Search Results (278)

Lubricant condition analysis is a valuable diagnostic tool for assessing engine performance and ensuring the reliable operation of diesel engines. While traditional diagnostic techniques—such as Fourier transform infrared spectroscopy (FTIR)—are constrained by slow response times, high costs, and the need for specialized personnel. In contrast, dielectric spectroscopy, impedance analysis, and soft computing offer real-time, non-destructive, and cost-effective alternatives. This review examines recent advances in integrating these techniques to predict lubricant properties, evaluate wear conditions, and optimize maintenance scheduling. In particular, dielectric and impedance spectroscopies offer insights into electrical properties linked to oil degradation, such as changes in viscosity and the presence of wear particles. When combined with soft computing algorithms, these methods enhance data analysis, reduce reliance on expert interpretation, and improve predictive accuracy. The review also addresses challenges—including complex data interpretation, limited sample sizes, and the necessity for robust models to manage variability in real-world operations. Future research directions emphasize miniaturization, expanding the range of detectable contaminants, and incorporating multi-modal artificial intelligence to further bolster system robustness. Collectively, these innovations signal a shift from reactive to predictive maintenance strategies, with the potential to reduce costs, minimize downtime, and enhance overall engine reliability. This comprehensive review provides valuable insights for researchers, engineers, and maintenance professionals dedicated to advancing diesel engine lubricant monitoring. Full article

Coatings that are tolerant of poor surface preparation are often used for rapid, real-time maintenance of aging steel surfaces. In this study, a modified epoxy (EP) anti-rust coating was proposed, utilizing methyl gallate (MG) as a rust conversion agent, graphene oxide (GO) as an active functional material, and epoxy resin as the film-forming material. The anti-rust mechanism was investigated using potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS), scanning electron microscopy (SEM), laser scanning confocal microscopy (LSCM), and the scanning vibration electrode technique (SVET). The results demonstrated that over a period of 21 days, the impedance of the coating increases while the corrosion current density decreases with prolonged soaking time. The coating exhibited a maximum impedance of 2259 kΩ, and a lower corrosion current density of 8.316 × 10⁻³ A/m², which demonstrated a three-order magnitude reduction compared to the corrosion current density observed in mild steel without coating. LSCM demonstrated that MG can not only penetrate the tiny gap between the rust particles, but also effectively convert harmful rust into a complex. SVET showed a much more uniform current density distribution in the micro-zones of mild steel with the anti-rust coating compared to uncoated mild steel, indicating that the presence of GO not only enhanced the electrical conductivity of the coating, but also improved the structure of the coating, which contributed to the high performance of the modified epoxy anti-rust coating. This work highlights the potential application of anti-rust coating in the protection of metal structures in coastal engineering. Full article

During the last few years, the requirements for highly efficient, sustainable, and versatile materials in modern biomedicine, aircraft and aerospace industries, automotive production, and electronic and electrical engineering applications have increased. This has led to the development of new and innovative methods for material modification and optimization. This can be achieved in many different ways, but one such approach is the application of surface thin films. They can be conductive (metallic), semi-conductive (metal-ceramic), or isolating (polymeric). Special emphasis is placed on applying semi-conductive thin films due to their unique properties, be it electrical, chemical, mechanical, or other. The particular thin films of interest are composite ones of the type of transition metal oxide (TMO) and transition metal nitride (TMN), due to their widespread configurations and applications. Regardless of the countless number of studies regarding the application of such films in the aforementioned industrial fields, some further possible investigations are necessary to find optimal solutions for modern problems in this topic. One such problem is the possibility of characterization of the applied thin films, not via textbook approaches, but through a simple, modern solution using their electrical properties. This can be achieved on the basis of measuring the films’ electrical impedance, since all different semi-conductive materials have different impedance values. However, this is a huge practical work that necessitates the collection of a large pool of data and needs to be based on well-established methods for both characterization and formation of the films. A thorough review on the topic of applying thin films using physical vapor deposition techniques (PVD) in the field of different modern applications, and the current results of such investigations are presented. Furthermore, current research regarding the possible methods for applying such films, and the specifics behind them, need to be summarized. Due to this, in the present work, the specifics of applying thin films using PVD methods and their expected structure and properties were evaluated. Special emphasis was paid to the electrical impedance spectroscopy (EIS) method, which is typically used for the investigation and characterization of electrical systems. This method has increased in popularity over the last few years, and its applicability in the characterization of electrical systems that include thin films formed using PVD methods was proven many times over. However, a still lingering question is the applicability of this method for backwards engineering of thin films. Currently, the EIS method is used in combination with traditional techniques such as X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), energy-dispersive X-ray spectroscopy (EDX), and others. There is, however, a potential to predict the structure and properties of thin films using purely a combination of EIS measurements and complex theoretical models. The current progress in the development of the EIS measurement method was described in the present work, and the trend is such that new theoretical models and new practical testing knowledge was obtained that help implement the method in the field of thin films characterization. Regardless of this progress, much more future work was found to be necessary, in particular, practical measurements (real data) of a large variety of films, in order to build the composition–structure–properties relationship. Full article

