Specific Frequency Bands Research Articles

An adversarial-based domain generalization method for the health evaluation of axial piston pumps

Abstract The axial piston pump is the power component in hydraulic systems and evaluating its health status is of great importance to the safe operation of hydraulic systems. Discharge pressure signals are common monitoring signals for axial piston pumps, but it is difficult to obtain satisfactory health evaluation results by directly using raw discharge pressure signals since the degradation information lies in some specific frequency bands. Furthermore, the axial piston pump often operates under different operating conditions and most existing deep learning models have low diagnostic accuracies due to the problems of insufficient degradation data and different data distribution. This paper proposes an adversarial-based domain generalization (DG) method by integrating time-frequency analysis and data augmentation, to accurately predict the health status of axial piston pumps under unknown working conditions. First, discharge pressure signals under various operating conditions are decomposed by using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and effective intrinsic mode functions (IMFs) are selected to train multiple convolutional neural network (CNN) models. Second, a novel data augmentation method based on the modified discrete cosine transform-composite spectrum (DCS) algorithm is introduced to fuse the IMFs from different domains and generate pseudo data sets. Finally, the adversarial training is adopted between the real data and the pseudo data to capture domain-generalized features. The discharge pressure signal of an actual axial piston pump at different health levels were collected on a test bench to verify the effectiveness of the proposed method. Results indicate that the proposed method has a higher prediction accuracy than the comparative methods.

A multi-frequency approach of the altered functional connectome for autism spectrum disorder identification.

Autism spectrum disorder stands as a multifaceted and heterogeneous neurodevelopmental condition. The utilization of functional magnetic resonance imaging to construct functional brain networks proves instrumental in comprehending the intricate interplay between brain activity and autism spectrum disorder, thereby elucidating the underlying pathogenesis at the cerebral level. Traditional functional brain networks, however, typically confine their examination to connectivity effects within a specific frequency band, disregarding potential connections among brain areas that span different frequency bands. To harness the full potential of interregional connections across diverse frequency bands within the brain, our study endeavors to develop a novel multi-frequency analysis method for constructing a comprehensive functional brain networks that incorporates multiple frequencies. Specifically, our approach involves the initial decomposition of functional magnetic resonance imaging into distinct frequency bands through wavelet transform. Subsequently, Pearson correlation is employed to generate corresponding functional brain networks and kernel for each frequency band. Finally, the classification was performed by a multi-kernel support vector machine, to preserve the connectivity effects within each band and the connectivity patterns shared among the different bands. Our proposed multi-frequency functional brain networks method yielded notable results, achieving an accuracy of 89.1%, a sensitivity of 86.67%, and an area under the curve of 0.942 in a publicly available autism spectrum disorder dataset.

A broadband active sound absorber with adjustable absorption coefficient and bandwidth.

Broadband adjustable sound absorbers are desired for controlling the acoustic conditions within enclosed spaces. Existing studies on acoustic absorbers, either passive or active, aim to maximize the sound absorption coefficients over an extended frequency band. By contrast, this paper introduces a tunable acoustic absorber, whose working frequency band and sound absorption characteristics can be defined by users for different applications. The approach leverages an error signal that can be synthesized using a standing wave separation technique. The error signal encodes different target reflection coefficients, leading to arbitrary absorption coefficients between 0 and 1. Experimental validation is conducted in a one-dimensional standing wave tube, demonstrating that the proposed active absorber achieves near-perfect absorption within the 150-1600 Hz frequency range, boasting an average absorption coefficient of 0.98. Adjustable absorption is demonstrated across three octave bands, aligning closely with theoretical predictions. Furthermore, when coupled with a shaping filter, the absorber exhibits spectrally tunable broadband absorption capabilities, selectively reflecting specific frequency bands while effectively absorbing others. These outcomes underscore the versatile tunability of the proposed active acoustic absorber, which is expected to pave the way for personalized regulating of the indoor acoustic environment.

