Matching Ability Research Articles

Face matching and self-insight: A Registered Report investigating individual differences in metacognitive sensitivity, efficiency, and bias.

Deciding whether two face photographs depict the same person or not can be a challenging task, and there are substantial individual differences in face matching ability. Far less is known about differences in metacognitive ability; that is, how well people can estimate the quality of their own face matching judgements. The purpose of this Registered Report was to determine the relationship between first-order performance in a face matching task, and three metacognitive measures: metacognitive sensitivity (the information exploited by metacognition), metacognitive efficiency (the quality of metacognitive processing itself), and metacognitive bias (the overall tendency towards high or low confidence). Participants completed a 200-trial unfamiliar face matching task, providing a second-order (metacognitive) confidence rating after each first-order (cognitive) response. Cognitive performance and metacognitive sensitivity showed a substantial positive association (rs = .60), suggesting that they were based on overlapping information, with poorer performers having lower quality information available. Once these differences in the information available to metacognition were accounted for, the quality of metacognitive processing itself (metacognitive efficiency) did not vary systematically with cognitive performance (rs = .00). Moreover, poor performers were not less or more confident overall than good performers, as cognitive performance and metacognitive bias did not correlate significantly (rs = .11). These findings allow us to move beyond consideration of group-level insight and inform regarding individual differences in cognitive and metacognitive abilities.

Comparison of Color Matching Ability of Dental Students and Dental Graduates using Vita Tooth Guide 3-D Master

Background: Achieving a satisfactory color match for dental restorations necessitates the effective selection and replication of shades. The knowledge of color science provides an understanding of the fundamental principles governing color mixing and interactionObjective: To compare the ability of color matching of dental students and dental graduates using vita tooth guide 3-D master.Material and Methods: This comparative cross-sectional study was conducted on 200 participants (100 dental students and 100 graduates) at the Prosthodontics department, LUMHS, Jamshoro using convenience sampling technique. At some time between 10:00 AM and 12:00 PM. The vita tooth guide 3D master® shade guide (VITA, Japan incorp) was used to compare the ability to match colors. Each participant was asked to color matched three shades (2L1.5, 1M2, and 2R1) of porcelain maxillary right central incisors. The comparison of color identification between dental students and graduates was conducted using the chi-squareResults: The gender distribution analysis revealed that group A had 43 (43%) male participants and group B had 37 (37%) male participants. Dental graduates were more competent in correct identified proper shade than dental students for sample I(shade 2L 1.5) (p<0.001), II (shade 1M2) (p<0.001), and III (shade 2R1) (p<0.001). The association experience with Vita tooth guide 3D master with color identification test was only statistically significant for sample II (p=0.04). The results were similar among genders. For all samples and in both genders dental graduates were better than dental students in color identification statistically.Conclusion: Training and experience significantly contribute to the enhancement of color matching skills in shade selection.Keywords: Color Matching, Dental Students, Dental Graduates, Vita toothguide 3D master.

Design and optimization of double-layer structure for improved electromagnetic wave absorbing characteristic of single-layer foam cement-based materials containing carbon black

Electromagnetic wave (EMW) absorbing properties are increasingly needed in cement-based materials; however, lightweight and broadband absorption properties are challenging to realize. This study aims to create lightweight, broadband, and highly efficient EMW-absorbing materials by investigating a foam cement-based material with single- and double-layer structures containing carbon black (CB). The results revealed that CB significantly affects the mechanical properties and electrical conductivity of foam cement-based materials. The compressive strength of the foam cement-based material containing 1.5 wt% CB with a density of 0.9 g cm−3 can reach 7.0 MPa and has satisfactory workability and conductivity. The dielectric properties of the foam cement-based material can be synergistically controlled by its density and CB content, and obtain favorable electric loss ability. For a single-layer structure, the optimal reflection loss (RL) value of a specimen with 10 mm-thick can reach −29.67 dB with an efficient bandwidth of 0.99 GHz. In contrast, the double-layer structure exhibited remarkable broadband characteristics. When the total thickness of double-layer structure is 10 mm, the optimal two specimens can achieve a minimum RL value of −28.62 and −25.52 dB with a maximum effective bandwidth of 1.49 and 1.87 GHz, respectively. This study proposes single- and double-layer foam cement-based structures with excellent impedance matching and loss attenuation ability, and constructs an effective design method for cement-based materials to achieve lightweight and broadband absorption. In addition, these cement-based materials can be regarded as a new generation of EMW-absorbing materials in the field of construction engineering.

