Electromagnetic Interference Research Articles

MXene: A new revolution in the world of 2-D materials

MXenes have imposed a profound effect on materials science and nanotechnology fields after their discovery in 2011. Theoretical models have predicted more than 100 potential compositions of MXene whereas laboratory-scale synthesis reflects their success of over 40 distinct structures till date. The distinctive properties of MXenes have led to their use for a diverse range of applications, such as energy storage, environmental remediation, electronics, communications, gas and liquid separation and adsorption, biomedical fields, and optoelectronics. The increased interest of researchers in MXenes has led to a wide rise in research publications, showing their growing importance in different scientific domains. In 2024, MXenes had shown wide potential in various areas, including energy storage devices, electromagnetic interference shielding, nanocomposites, and hybrid materials. However, the variations in the choice of precursors, reactor design, cost, synthesis parameters pose several challenges in ensuring the production of high-quality MXenes. The applicability of MXenes continues to broaden as its compositions are continuously accelerating. This review aims is to provide a comprehensive overview of MXene history, its properties, challenges, latest trends, and different applications to highlight its potential and gather new audiences towards this family of two-dimensional materials.

A low-cost perfusion heating system for slice electrophysiology

AbstractTemperature-critical applications, such as patch-clamp electrophysiology, require constant perfusion at a fixed temperature. However, maintaining perfusate at a specific temperature throughout various applications requires heaters or coolers with integrated feedback systems, which has historically increased complexity and cost. This makes such systems prohibitively expensive in research environments with lower funding rates, particularly in developing countries. We developed a custom temperature control system that relies on off-the-shelf components and few custom parts, which can be easily produced with common tools. Our system can be built for less than $30 and maintains a set perfusate temperature within 0.4 °C while introducing negligible electrical interference. Using this system, we demonstrate that Striatal Medium Spiny Neurons exhibit increased membrane resistance, longer membrane time constants, lower firing rates, and increased rheobase current at room temperature compared to physiological temperature.

Comparative enhancement of H+ and OH− treatment on electromagnetic interference shielding in aligned and compact Ti3C2Tx MXene film

Abstract The pressing demand for ultrathin and flexible shields to counter electromagnetic interference (EMI) has sparked interest in Ti3C2Tx MXene materials due to their exceptional electrical conductivity, tunable surface chemistry, and layered structure. However, pure Ti3C2Tx MXene films often lack the mechanical properties required for practical engineering applications, and traditional reinforcement methods tend to reduce electrical conductivity. This work demonstrates an effective strategy to enhance the alignment and densely packed layered structure of Ti3C2Tx MXene films by regulating the acidity and alkalinity of Ti3C2Tx MXene aqueous solutions. This approach simultaneously improves mechanical strength and electromagnetic interference shielding effectiveness (EMI SE). Compared with original Ti3C2Tx MXene films, MXene films modified with ammonia solution (NH3·H2O) via OH− show a significant improvement in tensile strength (27.7 ± 1.9 MPa). Meanwhile, MXene films treated with hydrochloric acid (HCl) via H+ reach an even higher tensile strength of 39 ± 1.5 MPa. Moreover, the EMI SE values of the treated MXene films increase significantly, each reaching 66.2 and 58.4 dB. The maximum improvements in EMI SE values for the acid- and alkali-treated samples are 17.9% and 4%, respectively. In conclusion, the simultaneous enhancement of mechanical strength and EMI shielding efficacy highlights the potential of acid- and alkali-treated Ti3C2Tx MXene films for applications in ultrathin and flexible EMI shielding materials. Graphical abstract

A dual-band broadband absorber using frequency selective surface for satellite antenna.

A dual-band broadband absorber based on frequency selective surface (FSS) is proposed. This double-layer absorber consists of two layers of FSS structure; each layer has a unique construction and plays its wave-absorbing function in different frequency bands. In the low-frequency band, layer I is a transmission channel, and layer II effectively absorbs electromagnetic waves. Furthermore, layer I exhibits significant absorption characteristics in the high-frequency band, whereas layer II provides effective reflection. The combination of the two layers results in a remarkably effective absorption. Its reflectivity below - 10dB covers a bandwidth of 5.5-14.8GHz and 22.9-33.1GHz, and its fractional bandwidth is 128%. The structure prevents external electromagnetic interference from affecting the antenna's performance, ensuring communication quality in critical frequency bands while reducing unwanted absorption in unwanted frequency bands. It is worth mentioning that the structure still has good stability under 45° oblique incidence. The physical mechanism is analyzed in detail by using an equivalent circuit model (ECM). The measurement results are in good agreement with those of simulation and ECM.