A Novel Time-Frequency Parameterization Method for Oscillations in Specific Frequency Bands and Its Application on OPM-MEG.

Time-frequency parameterization for oscillations in specific frequency bands reflects the dynamic changes in the brain. It is related to cognitive behavior and diseases and has received significant attention in neuroscience. However, many studies do not consider the impact of the aperiodic noise and neural activity, including their time-varying fluctuations. Some studies are limited by the low resolution of the time-frequency spectrum and parameter-solved operation. Therefore, this paper proposes super-resolution time-frequency periodic parameterization of (transient) oscillation (STPPTO). STPPTO obtains a super-resolution time-frequency spectrum with Superlet transform. Then, the time-frequency representation of oscillations is obtained by removing the aperiodic component fitted in a time-resolved way. Finally, the definition of transient events is used to parameterize oscillations. The performance of this method is validated on simulated data and its reliability is demonstrated on magnetoencephalography. We show how it can be used to explore and analyze oscillatory activity under rhythmic stimulation.

EEG subject-dependent neurofeedback training selectively impairs declarative memories consolidation process

The process of stabilization and storage of memories, known as consolidation, can be modulated by different interventions. Research has shown that self-regulation of brain activity through Neurofeedback (NFB) during the consolidation phase significantly impacts memory stabilization. While some studies have successfully modulated the consolidation phase using traditional EEG-based Neurofeedback (NFB) that focuses on general parameters, such as training a specific frequency band at particular electrodes, they often overlook the unique and complex neurodynamics that underlie each memory content in different individuals, potentially limiting the selective modulation of memories.The main objective of this study is to investigate the effects of a Subject-Dependent NFB (SD-NFB), based on individual models created from the brain activity of each participant, on long-term declarative memories. Participants underwent an experimental protocol involving three sessions. In the first session, they learned images of faces and houses while their brain activity was recorded. This EEG data was used to create individualized models to identify brain patterns related to learning these images. Participants were then divided into three groups, with one group receiving SD-NFB to enhance brain activity linked to faces, another to houses, and a CONTROL sham group that did not receive SD-NFB.Memory performance was evaluated 24 h and seven days later using an ‘old-new’ recognition task, where participants distinguished between ‘old’ and ‘new’ images. The results showed that memory contents (faces or houses) whose brain patterns were trained via SD-NFB scored lower in recognition compared to untrained contents, as evidenced 24 h and seven days post-training.In summary, this study demonstrates that SD-NFB can selectively impact the consolidation of specific declarative memories. This technique could hold significant implications for clinical applications, potentially aiding in the modulation of declarative memory strength in neuropsychiatric disorders where memories are pathologically exacerbated.

Difference thresholds for primary and secondary ride of a vehicle on a 4-poster test rig

Not only is it important to know how large the overall change in vibration should be for occupants to perceive an improvement in comfort, but also how large this change should be in specific frequency bands. Relative difference thresholds (RDT) of primary (0.5–4 Hz) and secondary (9–80 Hz) ride are estimated for 14 automotive engineers seated in a vehicle on a 4-poster test rig over two roads. Resulting stimuli differed in magnitude and spectral shape. The median RDTs estimated for primary and secondary ride were 16.68% and 13.82% on the smooth road, and 9.50% and 24.67% over the rough road. Statistically significant differences were found in the medians of the RDTs between (1) primary and secondary ride on the two roads and (2) the two roads for changes in the primary and secondary ride, suggesting that Weber’s law does not hold.

