Strong Complexes Research Articles

Integrating Risk-Conflict assessment for constructing and optimizing ecological security patterns of Polder landscape in the Urban-Rural fringe

Jianghan Plain is an important grain-producing area in China, and the transformation of the natural environment by the long-term polder activities of the people in this area has created a characteristic polder landscape. After 2000, urbanization has led to the destruction of ecosystems in these polder landscapes, especially in sensitive areas at the urban–rural interface. ESP can help mitigate this threat by systematically identifying and connecting ecologically significant areas to enhance ecosystem stability and sustainability. In this paper, taking Dongxihu Polder as an example, we apply multi-source remote sensing data to develop LER and LUC models. Combining these with MCR model, we construct an ESP for Dongxihu Polder. The study shows that (1) the growth of LER and LUC in Dongxihu Polder is characterized by “strong dynamics, high complexity and weak protection”, which is closely related to the urban expansion of the urban–rural boundary area; (2) Watersheds, woodlands, and farmlands play an important role in controlling LERs and LUCs, so we identified 33 ecologically important source areas that are mainly concentrated in these regions; (3) In this paper, 46 ecological strategic nodes and 16 ecological corridors were extracted to construct the Dongxihu Polder ESP of “one ring, five axes, multiple points and multiple chains”, and suggested specific optimization strategies. The study provides feasible ecological management policy recommendations for the protection as well as sustainable development of Dongxihu Polder.

Electrochemical and Theoretical Investigations on the Binding of Anticancer Drug Olaparib to Human Serum Albumin

Abstract This article explores the interaction between Olaparib (OLA), an anticancer drug, and human serum albumin (HSA) on a glassy carbon electrode using cyclic voltammetry and differential pulse voltammetry methods. The study investigates the alterations in the electrochemical behavior of [Fe(CN)6]3–/4– redox pairs in a physiological pH buffer solution due to the OLA-HSA interaction. The electrochemical and kinetic parameters of the redox probe were calculated in the presence of both the drug and protein. Notable variations in these parameters were observed, which can be attributed to the formation of an electro-inactive complex between the protein and drug. The study determined the parameters describing the OLA-HSA complex, including the binding constant and complex stoichiometry. The results revealed the formation of a strong singular drug-albumin complex with a binding constant of 1.12 × 105 M–1. Molecular docking studies supported the experimental findings and provided insights into the binding interactions of OLA with HSA. This study provides valuable insights for future research aimed at enhancing drug delivery systems.

The bi-long short-term memory based on multiscale and mesoscale feature extraction for electric load forecasting

Accurate power load prediction is beneficial to the efficient use of electric energy and the orderly development of power systems. Given the strong volatility and complexity of power load series, a hybrid load forecasting method based on multiscale and mesoscale information fusion, signal decomposition, model optimization, and bi-long-short-term memory (BiLSTM) is proposed. Firstly, the load sequence is analyzed on different time scales, and the extracted multi-scale information and mesoscale information are fused to improve the perception ability. Secondly, the empirical wavelet transform (EWT) with adaptive decomposition ability is used to decompose the sequence and extract the rich feature information. Thirdly, the complexity, volatility, and uncertainty of each mode component were analyzed, the data features were fully mined, and the feature fusion was carried out by the TOPISIS evaluation method. The BiLSTM model and the GWO-BiLSTM model are used to predict the low-frequency component and the high-frequency component, respectively. The optimization of Grey Wolf optimization (GWO) algorithm can improve the BiLSTM model's ability to learn long-term time series. Finally, the analysis of application examples shows that compared with various prediction models, the prediction error of mixed model EWT-SGEO-BiLSTM is the smallest, MAPE is as low as 1.07 %, and goodness of fit R2 is 0.99 which verifies the accuracy and applicability of the intelligent model.

Reservoir properties inversion using attention-based parallel hybrid network integrating feature selection and transfer learning

The inversion of subsurface reservoir properties is of profound significance to the oil and gas energy development and utilization. The strong heterogeneity and complex pore structure of underground reservoirs pose a challenge to efficient oil and gas energy development and utilization across the world, which increases the necessity of developing an efficient reservoir properties inversion method. However, traditional model-driven methods are confronted with the challenges of strong nonlinearity and geological heterogeneity. Moreover, previous studies rarely emphasized the importance of nonlinear feature selection and transfer learning (TL). Aiming to address the research gaps, a reservoir properties inversion method was proposed by combining random forest (RF) feature selection, bidirectional temporal convolutional network (BiTCN), bidirectional gated recurrent units (BiGRU) network, multi-head attention (MHA) mechanism and TL strategy. First, the RF was used to screen the features with significant correlation with the target reservoir properties. Thereafter, combining BiTCN and BiGRU network to leverage their complementary strengths, a parallel dual branch feature learning network was constructed to learn richer geological information from logging data. Meanwhile, MHA was introduced to fuse the output features of the dual network structure. Finally, the fused features were passed through the fully connected module to output the inversion results. TL was used to associate the correlation between reservoir properties and model inversion to improve the inversion performance. The application results with actual field data showed that the proposed method was accurate and robust in reservoir properties inversion. This study can provide a new way for reliable reservoir properties inversion and promote the application of artificial intelligence in data-driven energy science.