The proliferation of electromagnetic wave applications has accentuated electromagnetic pollution concerns, highlighting the critical importance of electromagnetic wave absorbers (EMA). This study proposes innovative I-Wrapped Package Lattice electromagnetic wave absorbers (IWP–EMA) based on the triply periodic minimal surface (TPMS) lattice structure. Through a rational design of porous gradient structures, broadband wave absorption was achieved while maintaining lightweight characteristics and mechanical robustness. The optimized three-dimensional configuration features a 20 mm thick gradient structure with a progressive relative density transition from 10% to 30%. Under normal incidence conditions, this gradient IWP–EMA basically achieves broadband absorption with a reflection loss below −10 dB across the 2–40 GHz frequency band, with absorption peaks below −19 dB, demonstrating good impedance-matching characteristics. Additionally, due to the complex interactions of electromagnetic waves within the structure, the proposed IWP–EMA achieves a wide-angle absorption range of 70° under Transverse Electric (TE) polarization and 70° under Transverse Magnetic (TM) polarization. The synergistic integration of the TPMS design and additive manufacturing technology employed in this study significantly expands the design space and application potential of electromagnetic absorption structures. Full article

Breast cancer remains one of the leading causes of mortality among women worldwide, highlighting the critical need for early and accurate detection methods. Traditional mammography, although widely used, has limitations, including radiation exposure and challenges in detecting early-stage lesions. Electrical Impedance Mammography (EIM) has emerged as a non-invasive and radiation-free alternative that assesses the density and electrical conductivity of breast tissue. EIM images consist of seven layers, each representing different tissue depths, offering a detailed representation of the breast structure. However, analyzing these layers individually can be redundant and complex, making it difficult to identify relevant features for lesion classification. To address this issue, advanced computational techniques are employed for image integration, such as the Root Mean Square ($C_{\mathrm{RMS}}$) Contrast and Contrast-Limited Adaptive Histogram Equalization (CLAHE), combined with the Coefficient of Variation (CV), CLAHE-based fusion, weighted average fusion, Gaussian pyramid fusion, and Wavelet–PCA fusion. Each method enhances the representation of tissue features, optimizing the image quality and diagnostic utility. This study evaluated the impact of these integration techniques on EIM image analysis, aiming to improve the accuracy and reliability of computational diagnostic models for breast cancer detection. According to the obtained results, the best performance was achieved using Wavelet–PCA fusion in combination with XGBoost as a classifier, yielding an accuracy rate of 89.5% and an F1-score of 81.5%. These results are highly encouraging for the further investigation of this topic. Full article

This paper presents a co-design approach for a microwave amplifier–phase shifter that integrates an arbitrary termination impedance reflection-type phase shifter as the input matching network of a microwave transistor. Since the proposed reflection-type phase shifter input matching network is capable of transforming both real and/or complex impedances to a system impedance of 50 Ω, the co-design approach can directly match the optimum source impedance of the microwave transistor to 50 Ω through a reflection-type phase shifter input matching network. To validate the proposed method, prototypes of microwave amplifier–phase shifters with different input matching networks configurations are designed, fabricated, and measured with a center frequency of 2.45 GHz. The experimental results demonstrate that the proposed co-design microwave amplifier–phase shifter achieves improved electrical performances compared to the conventional approach, where a 50-to-50 Ω termination impedance phase shifter is cascaded with a 50-to-50 Ω termination impedance conventional microwave amplifier. Measurement results demonstrate that the gains of a standalone conventional microwave amplifier, a cascaded phase shifter with a conventional microwave amplifier, and the proposed co-design microwave amplifier–phase shifter are 14.13 dB, 13.28 dB, and 13.74 dB, while the 1 dB compression points are 25.72 dBm, 24.77 dBm, and 25.26 dBm, respectively. Within the 200 MHz bandwidth, the proposed co-design microwave amplifier–phase shifter exhibits a maximum phase shift range of 185.62° and a phase deviation error of ±4.3°. The circuit size of the co-designed microwave amplifier–phase shifter is 38.5% smaller than the conventional cascaded phase shifter with a conventional microwave amplifier. Full article

