refractive index variations as low as possible, enabling real-time monitoring and timely intervention in adulteration incidents. The proposed HC-PCF sensor exhibits high selectivity, precisely targeting the refractive index signatures of fuels, ensuring accurate detection even in complex chemical environments. Its compact size and robust design make it suitable for deployment in various fuel quality control applications, from transportation to industrial settings. Overall, this work introduces cutting-edge HC-PCF sensor technology that addresses the critical need for reliable fuel adulteration detection with unparalleled sensitivity and selectivity, contributing to enhanced product quality, consumer protection, and environmental sustainability in the energy sector.

Water diffusion significantly contributes to the abnormal heating in composite insulators. At the same time, the material performance shortcomings caused by the composite insulator production process can further increase water diffusion. To optimise the production process of composite insulators to prevent or reduce such abnormal heating, the fillers of the composite insulator sheath and the production of glass-fibre-reinforced plastic (GRP) rods are analysed. Based on the water absorption model of the sheath and the fibre infiltration model of the rod, it is identified that the flame retardant particle size is a factor that affects the water absorption of the sheath, whereas the epoxy resin liquid viscosity and the fibre volume fraction are factors that affect the porosity of the GRP rod. The experimental results show that the use of modified large-particle-size flame retardants can reduce the water absorption rate of the sheath, whereas low-viscosity liquids and high-fibre volume fractions can reduce the porosity of the GRP rod. The sheath and GRP rod produced using the optimised process continue to meet the standards.

Metal–organic frameworks (MOFs), as a new type of porous crystalline materials, hold great promise for gas purification in electrical insulation equipment, especially for the selective capture of SO₂ produced from the decomposition of SF₆. However, challenges remain in the design and synthesis of MOF-based adsorbents with salient SO₂ adsorption performance due to the limited effective interactions with SO₂ molecules. This study reports the successful direct synthesis of two-dimensional (2D) MOFs nanosheets coordinated by Zr⁴⁺ and tridentate carboxylic organic ligands with nitrogen-containing electron-rich groups within the organic ligands. The resulting MOF nanosheets exhibit enhanced interactions with SO₂ molecules due to the spatial location and electronic properties of the nitrogen groups, thereby conducing to the adsorption of SO₂. Additionally, by precisely controlling the positioning of nitrogen groups, the Zr-BTB-NH₂ (BTB-NH₂: 1,1′:3′,1″-Terphenyl]-4,4″-dicarboxylic acid, 3,3″-diamino-5′-(3-amino-4-carboxyphenyl) and Zr-TPY (TPY: 4′-(4-carboxyphenyl)-[2,2′:6′,2″-terpyridine]-5,5″-dicarboxylic acid) MOFs synthesised in this study achieve differentiated SO₂ adsorption capacities of 39.3 cm³·g⁻¹ and 66.3 cm³·g⁻¹, respectively, surpassing those of several previously reported MOFs. This strategy provides a novel design strategy for developing efficient SO₂ absorbents and lays a foundation for the further development of absorbents promising for gas remediation in electrical insulation equipment.

Optical sensors getting widespread usage in almost every field, especially industries. A high thermal optical sensor is proposed to predict the environmental temperature in power plants. A high sensitivity, accuracy, low cost, compact size, linear operation, and suitable transmission coefficient optical sensor in a wide thermal range is achieved that changing the surrounding temperature has a severe effect on the conductivity of graphene which changes the transmission power of the sensor. In this paper, the position of the graphene sheet affects the interaction of light and graphene, effectively. A maximum sensitivity of 17.47% is achieved in $��500�$ K temperature deviation. The Cylindrical diameter is 4.1 lambda, the smallest in size among the references checked and has the desired performance up to 2000 K, indicating the suitable efficiency of the sensor and the fibre optic sensor with graphene coating has great potential in the field of measurement, especially in the temperature of the surrounding atmosphere in the industry.

The exact thermal evaluation of distribution transformers (DTs), which are critical and costly pieces of equipment for the power grids, may contribute to preventing the respective failures. Therefore, the present study non-uniformly investigated DT for correct anticipation of hotspot temperature (HST). Optical fibre sensors (OFSs) were applied for assessing our newly developed non-uniform 3D computational fluid dynamic (CFD)-based modelling while performing the temperature rise test (TRT). It should be noted that this new 3D CFD-based thermal analysis showed an error percentage of 0.11% (0.1°C) in comparison to the OFS measurement, reflecting the ideal efficiency and accuracy of the model. Moreover, thermography for both top-oil temperature (TOT) and bottom-oil temperature (BOT) was employed to validate the results from non-uniform 3D (three-dimensional) CFD-based thermal evaluations. The results indicated an acceptable level of relationship between thermography and thermal analysis of 3D CFD at the specified two spots, with an error percentage of <0.65%, demonstrating the acceptable accuracy of the new non-uniform 3D CFD-based model. In the following, yet importantly, the new non-uniform 3D model was subjected to the total harmonic distortions (THD) for the current and voltage of 5%, 10%, and 15%, which raised the HST more than the original model without harmonics by 3.3°C, 7.1°C, and 10.3°C, respectively. Ultimately, different mineral oil-based nanofluids’, such as multi-walled carbon nanotubes (MWCNTs) and diamond nanoparticles, influence on the HST decrement of DT in simultaneous current and voltage harmonics was investigated.