Large-scale aerial scene perception based on self-supervised multi-view stereo via cycled generative adversarial network

Unmanned aerial vehicle (UAV) has the characteristics of strong maneuverability and wide field of vision, and its application in real-time terrain perception and target capture is one of the active frontier topics in the field of UAV cooperative situation awareness. LiDAR-based method has unique advantages, but data acquisition is difficult and costly. In comparison, multiple view stereo-based (MVS) perception method contains rich image information with low cost, which is relatively easy to be collected. MVS also has potential application value in human–computer interaction and robot positioning (unstructured environment perception, visual servo). To reduce the dependence on costly ground truth, the current self-supervised MVS works calculate the training loss by assuming that the same space points projected into multiple views will share the same RGB information. However, this gap occurs mainly because in the real scene, interference factors such as specular reflection, illumination, and noise will cause large color changes in the source images, which will make it difficult to restrict the photometric consistency of different perspectives with ideal self-supervised loss. To realize more accurate scene perception, a novel self-supervised MVS integrated with generative adversarial network (GAN) is proposed. The overall framework is stacked twice by inputting real images and synthetic images into the network in turn, and adversarial learning is introduced to jointly distinguish the images. In addition, the consistency loss of predicted depth maps from real images and synthetic images is designed to improve the robustness of feature matching and color anti-noise ability. To the best of our knowledge, the proposed work is the first end-to-end depth inference model in MVS field that introduces the adversial training based on GAN, which is an effective complement to self-supervised scene reconstruction. Comprehensive experiment results on the public datasets show that the proposed work is comparable to the mainstream self-supervised MVS.

Increased Ability to Perceive Relevant Sensory Information Minimizes Low Back Exposures in Lifting.

We have previously shown evidence that some individuals seem to consistently minimize low back loads when lifting, while others do not. However, it is unknown why. Individual differences in ability to perceive relevant sensory information may explain differences in minimization of low back loads during lifting, consistent with considering load reduction in one's movement objective in an optimal feedback control theory framework. The purpose of this study was to investigate whether individuals' ability to perceive proprioceptive information (both force- and posture-senses) at the low back was associated with peak low back loads when performing generic or occupation-specific lifts. Seventy-two participants were recruited to perform 10 barbell (generic) and backboard (occupation-specific) lifts, while whole-body kinematics and ground reaction forces were collected. Peak low back compression and anteroposterior shear forces normalized to body mass were calculated as dependent variables. Both posture matching ability and force matching ability at the heavier force targets were associated with lower means and variability of peak low-back loads in both lift types, albeit with small effect sizes (R2 ≤ .17). These findings support the utility of an optimal feedback control theory framework to explore factors explaining interindividual differences in low back loads during lifting. Further, this evidence suggests improving proprioceptive ability may be a useful strategy in lift training programs designed for workplace injury prevention.

Olfactory training improves emotion matching ability in 6–9 years old children — preliminary evidence

AbstractEmotion knowledge involves the ability to recognize and express emotions and understand emotional processes. The neural substrates of emotion knowledge include i.a. the amygdala, orbitofrontal cortex, and anterior cingulate cortex. These structures are also involved in processing olfactory stimuli and their volume and functional activity have been shown to increase after a period of regular olfactory stimulation (olfactory training, OT). We verified if OT will improve one aspect of emotion knowledge — the ability to match emotional facial expressions. Eighty‐three children (39 boys) aged 6–9 years were randomly assigned either to an OT group (OTG) that smelled a set of four odors (lemon, cloves, eucalyptus, rose) twice a day for 12 weeks or to a placebo group that performed the training with odorless stimuli. Children in the OTG (n = 40) matched emotional facial expressions significantly better after OT as compared with their baseline score. This effect was not present in the placebo group (n = 43) whose scores remained the same after OT.Practical applicationsThis novel finding suggests the potential of olfactory stimulation in supporting emotion knowledge in children and bridges recent findings in sensory studies, psychophysiology, and emotional development. Further studies are necessary to delineate which aspects of emotion knowledge might be enhanced by OT. In future, OT might be potentially included in programs designed for groups with specific emotion knowledge deficits.