Temperature stability analysis of the all-fiber current sensor with a loop structure

Abstract All-fiber optical current sensor (AFOCS) is a perfect product formed by the combination of fiber-optic sensing technology and the Faraday effect. It boasts significant advantages such as resistance to electromagnetic interference, a high measurement dynamic range and precision, low power consumption, low cost, and insulation. Additionally, it enables long-distance transmission, making it one of the main devices for current monitoring in smart grids. However, the birefringence within the sensing fiber is highly susceptible to changes in temperature, causing the polarization state of light to be extremely sensitive to temperature variations. This sensitivity significantly impacts the accuracy of current measurements. A novel loop structure AFOCS with a coupled fiber polarization rotator (FPR) is introduced in this paper. The operating principle of this structure is theoretically analyzed, and the Jones matrix is used to analyze the output light intensity signal and derive the error formula. After comparing the measurement accuracy of the basic AFOCS and the loop structure AFOCS, it is demonstrated that this novel structure can improve current sensitivity by enhancing the temperature robustness of the system. Additionally, the error generated by the FPR is controlled below 1%, meeting the requirements for stable system operation. Therefore, this novel structure effectively improves the accuracy of current measurement and exhibits strong temperature stability.

Abstract 4119167: Electromagnetic Interference from Electric Vehicles: Implications for Subcutaneous Implantable Cardioverter-Defibrillators

Background: Electromagnetic interference (EMI) refers to signals discernible by a device’s circuitry, potentially resulting in erroneous sensing, pacing, mode switching and defibrillation. EMI poses potential risks to the functionality of cardiac implantable electronic devices, with Subcutaneous- Implantable Cardioverter-Defibrillators (S-ICDs) potentially more susceptible due to their design. Research Question: Does EMI from Electric Vehicles (EV) interfere with the functionality of S-ICDs? Goals: Developing and utilizing a loop antenna device that mimics S-ICDs to assess the susceptibility of S-ICDs to EMI from EVs in different charging conditions. Methods: A loop antenna device mimicking the S-ICD lead design was developed, and then validated by the Food and Drug Administration, to detect electromagnetic signals. The device was positioned at different locations in a Tesla Model 3 to measure electromagnetic signals. Results: Statistical analysis revealed a significant difference (p-value < 0.0001) in detected voltage (400-504 mV) around the cup holder compared to all other measured locations. These signals fell within the R-Wave Spectrum of 30-300 Hz. Conclusion: We identified EV-emitted EMI capable of disrupting S-ICD functionality. Further in-vivo studies are essential to determine the level of interference between S-ICDs and EMI from EVs.

High entropy alloy reinforcement for superior electromagnetic interference shielding performance in carbon fiber‐reinforced polymer composites

AbstractThis study explores the enhancement of electromagnetic interference (EMI) shielding effectiveness (SE) in carbon fiber‐reinforced polymer (CFRP) composites through the integration of equatomic CoCuFeNi high entropy alloy (HEA) particles. Employing mechanical alloying (MA), CoCuFeNi HEA powders were synthesized, revealing a face‐centered cubic structure with crystallite and particle sizes of 14.7 nm and 11.62 μm, respectively. The integration of these HEA particles at concentrations of 5%, 10%, and 15% by weight into epoxy resin, followed by the fabrication of composites using the hand lay‐up technique. Detailed structural analysis of HEA particles confirmed the successful synthesis of equatomic HEAs via MA. Structural analysis of the HEA integrated composites revealed vacancy regions at 5% concentration, a uniform distribution at 10%, and particle agglomeration causing inhomogeneity and vacancies at 15%. The composites demonstrated significant improvements in EMI SE, with the 10% HEA sample showing superior performance compared to the other samples. Specifically, the 10% HEA composite achieved a peak SE of 73.09 dB at 4.72 GHz, attributed to the optimized distribution of HEA particles that enhanced electrical conductivity and reflective properties.Highlights CoCuFeNi HEA particles were successfully synthesized via MA. HEA particles were added to epoxy at 5, 10, and 15 wt% for composite fabrication. Voids were observed in HEA5, uniformity in HEA10, and clustering in HEA15. EMI shielding was assessed using VNA, SE, dielectric permittivity, and magnetic permeability. The HEA10 composite achieved peak EMI shielding, 73.09 dB at 4.72 GHz.