Transformer health analysis using Dissolved Gas Analysis is crucial for diagnosing power transformer faults. This paper proposes an innovative approach to diagnose power transformer faults by integrating machine learning algorithms with Ensemble techniques. The method involves fusing reduced dimensional input features through Principal Component Analysis with Ensemble techniques such as Bagging, Decorate, and Boosting. Various machine learning algorithms, including Decision Tree (DT), K-Nearest Neighbours, Radial Basis Function Network, and Support Vector Machine, are employed in conjunction with Ensemble techniques. The long short-term memory algorithm was used to create synthetic data to solve the issue of data imbalance. A dataset of 683 samples is used in the study for training, testing, validation, and comparison with current techniques. The results highlight the effectiveness of Ensemble techniques, particularly Boosting, which demonstrates superior performance across all classification algorithms. The Boosting with DT algorithm achieves an impressive accuracy of 98.32%, surpassing alternative methods. In validation, the proposed Boosting Ensemble technique outperforms various approaches, showcasing its diagnostic accuracy and superiority over alternative methods. The research emphasises the model's effectiveness in smoothing input vectors, enhancing harmony with ensemble techniques, and overcoming limitations in prior methods.

The detection of human diseases is a major application for biosensors. During this work, a 2D photonic crystal fibre (PCF) biosensor design for dengue virus detection has been suggested. This work presents the decagonal hollow core PCF-based dengue virus bio-sensor and quantitatively investigates it over the terahertz regime. The suggested biosensor's performance is assessed using COMSOL Multiphysics, a professional tool that uses the Finite Element Method. The simulation's outcomes show that the suggested sensor performed better than earlier research, with a high sensitivity of 98.79%, 97.96%, 97.71%, 98.58%, 96.99% and 97.47% with more less confinement loss 1.2766 × 10⁻¹² dB/m, 1.6385 × 10⁻¹² dB/m, 2.8015 × 10⁻¹³ dB/m, 1.1798 × 10⁻¹³ dB/m, 7.0336 × 10⁻¹² dB/m and 0.00 dB/m respectively for infected Haemoglobin (Hgb), Normal Haemoglobin (Hgb), Infected Platelets (Plt), Normal Platelets (Plt), Infected Plasma (Psm) and Normal Plasma (Psm) at 3.0 THz using the ideal geometric configuration. Very soon, its remarkable sensitivity and guiding capabilities will be crucial to dengue virus detection technology.

This research study investigates the influence of various nanoparticles on the dielectric breakdown voltage, oil dissipation factor, viscosity, and thermal conductivity of nanofluids. Nanofluids were prepared using synthetic ester oil as the base fluid, and three nanoparticles, silicon carbide (SiC), boron nitride (BN), and zirconium dioxide (ZrO₂), were added at different concentrations (0.125 wt%, 0.250 wt%, and 0.375 wt%), which are basically the nano-sized powder that can be blended in the oil. The dielectric breakdown voltage testing was conducted to evaluate the electrical performance of the nanofluids. Additionally, rheological measurements were performed to study the kinematic viscosity, while thermal conductivity was determined using appropriate techniques. The enhancements in each property were evaluated and compared for the different nanoparticle concentrations and types. Previous studies focused only on the investigation of the electrical properties of nanofluids. However, in the present study, the electrical as well as thermo-physical characterisation of nanofluids is performed and analysed as they directly affect the cooling performance of transformers. The results provide dielectric and thermo-physical characterisation that exhibit excellent insulation and cooling functionalities and valuable insights into the potential applications of nanofluids as dielectrics in various high-voltage electrical equipment. ZrO₂ and SiC nanoparticles exhibited a reduction in the oil dissipation factor. SiC consistently improved breakdown voltage (Bdv), while ZrO₂ nanoparticles showed concentration-dependent effects, enhancing Bdv at low concentrations but degrading it at higher ones. Unexpectedly, nanoparticle dispersion and lubrication effects can lead to viscosity reductions, countering conventional expectations. Surprisingly, at the highest concentration, the thermal conductivity decreases compared to the lower nano-concentrations in synthetic ester oil.