Stimulus-responsive hydrogels have broad applications in the biomedical, sensing, and actuation fields. However, conventional fabrication methods are often limited to 2D structures, hindering the creation of complex, personalized 3D hydrogel architectures. Furthermore, hydrogels responding to only a single stimulus and delays in fabrication techniques restrict their practical utility in biomedicine. In this study, we developed two novel multi-stimuli-responsive hydrogels (based on Gelatin/Sodium Alginate and Tannic Acid/EDTA-FeNa complexes) specifically designed for direct ink writing (DIW) 3D printing. We systematically characterized the printed properties and optimized component ratio, revealing sufficient mechanical strength (e.g., tensile modulus: Gel/SA-TA ≥ 0.22854 ± 0.021 MPa and Gel/SA-TA@Fe³⁺ ≥ 0.35881 ± 0.021 MPa), high water content (e.g., water absorption rate Gel/SA-TA ≥ 70.21% ± 1.5% and Gel/SA-TA@Fe³⁺ ≥ 64.86% ± 1.28%), excellent biocompatibility (e.g., cell viability: Gel/SA-TA and Gel/SA-TA@Fe³⁺ ≥ 90%) and good shape memory performance (e.g., the highest shape recovery rate of Gel/SA-TA reaches 74.85% ± 4.776%). Furthermore, we explored electrical characteristics, showing that the impedance value of Gel/SA-TA@Fe³⁺ hydrogel changes significantly under finger bending and NIR irradiation. This investigation demonstrates the potential of these 3D-printed multi-stimuli hydrogels for applications such as wearable flexible strain sensors. Full article

This paper presents a comprehensive summary of recent advances in circuit topologies for piezoelectric energy harvesting, leading to self-powered systems (SPSs), covering the full-bridge rectifier (FBR) and half-bridge rectifier (HBR), AC-DC converters, and maximum power point tracking (MPPT) techniques. These approaches are analyzed with respect to their advantages, limitations, and overall impact on energy harvesting efficiency. Th work explores alternative methods that leverage phase shifting between voltage and current waveform components to enhance conversion performance. Additionally, it provides detailed insights into advanced design strategies, including adaptive power management algorithms, low-power control techniques, and complex impedance matching. The paper also addresses the fundamental principles and challenges of converting mechanical vibrations into electrical energy. Experimental results and performance metrics are reviewed, particularly in relation to hybrid approaches, load impedance, vibration frequency, and power conditioning requirements in energy harvesting systems. This review aims to provide researchers and engineers with a critical understanding of the current state of the art, key challenges, and emerging opportunities in piezoelectric energy harvesting. By examining recent developments, it offers valuable insights into optimizing interface circuit design for the development of efficient and self-sustaining piezoelectric energy harvesting systems. Full article

Terahertz metamaterial dual-band absorbers are used for multi-target detection and high-sensitivity sensing in complex environments by enhancing information that reflects differences in the measured substances. Traditional design processes are complex and time-consuming. Machine learning-based methods, such as neural networks and deep learning, require a large number of simulations to gather training samples. Existing design methods based on single-objective optimization often result in uneven multi-objective optimization, which restricts practical applications. In this study, we developed a metamaterial absorber featuring a circular split-ring resonator with four gaps nested in a “卍” structure and used the Multi-Objective Firefly Algorithm based on Multiple Cooperative Strategies to achieve fast optimization of the absorber’s structural parameters. A comparison revealed that our approach requires fewer iterations than the Multi-Objective Particle Swarm Optimization and reduces design time by nearly half. The absorber designed using this method exhibited two resonant peaks at 0.607 THz and 0.936 THz, with absorptivity exceeding 99%, indicating near-perfect absorption and quality factors of 31.42 and 30.08, respectively. Additionally, we validated the absorber’s wave-absorbing mechanism by applying impedance-matching theory. Finally, we elucidated the resonance-peak formation mechanism of the absorber based on the surface current and electric-field distribution at the resonance frequencies. These results confirmed that the proposed dual-band metamaterial absorber design is efficient, representing a significant step toward the development of metamaterial devices. Full article