A medical report generation method integrating teacher–student model and encoder–decoder network

The automatic medical report generation task can reduce the burden of radiologists and improve the intelligence of auxiliary diagnosis, but still faces the following challenges: (1) The small lesions are easily overlooked, leading to loss of crucial information in the report and low accuracy; (2) The generated long text reports often suffer from jumbled word order and sentence order, resulting in poor fluency. Through simulation of the cognitive principle of professional physicians during their training and work, this paper put forward a medical report generation method integrating a teacher–student model with an encoder–decoder network. The core idea is to propose a cross-modal teacher (text)-student (image) model, adopting different supervision methods for different stages of report generation to improve the model’s learning performance. A semantic space alignment mechanism is designed to enhance the cross-modal feature matching ability by contrasting the encoding methods of different modalities through adversarial learning, gradually optimizing and capturing the critical information. A layer-supervised decoder based on the Transformer hierarchical structure is proposed with the teacher model guiding the student model to decode layer by layer to increase the fluency of report generation. Comparative experiments are conducted on IU-X-ray and MIMIC-CXR datasets with various other methods, and the results show that the proposed method can effectively improve the quality of generated reports.

Synergistic effect of nickel selenide‑carbon composites for efficient broadband electromagnetic wave absorption

Novel NiSe2-based composites loaded with carbon nanotubes (CNTs) and graphene (GNs), respectively, were successfully synthesized via a facile hydrothermal reaction, aiming to enhance the electromagnetic wave (EMW) absorption performance. Microstructure, morphology, dielectric properties and EMW absorption performance of as-prepared NiSe2/CNTs and NiSe2/GNs composites were explored. Results showed that both NiSe2/CNTs and NiSe2/GNs composites exhibited improvement in EMW absorption compared to pure NiSe2, especially NiSe2/GNs showed the best EMW absorption performance. NiSe2/GNs composites demonstrated an exceptional minimum reflection loss (RLmin) of −52.10 dB at a thin thickness of merely 1.71 mm, accompanied by a wide effective absorption bandwidth (EAB) of 4.1 GHz. However, the EMW absorbing performance of NiSe2/CNTs composites was slightly lower than that of NiSe2/GNs composites, showing an RLmin of −45.10 dB and an EAB of 3.76 GHz at a thin thickness of 1.60 mm. Such significantly enhanced absorption capability of NiSe2/GNs composites can be attributed to the synergistic interplay of desirable impedance matching and efficient attenuation ability. These findings highlight the potential of NiSe2-based composites loading with carbon conductive fillers as highly promising contenders for high-performance EMW absorption materials.

Analysis of shade-matching ability in dental students: a comparative study under clinical and correcting light conditions

BackgroundSelecting the ideal tooth shade is essential to the success of aesthetic dental restorations. Students’ cognitive abilities are involved in the multifaceted and intricate process of shade matching. Hence, the present study aimed to assess and compare the shade-matching ability of undergraduate dental students in various years of dental education under clinical and correcting light.MethodsThis comparative cross-sectional study was instigated amongst male 4th, 5th, and 6th-year students of the dental complex of King Faisal University, Kingdom of Saudi Arabia. A total of 72 male dental students assessed the shade under clinical (fluorescent light) and correcting light (handheld Dental Base Light) by using VITA Classical shade guides. Statistical analysis was done using SPSS version 23 (Armonk, NY, USA). The Chi-square test was used to evaluate the association between correct and incorrect shade matching under correcting and clinical light.ResultsOut of 72 male students, 22(30.6%) were from the 4th year, 26(36.1%) were from the 5th year, and 24(33.3%) were from the 6th year, with a mean age of 22.92 ± 1.01 years. The majority of the 6th-year students selected shade of anterior tooth # 11 correctly under clinical and correcting light, and 3(12.5%) students selected incorrectly under clinical and correcting light, with a statistically significant association among them (p = 0.004). As far as the shade selection of the posterior tooth is concerned, a statistically significant difference was observed under clinical light among all clinical students (p = 0.008).ConclusionThe clinical performance of dental students in shade matching improved with advancing years of dental education. Additionally, the shade matching ability of all groups of dental students was superior under correcting light compared to conditions under clinical light.