Enhancing Epilepsy Seizure Detection Through Advanced EEG Preprocessing Techniques and Peak-to-Peak Amplitude Fluctuation Analysis

Objectives: Naturally, there are several challenges, such as muscular artifacts, ocular movements and electrical interferences that depend on precise diagnosis and classification, which hamper exact epileptic seizure detection. This study has been conducted to improve seizure detection accuracy in epilepsy patients using an advanced preprocessing technique that could remove such noxious artifacts. Methods: In the frame of this paper, the core tool in the area of epilepsy, EEG, will be applied to record and analyze the electrical patterns of the brain. The dataset includes recordings of seven epilepsy patients taken by the Unit of Neurology and Neurophysiology, University of Siena. The preprocessing techniques employed include advanced artifact removal and signal enhancement methods. We introduced Peak-to-Peak Amplitude Fluctuation (PPAF) to assess amplitude variability within Event-Related Potential (ERP) waveforms. This approach was applied to data from patients experiencing 3–5 seizures, categorized into three distinct groups. Results: The results indicated that the frontal and parietal regions, particularly the electrode areas Cz, Pz and Fp2, are the main contributors to epileptic seizures. Additionally, the implementation of the PPAF metric enhanced the effectiveness of seizure detection and classification algorithms, achieving accuracy rates of 99%, 98% and 95% for datasets with three, four and five seizures, respectively. Conclusions: The present research extends the epilepsy diagnosis with clues on brain activity during seizures and further demonstrates the effectiveness of advanced preprocessing techniques. The introduction of PPAF as a metric could have promising potential in improving both the accuracy and reliability of epilepsy seizure detection algorithms. These observations provide important implications for control and treatment both in focal and in generalized epilepsy.

High-Frequency Modeling and Analysis of Single-Layer NiZn Ferrite Inductors for EMI Filtering in Power Electronics Applications

In the high-frequency (HF) region, specifically within the 150 kHz to 30 MHz range for conducting electromagnetic interference (EMI) modeling, NiZn inductors exhibit enhanced efficiency due to their low core losses and stable permeability. Consequently, the accurate modeling of NiZn ferrite toroidal inductors is essential, given their widespread applications in the HF domain, with the aim of addressing existing knowledge gaps. Previous inductor models often relied on the perfect electric conductor (PEC) assumption, which simplifies the analysis but does not fully represent the electromagnetic behavior of the cores to which the PEC assumption cannot be applied. This study investigates the actual electromagnetic behavior of NiZn cores, treating them as dielectrics, which diverges from the traditional PEC-based models. Furthermore, this research fully considers the actual geometry of the inductors, proposing a comprehensive and precise analytical model for NiZn ferrite toroidal inductors. The impact of various winding methods on core capacitance is also explored. The paper provides a detailed explanation of the physical significance underlying the proposed model. A comparative analysis of both modeling methods is presented, and the efficacy of the suggested approach is validated through simulations and experimental results in several distinct scenarios.

Acoustic analysis of a three-dimensional cylindrical shell model under electromagnetic vibration

This paper presents the acoustic analysis of a three-dimensional cylindrical shell model under electromagnetic vibration, a critical factor affecting the performance of electric motors in various applications such as automotive, aerospace, and industrial systems. The study provides a multidisciplinary approach that integrates electromagnetics, structural vibration, and acoustics, solved using the fast multipole boundary element method (FMBEM). The results summarize the validation of the analytical models and numerical simulations, offering insights into effective vibration reduction methods. The conclusions indicate that the 3-D numerical analysis using FMBEM aligns well with the analytical solution for the sound pressure in the exterior acoustic domain of the cylindrical shell model. The paper contributes valuable insights for the design of low-noise motors and the control of electromagnetic vibration and noise in electric motors.