A shared-aperture pentaband antenna with high impedance surface for CubeSat application

A circular polarized (CP) pentaband antenna based on the aperture-in-aperture (AIA) concept is presented for CubeSat applications. This AIA consists of five different bands ranging from L-band to Ka-band. Four different antennas, each operating at a specific frequency band, namely 12 GHz, 18.5 GHz, 26 GHz, and 32 GHz, were incorporated into an L-band (viz. 1.5 GHz) antenna. Notably, the five antennas can operate simultaneously for a CubeSat downlink operation with a frequency ratio of 21.3:1. The antenna structure shows a realized gain of 5–10 dBi with good CP bandwidth (< 3 dB) across the overall operational frequency range. That is, the realized gain of L-band (1.5 GHz), X-band (12.5 GHz), K-band1 (18.5 GHz), K-band2 (26 GHz), and Ka-bands (32 GHz) are 5.05 dBi, 8.21 dBi, 7.33 dBi, 7.97 dBi, and 8.56 dBi. A high impedance surface (HIS) is incorporated with the Ka-band antenna to mitigate the ripples in the radiation pattern created by the interference of surface waves. A prototype was fabricated and tested. The measurement data agrees well with the simulation.

Multi-domain awareness for compressed deepfake videos detection over social networks guided by common mechanisms between artifacts

The viral spread of massive deepfake videos over social networks has caused serious security problems. Despite the remarkable advancements achieved by existing deepfake detection algorithms, deepfake videos over social networks are inevitably influenced by compression factors. This causes deepfake detection performance to be limited by the following challenging issues: (a) interfering with compression artifacts, (b) loss of feature information, and (c) aliasing of feature distributions. In this paper, we analyze the common mechanism between compression artifacts and deepfake artifacts, revealing the structural similarity between them and providing a reliable theoretical basis for enhancing the robustness of deepfake detection models against compression. Firstly, based on the common mechanism between artifacts, we design a frequency domain adaptive notch filter to eliminate the interference of compression artifacts on specific frequency bands. Secondly, to reduce the sensitivity of deepfake detection models to unknown noise, we propose a spatial residual denoising strategy. Thirdly, to exploit the intrinsic correlation between feature vectors in the frequency domain branch and the spatial domain branch, we enhance deepfake features using an attention-based feature fusion method. Finally, we adopt a multi-task decision approach to enhance the discriminative power of the latent space representation of deepfakes, achieving deepfake detection with robustness against compression. Extensive experiments show that compared with the baseline methods, the detection performance of the proposed algorithm on compressed deepfake videos has been significantly improved. In particular, our model is resistant to various types of noise disturbances and can be easily combined with baseline detection models to improve their robustness.

Design of Digital Lock-in Amplifier based on FPGA

Abstract In order to achieve high-precision measurement of strong noise background signal, a digital lock-in amplifier based on field programmable gate array (FPGA) is designed. The core of the lock-in amplifier consists of a signal processing module, an acquisition and quantisation module and a digital demodulation module. The signal processing module completes the amplification and gain adjustment of the input signal and modulates the signal to be measured into a specific frequency band, which is digitally quantised by the analog to digital converter (ADC) after anti-aliasing filtering and transmitted to the FPGA, and the signal to be measured is extracted by phase-sensitive detection through the mutual-correlation digital demodulation method, thus realising the detection of the weak signal. The performance of the lock-in amplifier is evaluated with a signal generator as the input source, and the results show that the signal-to-noise improvement ratio is 35dB and the relative measurement error of voltage is less than 0.03% after the digital lock-in amplifier is processed, which accurately restores the direct current (DC) signals in the noisy background, and provides an effective technical way for the accurate measurement of weak signals.

BearingFM: Towards a foundation model for bearing fault diagnosis by domain knowledge and contrastive learning