Braiding drive data processing through rote learning

It is difficult for traditional data processing methods to make full use of the potential of braiding driven data, unable to quickly collect and preprocess data, and difficult to ensure the accuracy of data. Rote learning (RL) is part of the research field of artificial intelligence, which aims to enable computers to learn autonomously, just like humans. This allows understanding of relationships and patterns between data and helps computers process information quickly. In order to solve the problems of poor data integrity, slow data processing efficiency and poor information sharing in traditional data processing, and further optimize the braiding driven data processing technology, this paper combined RL with braiding driven data. Through the method of mechanical learning, the potential of weaving driving data is fully exerted, so that it can better cope with nonlinear relations and high-dimensional features. It used the effective method provided by the RL to process the braiding drive data, collect the data, and preprocessed the collected data to ensure the correctness of the data. It extracted the features of the data, which was convenient to classify the data according to its attributes. At the same time, this paper verified it by the steps of feature extraction, model training and data analysis. In order to test whether braiding drive data processing by RL can effectively solve the problems existing in traditional drive data technology, this paper tested the performance of compiled drive data processing, and the analysis results were as follows. The data integrity rate of braiding drive data was as low as 81%, which was much higher than that of traditional drive data processing. The recognition ability of data acquisition and matching was much higher than that of traditional drive data processing. Compared with the traditional drive data processing, the information sharing has been greatly improved. In terms of data processing efficiency, it is also much higher than the traditional drive data processing. It can be seen that the method of braiding drive data processing through RL effectively improves the accuracy of data processing. It strengthens the identification ability of data collection and matching, improves the sharing of information, enables users to obtain data and analyze it faster, and also improves the processing efficiency of data.

Optimisation of cross-border e-commerce logistics model innovation path in the era of digital technology

Abstract Currently, cross-border e-commerce logistics is lagging behind the development of infrastructure imperfections and other issues, so the realization of e-commerce logistics model innovation is a top priority. This paper firstly analyzes the innovation path of cross-border e-commerce logistics mode to shorten the logistics cost and improve the development efficiency of cross-border logistics chain by supply chain, logistics system, packaging, and localization. Secondly, it establishes a logistics operation demand forecasting model based on principal component analysis and radial basis function and introduces the composite system synergy model to measure the logistics matching ability and calculate the cross-border e-commerce logistics matching ability. Finally, quantitative analysis is carried out on three aspects of cross-border e-commerce logistics operations: demand, matching ability, and service effect. The results show that: the application of a cross-border e-commerce logistics model innovation path reduces the potential risk of cross-border express clearance, and the average timeliness of returned express shipment, goods arrival to delivery, product tariff tax rate across the border, and the complaint rate are reduced by 45, 0.5, 0.28, and 0.65 in 2019-2023. Meanwhile, the customer satisfaction is increased from 0.8416 to 0.9641. The cross-border e-commerce logistics model innovation path is proposed to meet the actual needs of social development.

Unsupervised Non-rigid Histological Image Registration Guided by Keypoint Correspondences Based on Learnable Deep Features with Iterative Training.

Histological image registration is a fundamental task in histological image analysis. It is challenging because of substantial appearance differences due to multiple staining. Keypoint correspondences, i.e., matched keypoint pairs, have been introduced to guide unsupervised deep learning (DL) based registration methods to handle such a registration task. This paper proposes an iterative keypoint correspondence-guided (IKCG) unsupervised network for non-rigid histological image registration. Fixed deep features and learnable deep features are introduced as keypoint descriptors to automatically establish keypoint correspondences, the distance between which is used as a loss function to train the registration network. Fixed deep features extracted from DL networks that are pre-trained on natural image datasets are more discriminative than handcrafted ones, benefiting from the deep and hierarchical nature of DL networks. The intermediate layer outputs of the registration networks trained on histological image datasets are extracted as learnable deep features, which reveal unique information for histological images. An iterative training strategy is adopted to train the registration network and optimize learnable deep features jointly. Benefiting from the excellent matching ability of learnable deep features optimized with the iterative training strategy, the proposed method can solve the local non-rigid large displacement problem, an inevitable problem usually caused by misoperation, such as tears in producing tissue slices. The proposed method is evaluated on the Automatic Non-rigid Histology Image Registration (ANHIR) website and AutomatiC Registration Of Breast cAncer Tissue (ACROBAT) website. It ranked 1st on both websites as of August 6th, 2024.

Effect of Formal Training on Tooth Shade Matching Ability among Indian Dental Students in Diverse Clinical Settings: A Quasi-experimental Study

Introduction: Selecting a correct shade is the key to success of any prosthesis, especially in the aesthetic zone. Variations of shade in a single tooth or the polychromatic nature of a tooth can impose greater challenges in shade matching. Therefore, it would be best if it is taught during the undergraduate course in dental schools. Aim: To evaluate the knowledge and shade matching skills among dental undergraduate students with and without training in three different clinical conditions. Materials and Methods: This post-test only quasi-experimental study was conducted with 377 undergraduate students of Indian origin studying in a dental teaching institute. The students were divided into two groups based upon their clinical training in shade matching: Group I (trained, n=197) and Group II (untrained, n=180). Three different patients, in terms of their shade complexity, were presented to both the groups and their shade matching ability was assessed. The results were subjected to statistical analysis using the Chi-square test and one-way Analysis of Variance (ANOVA). Results: Statistical analysis showed that the mean correct scores of the trained students were significantly higher than those of the untrained students (p=0.0001) in all three patients. There was a statistically significant difference (p=0.0001), as the trained group was better able to identify the correct or clinically acceptable shade compared to the untrained group in all three cases. Conclusion: The study concluded that training significantly improved the shade matching ability of dental undergraduate students in clinical conditions presenting varied complexities.