All-Polymer Composite Film with Outstanding Mechanical, Thermal Conductive, and EMI Shielding Performances.

Robust polymer-based composites with high thermal conductivity (TC) and electromagnetic interference shielding effectiveness (EMI SE) hold great promise for applications in rapidly developing electronics. Traditional composites containing inorganic fillers often suffer from increases in processing difficulty, density, and significant deterioration in mechanical properties. Herein, we report for the first time a flexible multilayered all-polymer composite of poly(p-phenyl-2,6-phenylene bisoxazole) (PBO) and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS), featuring excellent mechanical strength of 203.9 MPa, in-plane TC of 21.9 W/mK, EMI shielding effectiveness (SE) of 44.2 dB, high-temperature stability, and flame retardancy. The excellent TC, mechanical strength, and flame retardancy of PBO, the remarkable EMI shielding performance of PEDOT:PSS, and the π-π stacking interactions between adjacent layers contribute to the comprehensive properties, which outperform most of the traditional composites of polymers and inorganic fillers reported so far. This full-polymer design may pioneer a new strategy for the preparation of robust and lightweight multifunctional composites.

A Radio Frequency Interference Screening Framework—From Quick-Look Detection Using Statistics-Assisted Network to Raw Echo Tracing

Synthetic aperture radar (SAR) is often affected by other high-power electromagnetic devices during ground observation, which causes unintentional radio frequency interference (RFI) with the acquired echo, bringing adverse effects into data processing and image interpretation. When faced with the task of screening massive SAR data, there is an urgent need for the global perception and detection of interference. The existing RFI detection method usually only uses a single type of data for detection, ignoring the information association between the data at all levels of the real SAR product, resulting in some computational redundancy. Meanwhile, current deep learning-based algorithms are often unable to locate the range of RFI coverage in the azimuth direction. Therefore, a novel RFI processing framework from quick-looks to single-look complex (SLC) data and then to raw echo is proposed. We take the data of Sentinel-1 terrain observation with progressive scan (TOPS) mode as an example. By combining the statistics-assisted network with the sliding-window algorithm and the error-tolerant training strategy, it is possible to accurately detect and locate RFI in the quick looks of an SLC product. Then, through the analysis of the TOPSAR imaging principle, the position of the RFI in the SLC image is preliminarily confirmed. The possible distribution of the RFI in the corresponding raw echo is further inferred, which is one of the first attempts to use spaceborne SAR data to ‌elucidate the RFI location mapping relationship between image data and raw echo. Compared with directly detecting all of the SLC data, the time for the proposed framework to determine the RFI distribution in the SLC data can be shortened by 53.526%. All the research in this paper is conducted on Sentinel-1 real data, which verify the feasibility and effectiveness of the proposed framework for radio frequency signals monitoring in advanced spaceborne SAR systems.

Leveraging machine learning for the detection of structured interference in Global Navigation Satellite Systems

Radio frequency interference disrupts services offered by Global Navigation Satellite Systems (GNSS). Spoofing is the transmission of structured interference signals intended to deceive GNSS location and timing services. The identification of spoofing is vital, especially for safety-of-life aviation services, since the receiver is unaware of counterfeit signals. Although numerous spoofing detection and mitigation techniques have been developed, spoofing attacks are becoming more sophisticated, limiting most of these methods. This study explores the application of machine learning techniques for discerning authentic signals from counterfeit ones. The investigation particularly focuses on the secure code estimation and replay (SCER) spoofing attack, one of the most challenging type of spoofing attacks, ds8 scenario of the Texas Spoofing Test Battery (TEXBAT) dataset. The proposed framework uses tracking data from delay lock loop correlators as intrinsic features to train four distinct machine learning (ML) models: logistic regression, support vector machines (SVM) classifier, K-nearest neighbors (KNN), and decision tree. The models are trained employing a random six-fold cross-validation methodology. It can be observed that both logistic regression and SVM can detect spoofing with a mean F1-score of 94%. However, logistic regression provides 165dB gain in terms of time efficiency as compared to SVM and 3 better than decision tree-based classifier. These performance metrics as well as receiver operating characteristic curve analysis make logistic regression the desirable approach for identifying SCER structured interference.