Monitoring bearing failures in production equipment can effectively prevent finished product quality issues and unplanned factory downtime, thereby reducing supply chain uncertainties and risk. Therefore, monitoring bearing failures in production equipment is important for improving supply chain sustainability. Due to the generalization limitations of neural network models, specific models must be trained for specific tasks. However, in real industrial scenarios, there is a severe lack of labeled samples, making it difficult to deploy fault diagnosis models across massive amounts of equipment in workshops. In order to solve the above issue, this paper proposes a cloud-edge-end collaborative semi-supervised learning framework, which provides multi-level computing power and data support for building a foundation model. A data augmentation method based on the bearing fault mechanism is proposed, which effectively preserves the inherent essential characteristics in vibration signals by normalizing frequency and adding noise in specific frequency bands. A novel contrastive learning model is designed, which narrows the distances between positive samples and widens the distances between negative samples in the high-dimensional space through cross comparisons in the time dimension and knowledge dimension, thereby extracting the most essential characteristics from the unlabeled signals. Multiple sets of experiments conducted on four datasets demonstrate that the proposed approach achieves an approximately 98% fault classification accuracy with only 1.2% labeled samples.

Frequency-Selective, Multi-Channel, Self-Powered Artificial Basilar Membrane Sensor with a Spiral Shape and 24 Critical Bands Inspired by the Human Cochlea.

A spiral-artificial basilar membrane (S-ABM) sensor is reported that mimics the basilar membrane (BM) of the human cochlea and can detect sound by separating it into 24 sensing channels based on the frequency band. For this, an analytical function is proposed to design the width of the BM so that the frequency bands are linearly located along the length of the BM. To fabricate the S-ABM sensor, a spiral-shaped polyimide film is used as a vibrating membrane, with maximum displacement at locations corresponding to specific frequency bands of sound, and attach piezoelectric sensor modules made of poly(vinylidene fluoride-trifluoroethylene) film on top of the polyimide film to measure the vibration amplitude at each channel location. As the result, the S-ABM sensor implements a characteristic frequency band of 96-12,821Hz and 24-independent critical bands. Using real-time signals from discriminate channels, it is demonstrated that the sensor can rapidly identify the operational noises from equipment processes as well as vehicle sounds from environmental noises on the road. The sensor can be used in a variety of applications, including speech recognition, dangerous situation recognition, hearing aids, and cochlear implants, and more.

Resonant Acoustic Metamaterials

Acoustic applications of metamaterials have rapidly developed over the past few decades. The sound attenuation provided by metamaterials is due to the interaction between soundwaves and scatterers organized into a reticular grid, with a peak attenuation at a specific frequency band that is highly dependent on the scatterers’ diameter and reticular geometric organization of installation. In this article, the scatterer types chosen for the experiments are represented by a 2D shape, which are cylindrical solid-wood bars of 15 mm diameter and empty cylindrical bars of 20 mm diameter. Acoustic measurements were conducted in a semi-anechoic chamber to identify the specific frequency at which the highest insertion loss (IL) was registered. A second experiment was conducted by creating holes of 5 mm diameter on the external surface of the empty bars; in this way, it registered a higher sound attenuation. In particular, the resonant system characterized with holes, in combination with the attenuation given by 2D scatterer metamaterials, increased the sound attenuation for the frequency range between 1 kHz and 10 kHz.

An end-to-end multi-task motor imagery EEG classification neural network based on dynamic fusion of spectral-temporal features

Electroencephalograph (EEG) brain-computer interfaces (BCI) have potential to provide new paradigms for controlling computers and devices. The accuracy of brain pattern classification in EEG BCI is directly affected by the quality of features extracted from EEG signals. Currently, feature extraction heavily relies on prior knowledge to engineer features (for example from specific frequency bands); therefore, better extraction of EEG features is an important research direction. In this work, we propose an end-to-end deep neural network that automatically finds and combines features for motor imagery (MI) based EEG BCI with 4 or more imagery classes (multi-task). First, spectral domain features of EEG signals are learned by compact convolutional neural network (CCNN) layers. Then, gated recurrent unit (GRU) neural network layers automatically learn temporal patterns. Lastly, an attention mechanism dynamically combines (across EEG channels) the extracted spectral-temporal features, reducing redundancy. We test our method using BCI Competition IV-2a and a data set we collected. The average classification accuracy on 4-class BCI Competition IV-2a was 85.1 % ± 6.19 %, comparable to recent work in the field and showing low variability among participants; average classification accuracy on our 6-class data was 64.4 % ± 8.35 %. Our dynamic fusion of spectral-temporal features is end-to-end and has relatively few network parameters, and the experimental results show its effectiveness and potential.