Multi-component quantitative analysis of LIBS using adaptively optimized multi-branch CNN

Multi-component quantitative analysis of laser-induced breakdown spectroscopy (LIBS) is very important for industrial process control, food safety and space exploration. Due to the strong background interference and complex matrix effect, traditional quantitative analysis methods are difficult to realize accurate multi-component regression. Deep learning has the advantages of automatically extracting full-spectrum features and strong nonlinear modeling capabilities, which provides new opportunities for advancing the progress of LIBS quantitative analysis. However, due to the large difference of spectral line intensity and spectral cross-interference, existing deep learning models have limited performance for simultaneous multi-component regression. To this end, a novel adaptively optimized multi-branch convolution neural network (AOMB-CNN) framework is proposed for multi-component quantitative analysis of LIBS. Multi-branch structure is adopted to ensure the accuracy of multi-component content simultaneously. Dynamic weighted loss function and Bayesian optimization algorithm are used to adaptively optimize the deep learning model. The results demonstrate that the AOMB-CNN can effectively improve the performance of multi-component regression on 2 LIBS datasets compared with the traditional quantitative analysis methods. The root mean square error (RMSE) of 8 components of the ChemCam test set are 2.4465, 0.3486, 1.2481, 1.3909, 0.9010, 0.8892, 0.6700, and 0.5498 respectively, and the RMSE of 4 components of the steel test sets are 1.9787, 0.3455, 0.6025 and 1.6631 respectively. The proposed method provides an end-to-end, adaptively optimized, full-spectrum deep learning solution for LIBS multi-component content measurement without human intervention.

Highly-fluorescent extracts from Pterocarpus wood for Fe3+ ion detection

At present, excessive Fe3+ in daily water has become a threat to human health. Among the conventional detection methods for Fe3+, fluorescent probes have been applied on a large scale due to their simplicity and efficiency. However, the currently available fluorescent probes are difficult to synthesize, costly and environmentally unfriendly, limiting their applications. In this work, a fluorescent extract of Pterocarpus wood was successfully obtained, and the structure of some coumarin-based molecules in this extract was determined by 2D-NMR. Subsequently, the intensity of this fluorescence was optimized using response surface methodology (RSM), resulting in a high-intensity fluorescent probe. The probe was sensitive to the concentrations of Fe3+ and MnO4−, and could efficiently detects Fe3+ in the range of 2.7 μM–8.0 μM, with LOD and LOQ reaching 1.06 μM and 3.20 μM, respectively. Moreover, based on the strong complexation property of EDTA on Fe3+, this work designed the "switch-on" fluorescent probes. The experiment shows that both static and dynamic quenching exist in this system. The mechanism of complexation and oxidation of fluorescent molecules by the quencher is interpreted in the quenching reaction. In addition, the fluorescent probe has a high yield and low cost, it also performs well in actual water sample tests. This method is expected to be developed as a new way on Fe3+ detection.

Interface engineering to construct heterostructured SnS2/Sb2S3 on rGO through a targeted-complexation deposition strategy for high-performance lithium-ion capacitors

Accurate synthesis of electrode materials with homogeneous heterostructure is an important approach to eliminate the energy storage dynamics discrepancy between the anode and cathode of lithium-ion capacitors (LICs). However, the complex synergistic effects between two electrodes is yet to materialize due to the lack of effective approach. Herein, a targeted-complexation deposition strategy is proposed to prepare uniform SnS2/Sb2S3 heterostructure attached to reduced graphene oxide (rGO) (SnS2/Sb2S3@rGO) with the aid of L-cysteine. The strong complexation between L-cysteine and metal ions effectively suppresses the hydrolysis reaction of Sb3+ ion and guides the uniform deposition of SnS2/Sb2S3 heterostructures on rGO. Due to the charge redistribution and mutual-support effect, the lithium storage properties of SnS2/Sb2S3@rGO can be integrated and greatly enhanced. Furthermore, a three-dimensional conductive network consisting of polyaniline and rGO heterostructure is synthesized by a self-assembly strategy, which is able to deliver a superior capacity of 178.6 mA h g−1 as well as outstanding rate property as a cathode. The LICs assembled with heterogeneous cathode and anode materials demonstrates an exceptional energy density (reaching 298 W h kg−1), impressive power density (58.5 kW kg−1) and remarkable capacity maintenance (78 %) even over 6000 cycles, providing a valuable guide for producing the high-performance LICs.