Advancing Electrical Engineering with Biomass-derived Carbon Materials: Applications, Innovations, and Future Directions.

The ongoing global shift towards sustainability in electrical engineering necessitates novel materials that offer both ecological and technical benefits. Biomass-derived carbon materials (BCMs) are emerging as cornerstones in this transition due to their sustainability, cost-effectiveness, and versatile properties. This review explores the expansive role of BCMs across various electrical engineering applications, emphasizing their transformative impact and potential in fostering a sustainable technological ecosystem. The fundamentals of BCMs are investigated, including their unique structures, diverse synthesis procedures, and significant electrical and electrochemical properties. A detailed examination of recent innovations in BCM applications for energy storage, such as batteries and supercapacitors, and their pivotal role in developing advanced electronic components like sensors, detectors, and electromagnetic interference shielding composites has been covered. BCMs offer superior electrical conductivities, tunable surface chemistries, and mechanical properties compared to traditional carbon sources. These can be further enhanced through innovative doping and functionalization techniques. Moreover, this review identifies challenges related to scalability and uniformity in properties and proposes future research directions to overcome these hurdles. By integrating insights from recent studies with a forward-looking perspective, this paper sets the stage for the next generation of electrical engineering solutions powered by biomass-derived materials, aligning technological advancement with environmental stewardship.

Enhanced Mechanical and Multifunctional Properties of GNPs/CNTs Hybridized PLA Nanocomposites by Implementing Dual‐Processing of Pickering Emulsion‐Melt Blending Methods

AbstractPolylactic acid (PLA) composites with multifunctional properties and minimal filler content are increasingly in demand across various industries. However, achieving a balance between high mechanical strength, electrical conductivity, thermal conductivity, and electromagnetic interference (EMI) shielding remains challenging. In this study, a dual‐processing strategy combining Pickering emulsion templating and melt blending is presented to hybridize 1D carbon nanotubes (CNTs) and 2D graphene nanoplatelets (GNPs) within a PLA matrix. This approach successfully forms a stable dual‐filler network, ensuring uniform dispersion of the fillers. The results show that this method significantly enhances the performance of the resulting PM‐PGxCy composites (P: Pickering emulsion; M: melt blending; x and y: mass fractions of GNPs and CNTs, respectively). Specifically, the PM‐PG1.43C1.43 composite exhibits remarkable improvements in mechanical strength (56.2 MPa), electrical conductivity (43.5 S m−1), EMI shielding effectiveness (20.1 dB), and thermal conductivity (0.34 W m·K−1), outperforming composites prepared using either method alone. These findings indicate that the dual‐processing strategy effectively combines 1D and 2D fillers, facilitating superior interfacial interactions and enhancing the multifunctional properties of PLA‐based composites. This study offers a new approach to achieving high‐performance PLA composites with low filler content, offering significant potential for applications in electronics, packaging, and EMI shielding technologies.

High Entropy Ceramics for Electromagnetic Functional Materials

AbstractMicrowave absorbing materials play an increasingly important role in modern electronic warfare technology for enhancing electromagnetic compatibility and suppressing electromagnetic interference. High‐entropy ceramics (HECs) possess extraordinary physical and chemical properties, and more importantly, the high tunability of multi‐component HECs has brought new opportunities to microwave absorbing materials. Rich crystallographic distortions and multi‐component occupancies enable HECs to have highly efficient microwave absorption properties, excellent mechanical properties, and thermal stability. Therefore, the structural advantages of HECs are integrated from comprehensive perspectives, emphasizing on the role of dielectric and magnetic properties in the absorption phenomenon. Strategies are proposed to improve the microwave absorption capacity of HECs, including composition optimization, microstructure engineering, and post‐treatment technology. Finally, the problems and obstacles associated with high‐entropy materials (HEMs) research are discussed. The innovative design concepts of high‐entropy microwave absorbing ceramics are highlighted.