Microgrid F36ault Detection Method Based on Lightweight Gradient Boosting Machine–Neural Network Combined Modeling

The intelligent architecture based on the microgrid (MG) system enhances distributed energy access through an effective line network. However, the increased paths between power sources and loads complicate the system’s topology. This complexity leads to multidirectional line currents, heightening the risk of current loops, imbalances, and potential short-circuit faults. To address these challenges, this study proposes a new approach to accurately locate and identify faults based on MG lines. Initially, characteristic indices such as fault voltage, voltage fundamentals at each MG measurement point, and extracted features like peak voltage values in specific frequency bands, phase-to-phase voltage differences, and the sixth harmonic components are utilized as model inputs. Subsequently, these features are classified using the Lightweight Gradient Boosting Machine (LightGBM), complemented by the bagging (Bootstrap Aggregating) ensemble learning algorithm to consolidate multiple strong LightGBM classifiers in parallel. The output classification results of the integrated model are then fed into a neural network (NN) for further training and learning for fault-type identification and localization. In addition, a Shapley value analysis is introduced to quantify the contribution of each feature and visualize the fault diagnosis decision-making process. A comparative analysis with existing methodologies demonstrates that the LightGBM-NN model not only improves fault detection accuracy but also exhibits greater resilience against noise interference. The introduction of the bagging method, by training multiple base models on the initial classification subset of LightGBM and aggregating their prediction results, can reduce the model variance and prevent overfitting, thus improving the stability and accuracy of fault detection in the combined model and making the interpretation of the Shapley value more stable and reliable. The introduction of the Shapley value analysis helps to quantify the contribution of each feature to improve the transparency and understanding of the combined model’s troubleshooting decision-making process, reduces the model’s subsequent collection of data from different line operations, further optimizes the collection of line feature samples, and ensures the model’s effectiveness and adaptability.

Frequency-Specific Alterations of Fractional Amplitude of Low-Frequency Fluctuations in Adult-Onset Hypothyroidism.

The neuropathophysiological mechanisms of brain damage underlying hypothyroidism remain unclear. Fractional amplitude of low-frequency fluctuations (fALFF) has been established as a reliable indicator for investigation of abnormal spontaneous brain activity that occurs at specific frequencies in different types of mental disorder. However, the changes of fALFF in specific frequency bands in hypothyroidism have not yet been investigated. Fifty-three hypothyroid patients and 39 healthy controls (HCs) underwent thyroid-related hormone levels tests, neuropsychological assessment, and magnetic resonance imaging (MRI) scans. The fALFF in the standard band (0.01-0.1 Hz), slow-4 (0.027-0.073 Hz), and slow-5 bands (0.01-0.027 Hz) were analyzed. An analysis of Pearson correlation was conducted between fALFF, thyroid-related hormone levels, and neuropsychological scores in hypothyroid patients. Compared to HCs, within the routine band, hypothyroidism group showed significantly decreased fALFF in left lingual gyrus, middle temporal gyrus (MTG), precentral gyrus, calcarine cortex, and right inferior occipital gyrus; within the slow-5 band, the hypothyroidism group exhibited decreased fALFF in left lingual gyrus, MTG, superior temporal gyrus, postcentral gyrus, and paracentral lobule, and increased fALFF in supplementary motor area (SMA) and right middle frontal gyrus; additionally, fALFF in the left lingual gyrus within the routine and slow-5 bands were negatively correlated with the level of thyroid stimulating hormone. In this study, the slow-5 frequency band exhibits better sensitivity than the standard band in detecting fALFF values. A decrease of fALFF values in the lingual gyrus and MTG was observed in both the standard and slow-5 bands and might present potential neuroimaging biomarkers for hypothyroidism. No: ChiCTR2000028966. Registered 9 January, 2020, https://www.chictr.org.cn.