Cost Volume Aggregation in Stereo Matching Revisited: A Disparity Classification Perspective.

Cost aggregation plays a critical role in existing stereo matching methods. In this paper, we revisit cost aggregation in stereo matching from disparity classification and propose a generic yet efficient Disparity Context Aggregation (DCA) module to improve the performance of CNN-based methods. Our approach is based on an insight that a coarse disparity class prior is beneficial to disparity regression. To obtain such a prior, we first classify pixels in an image into several disparity classes and treat pixels within the same class as homogeneous regions. We then generate homogeneous region representations and incorporate these representations into the cost volume to suppress irrelevant information while enhancing the matching ability for cost aggregation. With the help of homogeneous region representations, efficient and informative cost aggregation can be achieved with only a shallow 3D CNN. Our DCA module is fully-differentiable and well-compatible with different network architectures, which can be seamlessly plugged into existing networks to improve performance with small additional overheads. It is demonstrated that our DCA module can effectively exploit disparity class priors to improve the performance of cost aggregation. Based on our DCA, we design a highly accurate network named DCANet, which achieves state-of-the-art performance on several benchmarks.

Evaluating the Color Matching Ability of a Smart Chromatic Technology-Based Composite Resin for Premolar Teeth Restoration.

Objectives: This study aims to evaluate the color-matching ability of OMNICHROMA composite, a single-shade composite, using the CIE L ∗ a ∗ b ∗ system and determining ΔE values. Materials and Methods: In this in vitro study, 30 intact premolar teeth were collected, cleaned, and disinfected with 0.5% thymol solution. The teeth were stored in distilled water at 37°C. A putty index calibrated the color assessment sites on buccal and lingual surfaces. Two cavities were prepared, one with 1.25 mm depth (enamel) and another with 2.25 mm depth (dentin), and restored using OMNICHROMA composite resin. The putty index measured the color of the cured composite, and ΔE between the composite and original tooth color was calculated. Statistical analysis: Data analysis included t-tests, correlation coefficients, and Fisher's exact test (α = 0.05). Results: The mean ΔE was 2.39 in enamel cavities and 2.32 in dentin cavities, both within the clinically acceptable range (<3.3). In enamel cavities, the composite color was darker than the tooth, shifting toward green and blue shades. In dentin cavities, the composite color was lighter than the tooth, with shifts toward green and yellow shades. Color matching was slightly better in dentin cavities, but the difference was not statistically significant (p=0.719). Conclusion: OMNICHROMA composite resin's color matching falls within the clinically acceptable range, unaffected by cavity depth.

An Implicit Geometric Optimization Study of Intelligent Teaching Methods in the Reform of Public Physical Education Courses in Colleges and Universities

Abstract Virtual reality is an important realization of intelligent teaching methods, which is of great significance for the reform of public sports courses in colleges and universities. Aiming at the problem of the lack of three-dimensional information representation of the sports human body in virtual reality technology, this paper proposes a representation optimization method based on implicit geometry, which improves the traditional representation by pixel alignment features, voxel alignment features, and the introduction of z-difference. Experiments show that the new representation method has smaller representation errors and higher computational speeds. The table tennis forehand and backhand table tennis scores of the experimental group are higher than those of the control group by 7.8% and 12.5%, and the total score of the match ability reaches 71.05. The interest score is higher than that of the control group by 3.23 points. The positive effects of the new representation and intelligent teaching methods are evident.