Electric fields (EFs) are widely employed to promote tissue regeneration and accelerate wound healing. Despite extensive study, the cellular responses elicited by EFs are complex and not well understood. The present work focuses on cell migration—a process essential to organismal development, immune surveillance, and repair—and seeks to achieve its precise, closed-loop regulation. Effective control is impeded by (i) the nonlinear and stochastic nature of migratory dynamics and (ii) safety constraints that restrict the admissible EF magnitude. To address these challenges, we reformulate a neural network (NN) feedback controller previously developed for single-cell membrane-potential regulation and adapt it to guide population-level cell migration. A projection operator is embedded into the NN weight-update law to prevent maladaptive learning that arises when the control signal saturates at its EF limit. Numerical simulations confirm that the modified controller maintains accurate trajectory tracking under saturation and outperforms the original NN design. Finally, we demonstrate a proof-of-concept by implementing the controller in vitro to direct the electrotactic migration of naïve macrophages in 2D culture under a unidirectional EF. For the in vitro experiments, we compare performance to the standard proportional–integral–derivative (PID) controller. Full article

4H-silicon carbide (4H-SiC) is a cornerstone for next-generation optoelectronic and power devices owing to its unparalleled thermal, electrical, and optical properties. However, its chemical inertness and low dopant diffusivity for most dopants have historically impeded effective doping. This study unveils a transformative laser-assisted boron doping technique for n-type 4H-SiC, employing a pulsed Nd:YAG laser (λ = 1064 nm) with a liquid-phase boron precursor. By leveraging a heat-transfer model to optimize laser process parameters, we achieved dopant incorporation while preserving the crystalline integrity of the substrate. A novel optical characterization framework was developed to probe laser-induced alterations in the optical constants—refraction index ($\mathrm{n}$) and attenuation index ($\mathrm{k}$)—across the MIDIR spectrum (λ = 3–5 µm). The optical properties pre- and post-laser doping were measured using Fourier-transform infrared spectrometry, and the corresponding complex refraction indices were extracted by solving a coupled system of nonlinear equations derived from single- and multi-layer absorption models. These models accounted for the angular dependence in the incident beam, enabling a more accurate determination of $\mathrm{n}$ and $\mathrm{k}$ values than conventional normal-incidence methods. Our findings indicate the formation of a boron-acceptor energy level at 0.29 eV above the 4H-SiC valence band, which corresponds to λ = 4.3 µm. This impurity level modulated the optical response of 4H-SiC, revealing a reduction in the refraction index from 2.857 (as-received) to 2.485 (doped) at λ = 4.3 µm. Structural characterization using Raman spectroscopy confirmed the retention of crystalline integrity post-doping, while secondary ion mass spectrometry exhibited a peak boron concentration of 1.29 × 10¹⁹ cm⁻³ and a junction depth of 450 nm. The laser-fabricated p–n junction diode demonstrated a reverse-breakdown voltage of 1668 V. These results validate the efficacy of laser doping in enabling MIDIR tunability through optical modulation and functional device fabrication in 4H-SiC. The absorption models and doping methodology together offer a comprehensive platform for paving the way for transformative advances in optoelectronics and infrared materials engineering. Full article

Eddy current testing (ECT) has become a widely adopted technique for non-destructive testing (NDT) due to its effectiveness in detecting surface and near-surface defects in conductive materials. However, traditional methods mainly focus on defect detection and face significant challenges in extracting geometric information such as defect size and shape, which is crucial for structural health monitoring (SHM) and remaining useful life (RUL) assessment. To address these challenges, this study proposes a defect reconstruction approach based on a complex-valued convolutional neural network (CV-CNN), which directly leverages both amplitude and phase information inherent in complex-valued impedance signals. The proposed framework employs convolution, pooling, and activation operations specifically designed within the complex-valued domain to facilitate the high-fidelity reconstruction of defect morphology as well as precise multi-class defect classification. Notably, this approach processes the complete complex-valued signal without relying on prior structural parameters or baseline data, thereby achieving substantial improvements in both defect visualization and classification performance. Moreover, when compared to a complex-valued fully convolutional neural network (CV-FCNN), CV-CNN demonstrates a superior average classification accuracy of 85%, significantly outperforming the CV-FCNN model. Experimental results on carbon steel specimens with standard electrical discharge machining (EDM) notches under multi-frequency excitation confirm these advantages. This contribution provides a promising solution in the field of NDT for intelligent and precise defect detection. Full article