A self-powered metamaterial augmented nanogenerator for low-frequency acoustic telecommunication

This study introduces a novel self-powered metamaterial augmented nanogenerator for low-frequency acoustic telecommunication (MANLAT), which is designed to enhance acoustic communication capabilities by utilizing the local resonance mechanism to selectively amplify specific frequency bands of incident sound and generate large amplitude signals in a self-powered manner. The MANLAT device consists of a flexible bottom that responds to pressure differences by generating strong vibrations. Additionally, a lightweight cone-shaped component is attached to the central part of the flexible plate, thus increasing the surface area in that region. Through contact electrification and electrostatic induction mechanisms, a substantial electrical signal is generated, allowing the device to serve as a self-powered acoustic telecommunication receiver. To assess its performance in real-world scenarios, environmental interference was simulated by generating white noise added to the modulated waveform containing information, which was then transmitted through a loudspeaker. At the receiving end, a high-precision microphone sensor was used alongside the MANLAT device to capture the sound wave. The experimental results demonstrate that the MANLAT device surpassed the microphone sensor in mitigating noise interference and enabled the effective decoding of information even in the presence of high levels of white noise or background music. Consequently, the device simplifies communication systems, facilitates reliable and environmentally friendly communication, and enhances information security.

Classification of motor imagery using chaotic entropy based on sub-band EEG source localization

Objective. Electroencephalography (EEG) has been widely used in motor imagery (MI) research by virtue of its high temporal resolution and low cost, but its low spatial resolution is still a major criticism. The EEG source localization (ESL) algorithm effectively improves the spatial resolution of the signal by inverting the scalp EEG to extrapolate the cortical source signal, thus enhancing the classification accuracy. Approach. To address the problem of poor spatial resolution of EEG signals, this paper proposed a sub-band source chaotic entropy feature extraction method based on sub-band ESL. Firstly, the preprocessed EEG signals were filtered into 8 sub-bands. Each sub-band signal was source localized respectively to reveal the activation patterns of specific frequency bands of the EEG signals and the activities of specific brain regions in the MI task. Then, approximate entropy, fuzzy entropy and permutation entropy were extracted from the source signal as features to quantify the complexity and randomness of the signal. Finally, the classification of different MI tasks was achieved using support vector machine. Main result. The proposed method was validated on two MI public datasets (brain–computer interface (BCI) competition III IVa, BCI competition IV 2a) and the results showed that the classification accuracies were higher than the existing methods. Significance. The spatial resolution of the signal was improved by sub-band EEG localization in the paper, which provided a new idea for EEG MI research.

Disrupted Functional Brain Network Architecture in Sufferers with Boxing-Related Repeated Mild Traumatic Brain Injury: A Resting-State EEG Study.

Repetitive mild traumatic brain injury (rmTBI) often occurs in individuals engaged in contact sports, particularly boxing. This study aimed to elucidate the effects of rmTBI on phase-locking value (PLV)-based graph theory and functional network architecture in individuals with boxing-related injuries in five frequency bands by employing resting-state electroencephalography (EEG). Twenty-fore professional boxers and 25 matched healthy controls were recruited to perform a resting-state task, and their noninvasive scalp EEG data were collected simultaneously. Based on the construction of PLV matrices for boxers and controls, phase synchronization and graph-theoretic characteristics were identified in each frequency band. The significance of the calculated functional brain networks between the two populations was analyzed using a network-based statistical (NBS) approach. Compared to controls, boxers exhibited an increasing trend in PLV synchronization and notable differences in the distribution of functional centers, especially in the gamma frequency band. Additionally, attenuated nodal network parameters and decreased small-world measures were observed in the theta, beta, and gamma bands, suggesting that the functional network efficiency and small-world characteristics were significantly weakened in boxers. NBS analysis revealed that boxers exhibited a significant increase in network connectivity strength compared to controls in the theta, beta, and gamma frequency bands. The functional connectivity of the significance subnetworks exhibited an asymmetric distribution between the bilateral hemispheres, indicating that the optimized organization of information integration and segregation for the resting-state networks was imbalanced and disarranged for boxers. This is the first study to investigate the underlying deficits in PLV-based graph-theoretic characteristics and NBS-based functional networks in patients with rmTBI from the perspective of whole-brain resting-state EEG. Joint analyses of distinctive graph-theoretic representations and asymmetrically hyperconnected subnetworks in specific frequency bands may serve as an effective method to assess the underlying deficiencies in resting-state network processing in patients with sports-related rmTBI.