Suitable-Matching Areas’ Selection Method Based on Multi-Level Saliency

Scene-matching navigation is one of the essential technologies for achieving precise navigation in satellite-denied environments. Selecting suitable-matching areas is crucial for planning trajectory and reducing yaw. Most traditional selection methods of suitable-matching areas use hierarchical screening based on multiple feature indicators. However, these methods rarely consider the interrelationship between different feature indicators and use the same set of screening thresholds for different categories of images, which has poor versatility and can easily cause mis-selection and omission. To solve this problem, a suitable-matching areas’ selection method based on multi-level saliency is proposed. The matching performance score is obtained by fusing several segmentation levels’ salient feature extraction results and performing weighted calculations with the sub-image edge density. Compared with the hierarchical screening methods, the matching performance of the candidate areas selected by our algorithm is at least 22.2% higher, and it also has a better matching ability in different scene categories. In addition, the number of missed and wrong selections is significantly reduced. The average matching accuracy of the top three areas selected by our method reached 0.8549, 0.7993, and 0.7803, respectively, under the verification of multiple matching algorithms. Experimental results show this paper’s suitable-matching areas’ selection method is more robust.

Comparison of Shade Matching Performance of Dental Professionals and Untrained Individuals in Visual Shade Matching Method

Statement of the problem: It is unclear how the success of color selection with custom shade guides is influenced by the observer's level of color education and the type of light source. Objective: The aim of this study is to compare the color matching ability under different light sources between observers with and without color education and to investigate in which shade tabs they are more successful, using a custom-made shade guide. Materials &amp; Methods: The study included 5 prosthodontists (PD), 5 general dentists (GD), 5 dental ceramists (DC), 5 removable denture technicians (RDT), and 10 participants with no color education (NP). Using dentin powder from feldspathic dental porcelain, 2 sets of 16 shade tabs matching those in the Vita Lumin Classic shade guide were produced. The Lab values of the produced shade tabs with the same shade code were measured using a colorimeter device, and those with ΔE values below 0.1 were used for shade matching. Each participant conducted a total of 48 shade matches under 3 different light sources (tungsten-TG, fluorescent-FL, and D65). Results: It was found that the shade matching success of dental professionals in the PD, GD, RDT, and DC groups significantly differed from that of the participants in the NP group (χ² = 23.417; P&lt; 0.001). Shade matches conducted under the D65 light source were more successful than those under the TG and FL light sources, and participants were found to match shade tabs with higher saturation, such as A4 and C4, more successfully. Conclusions: Color matching performed with a custom color scale is influenced by the observer's training and the light source. As the saturation of shade tabs increases, the matching success also increases.

The Connotation of Objectives and Hierarchical Orientation of Teaching English and American Literature Based on the Era of Big Data

Abstract This paper combines the knowledge structure of English and American literature and learners’ personalized knowledge characteristics to design and propose personalized teaching based on a genetic algorithm. Genetic algorithms are used to extract learner characteristics, and content-based recommendation algorithms are applied to match ability characteristics, goal characteristics and learning object characteristics to achieve personalized course teaching. The crossover probability, variation probability, selection operator and crossover operator of the genetic algorithm are determined, and simulation experiments are designed to analyze the optimization effect of its parameters. Setting three levels of colleges and universities, with the connotation of the objectives and level positioning of English and American literature teaching, i.e., reading experience, reading knowledge, reading method, and the cultivation of cultural connotation and humanistic sentiment as factors, a one-way ANOVA was carried out to examine whether the teaching objectives of different levels of colleges and universities differed significantly in personalized teaching. In terms of the highest level of the teaching objectives of English and American literature, i.e., the cultivation of humanistic sentiment, F=6.607, Sig=0.002, 985 colleges&gt;211 colleges and universities, and general undergraduate schools&gt;211 colleges and universities.

Electrostatically self-assembled Fe3O4@SiO2/MXene 3D interlayered structure improves Ku-band microwave absorption efficiency of epoxy-based nanocomposites

It is challenging to develop lightweight and efficient frequency-selective microwave absorption materials due to the trade-off between impedance matching and microwave attenuation ability. This study proposes a multiple heterointerfaces engineering strategy via electrostatically assembling to construct Fe3O4@SiO2/MXene 3D interlayer structure, which fully exerted the synergistic effect of dielectric-magnetic components. The 3D interlayer heterostructure achieves good impedance matching due to electromagnetic coupling and extended microwave propagation path. Simultaneously, massive charge transfer and redistribution at multiple heterointerfaces embedded in this structure boost the polarization relaxation and dielectric loss ability. Consequently, the prepared nanocomposites exhibit remarkable Ku-band microwave absorption efficiency (−60.9 dB at a thickness of 1.0 mm), and have demonstrated valid practicality via Tesla wireless transmission shielding experiments. This study opens exciting possibilities for progress in the structural design, facile preparation and practical application of high-performance Ku-band microwave absorption materials.