Glu-Phe-Asp (GFD) proteinoids represent a class of synthetic polypeptides capable of self-assembling into microspheres, fibres, or combinations thereof, with morphology dramatically influencing their electrical properties. Extended recordings and detailed waveforms demonstrate that microspheres generate rapid, nerve-like spikes, while fibres exhibit consistent and gradual variations in voltage. Mixed networks integrate multiple components to achieve a balanced output. Electrochemical measurements show clear differences. Microspheres have a low capacitance of $1.926\pm 5.735$$\mathsf{\mu }$F. They show high impedance at $6646.282\pm 178.664$ Ohm. Their resistance is low, measuring 15,830.739 ± 652.514 m$\mathsf{\Omega }$. This structure allows for quick ionic transport, leading to spiking behaviour. Fibres show high capacitance ($9.912\pm 0.171$$\mathsf{\mu }$F) and low impedance ($209.400\pm 0.286$ Ohm). They also have high resistance (163,067.613 ± 9253.064 m$\mathsf{\Omega }$). This combination helps with charge storage and slow potential changes. The 50:50 mixture shows middle values for all parameters. This confirms that hybrid electrical properties have emerged. The differences come from basic structural changes. Microspheres trap ions in small, round spaces. This allows for quick release. In contrast, fibers spread ions along their length. This leads to slower wave propagation. In mixed systems, diverse voltage zones emerge, suggesting cooperative dynamics between morphologies. This electrical polymorphism in simple proteinoid systems may explain complexity in biological systems. This study shows that structural polymorphism in GFD proteinoids affects their electrical properties. This finding is significant for biomimetic computing and sheds light on prebiotic information-processing systems. Full article

Electroencephalography (EEG) remains an essential method for monitoring brain activity, but the limitations of conventional systems due to the complexity of installation and lack of portability have led to the introduction and development of in-ear EEG technology. In-ear EEG is an emerging method of recording electrical activity in the brain and is an innovative concept that offers multiple advantages both from the point of view of the device itself, which is easily portable, and from the user’s point of view, who is more comfortable with it, even in long-term use. One of the fundamental components of this type of device is the electrodes used to capture the EEG signal. This innovative method allows bioelectrical signals to be captured through electrodes integrated into an earpiece, offering significant advantages in terms of comfort, portability, and accessibility. Recent studies have demonstrated that in-ear EEG can record signals qualitatively comparable to scalp EEG, with an optimized signal-to-noise ratio and improved electrode stability. Furthermore, this review provides a comparative synthesis of performance parameters such as signal-to-noise ratio (SNR), common-mode rejection ratio (CMRR), signal amplitude, and comfort, highlighting the strengths and limitations of in-ear EEG systems relative to conventional scalp EEG. This study also introduces a visual model outlining the stages of technological development for in-ear EEG, from initial research to clinical and commercial deployment. Particular attention is given to current innovations in electrode materials and design strategies aimed at balancing biocompatibility, signal fidelity, and anatomical adaptability. This article analyzes the evolution of EEG in the ear, briefly presents the comparative aspects of EEG—EEG in the ear from the perspective of the electrodes used, highlighting the advantages and challenges of using this new technology. It also discusses aspects related to the electrodes used in EEG in the ear: types of electrodes used in EEG in the ear, improvement of contact impedance, and adaptability to the anatomical variability of the ear canal. A comparative analysis of electrode performance in terms of signal quality, long-term stability, and compatibility with use in daily life was also performed. The integration of intra-auricular EEG in wearable devices opens new perspectives for clinical applications, including sleep monitoring, epilepsy diagnosis, and brain–computer interfaces. This study highlights the challenges and prospects in the development of in-ear EEG electrodes, with a focus on integration into wearable devices and the use of biocompatible materials to improve durability and enhance user comfort. Despite its considerable potential, the widespread deployment of in-ear EEG faces challenges such as anatomical variability of the ear canal, optimization of ergonomics, and reduction in motion artifacts. Future research aims to improve device design for long-term monitoring, integrate advanced signal processing algorithms, and explore applications in neurorehabilitation and early diagnosis of neurodegenerative diseases. Full article

To enhance the adaptability of grid partitioning under transient scenarios, this paper proposes a two-stage dynamic partitioning strategy based on structure–function coupling. Electrical coupling strength is first characterized using short-circuit impedance and the sensitivity between reactive power and voltage, while transient voltage correlation is incorporated through cosine similarity as edge weights in a graph model. Grid partitioning is then conducted by maximizing modularity through a staged approach that ensures network connectivity and automatically determines partition numbers. Case studies on the modified IEEE 39-bus system demonstrate that compared with transient voltage-based partitioning and conventional complex network methods, the proposed approach improves modularity by 69%, reduces the maximum post-fault voltage deviation by 38.6%, and achieves the highest regional decoupling rate. The result shows strong intra-regional cohesion and weak inter-regional connectivity, verifying the strategy’s effectiveness in enhancing adaptability and decoupling under transient conditions. Full article