A Scoping Review of the Effect of EEG Neurofeedback on Pain Complaints in Adults with Chronic Pain.

Background and Aim: Non-pharmacological treatments such as electroencephalogram (EEG) neurofeedback have become more important in multidisciplinary approaches to treat chronic pain. The aim of this scoping review is to identify the literature on the effects of EEG neurofeedback in reducing pain complaints in adult chronic-pain patients and to elaborate on the neurophysiological rationale for using specific frequency bands as targets for EEG neurofeedback. Methods: A pre-registered scoping review was set up and reported following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) extension for Scoping Reviews (PRISMA-ScR). The data were collected by searching for studies published between 1985 and January 2023 in PubMed, EMBASE, and PsycINFO. Results: Thirty-two studies on various types of chronic pain were included. The intervention was well-tolerated. Approximately half of the studies used a protocol that reinforced alpha or sensorimotor rhythms and suppressed theta or beta activity. However, the underlying neurophysiological rationale behind these specific frequency bands remains unclear. Conclusions: There are indications that neurofeedback in patients with chronic pain probably has short-term analgesic effects; however, the long-term effects are less clear. In order to draw more stable conclusions on the effectiveness of neurofeedback in chronic pain, additional research on the neurophysiological mechanisms of targeted frequency bands is definitely worthwhile. Several recommendations for setting up and evaluating the effect of neurofeedback protocols are suggested.

Research and simulation of electromagnetic wave absorbing performance for lightweight cement-based materials containing expanded polystyrene with different particle sizes and carbon black

The electromagnetic wave (EMW) absorbing performance of cement-based materials incorporating expanded polystyrene (EPS) with varying particle sizes is comprehensively studied through a combination of experimental and simulation methods. The addition of conductive carbon black (CB) to the cement matrix significantly increases the conductivity and dielectric constant, thereby improving the loss capacity. The frequency shift behavior of single and three-layer absorbing materials adheres to the effective medium theory and interference cancellation principle. The enhancement in overall EM parameters leads to a shift of the reflection loss (RL) peak towards lower frequencies. For three-layer materials, an increase in the thickness of absorbent layer proves beneficial for enhancing absorption performance. Simulations can effectively model cement-based materials by employing uniform material settings, with the size of EPS particles serving as the primary variable. The simulation results reveal that an increase in EPS particle size, in both single-layer and multi-layer materials, causes a shift of the RL peak towards lower frequencies. Additionally, analysis of electric field mode distribution and EM power loss density distribution indicates that scattering at the curved EPS interface alters the propagation direction of EMWs, increasing opportunities for loss inside the cement-based material. Moreover, the three-layer absorbing material, with a density ranging from 850 to 900 kg/m3 and compressive strength exceeding 4 MPa, is suitable for EMW absorption in building envelope structures. In conclusion, an increase in EPS particle size and CB content leads to a low-frequency shift of the RL absorption peak. This discovery not only provides an empirical foundation for the development of absorption materials tailored to specific frequency bands but also offers an efficient approach for repurposing waste EPS of diverse sizes. Furthermore, the grading of EPS with different particle sizes has important practical significance for increasing the content of EPS and preparing lightweight cement-based materials.