Multifunctional-structural integrated wood cellulose‑based composite with Magpie’s Nest-shaped enhance flame retardancy and electromagnetic interference shielding

The construction of multifunctional hydrophobic wood with electromagnetic interference shielding effectiveness had attracted great interest, at the same time, the problem of wood flammability must also be solved. In this study, the effectiveness of DOPO-based P and Zn complexes in improving wood flame retardancy and electromagnetic interference shielding effectiveness, and the outstanding contribution of lignocellulose skeleton and multi-interface to shielding effectiveness was demonstrated. DOPO-based P and Zn complexes with “Needle-punched” structure and much functional groups were loaded on the wood surface, and then micro-nano metal Ni particles were deposited. Through the optimization of the wood surface and itself structure, and the construction of a complete conductive network. Wood/ZnP/Ni composites electromagnetic interference shielding effectiveness couold reach 65.04 dB in the X-band. It was worth noting that the lignocellulose skeleton prepared by the top-down method and multi-interface structure increase electromagnetic interference shielding effectiveness by 10.71 % and 33.89 %, respectively. In addition, the synergistic effect of DOPO-based P and Zn complexes and the introduction of PDMS layer improved the flame retardancy (limiting oxygen index was 35.3 %) and hydrophobicity (contact angle was 133.07°), which avoided the wood poor durability problem in use. There was no doubt that it provided a new strategy for the preparation of novel materials with low-cost and extremely excellent electromagnetic interference shielding properties.

Optimization of Electromagnetic-Wave Reflectivity Performance of Electromagnetic Shielding Yarn Prepared by Evaporation-Induced Sintering of Ga60.5In25Sn13Zn1.5 Alloy with Nanosilicates.

Electromagnetic interference (EMI) shielding textiles have received widespread attention, and liquid metal (LM) shows superiority in flexible and deformable electronics. Here, we introduce a novel method using nanosilicates to help sinter LM through capillary evaporation, resulting in strong adhesion to substrates. By adjustment of the amount of nanosilicates, flexible EMI shielding yarns are created using dip-coating and curing processes. The sintered LM tightly adhered to the undulating and uneven surfaces of polyurethane (PU) yarns. The as-fabricated ION/LM@PU ("ION" is abbreviation of "ionogel") has strong EMI shielding and low EM-wave reflection due to the high electrical conductivity of the LM layer and good impedance matching of P(AAm-co-AA) ionogel. The addition of an ionogel enhances EM-wave absorption and strengthens interfacial polarization, making it an effective green EMI shielding yarn for reducing secondary reflection pollution. ION/LM@PU exhibited high total electromagnetic interference shielding effectiveness (EMI SET) (∼56 dB), low reflected power coefficient (R) (∼0.153), high impedance matching (|Zin/Z0| ≈ 0.660), high tensile strength (∼23.75 MPa), and high elastic recovery (∼0.92 at 10th stretch-release cycle). The EMI shielding mechanism of ION/LM@PU ± 60° is composed of reflective loss and absorption loss (including multireflective loss, conduction loss, dipole polarization loss, and interfacial polarization loss).

“Sandwich Structured” Composite Film with Double Barrier Radiative Cooling, Adjustable Heating, and Multi-reflective Electromagnetic Interference Shielding for All-Weather Protection

“Sandwich Structured” Composite Film with Double Barrier Radiative Cooling, Adjustable Heating, and Multi-reflective Electromagnetic Interference Shielding for All-Weather Protection

Research on path planning in UAV-assisted emergency communication

The rapid development of UAV communication technology makes it have application potential in wireless systems. However, for the optimization problem of UAV base station providing communication for ground personnel in the disaster area under special natural disasters, traditional deep reinforcement learning is used. Algorithms cannot solve such problems well. This article proposes the AD3QN algorithm combined with the attention mechanism, which can communicate with disaster victims on the ground better and more quickly, providing better information collection for rescue missions and completing the task of communication optimization. In the mission simulation environment, in order to make the environment more realistic, we innovatively designed the ground user distribution model, air-to-ground communication model, and electromagnetic interference model. Finally, the effect of the proposed algorithm is evaluated by comparing different deep reinforcement learning algorithms. The results show that the algorithm we proposed can provide communication services faster and has higher practicability in terms of algorithm.