Measurement of total amine site concentrations on bacterial cell surfaces using selective site-blocking and potentiometric titrations

Bacterial cell surface amine binding sites can form strong complexes with both toxic metal cations and anionic contaminants, thereby affecting the behavior of these contaminants in both natural and engineered systems. In this study, a novel approach was developed to measure the concentration of total amine sites on bacterial cell surfaces by combining selective site-blocking, potentiometric titrations and surface complexation modeling. Our controls show that the amine blocker sulfo-N-hydroxysulfosuccinimide acetate (SNHS) selectively reacts with primary and secondary amine sites but does not react with other binding sites on the bacterial cell surface. Amine site concentrations on bacterial cell surfaces, therefore, can be measured as the difference between the total site concentrations of un-blocked and SNHS-blocked bacterial samples. We measured amine site concentrations on Bacillus subtilis and Pseudomonas putida of 52 ± 5 and 78 ± 6 μmol/g, respectively, which account for 24% and 32% of the total binding sites on each bacterial surface. Our results suggest that amine sites can be present in relatively high concentrations on bacterial surfaces, and further studies that are focused on the role of cell surface amine sites on the transport and bioavailability of toxic metal cations and anionic contaminants are crucial.

Field study on vegetation eco-protection technology for red sandstone fill slope against water damage

The construction of fill slopes becomes a critical aspect when there is a need to change the terrain or create new terrain. However, due to the poor engineering properties of the fill material, especially when red sandstone with notable disintegration properties is used, the risk of slippage or collapse may occur. This material is prone to erosion and disintegration under the action of natural factors such as heavy rainfall, leading to severe soil erosion and slope instability. In addition, the construction of fill slopes inevitably causes the destruction of native vegetation, exacerbating environmental problems. To address these problems, an novel ecological approach for preventing water damage to red sandstone fill slopes was developed using the vegetation–high-performance turf reinforcement mat –anchor–drainage pipe–synergistic slope protection system. Three test red sandstone slopes with different protection methods (unprotected, three-dimensional (3D) protection mesh, and vegetation ecological protection system slopes) were constructed, and the feasibility and reliability of ecological protection against water damage to red sandstone fill slopes were analysed via the field test method. The results showed that the vegetation ecological protection system can effectively inhibit soil erosion and increase the survival rate of vegetation roots. Moreover, the the high-performance turf reinforcement mat provides a strong protective complex through interactions with vegetation roots, anchors, and drains, which significantly enhances slope stability. Under heavy rainfall conditions, the vegetation ecological protection system can effectively limit slope erosion due to water scour, thus maintaining the structural integrity of the slope.

Thermoplastic polyimide with good comprehensive performance based on carbazole groups and short flexible linkages

AbstractThermoplastic polyimides (TPIs) can meet the requirements of advanced electronic packaging such as flexible copper clad laminate (FCCL) applied under extreme conditions, attributed to their excellent thermal stability, mechanical properties, electrical insulation and chemical resistance. With the increasing demands for electronic devices, such as applications in high‐frequency communication, it is highly desirable to develop high‐performance TPI with good thermoplasticity, thermal stability, mechanical properties and dielectric properties. However, there is a trade‐off between thermoplasticity and other properties. Herein, we introduce an effective strategy to afford TPIs with good performance by copolymerization of a diamine monomer consisted of a coplanar aromatic carbazole group and a short flexible linkage methylene group. On the one hand, short flexible linkages help improve the flexibility of polymer chains and are not too much to enable small‐scale molecular motions below Tg. On the other hand, coplanar aromatic donor groups benefit to strong charge transfer complex (CTC) interaction and π‐π stacking interaction. Based on this strategy, the resultant TPI possesses good thermoplasticity with a proper Tg of 348 °C. Meanwhile, it preserves a low coefficient thermal expansion of 48 ppm K−1, low dielectric constant/dielectric loss factor of 3.27/0.0073 at 10 GHz, and tensile strength of ca. 78 MPa.

Short-term industrial load forecasting based on error correction and hybrid ensemble learning

Accurate industrial load forecasting is a prerequisite for ensuring the smooth operation of the power system. Due to the strong fluctuation and complex characteristics of industrial loads, it is difficult to accurately predict short-term power demand. To address this issue, this paper proposes a deep learning prediction model based on hybrid ensemble and error correction. The proposed model is divided into two phases: in the first phase, deep power features are extracted from multivariate data through a hybrid ensemble strategy consisting of Random Subspace, Boosting, Ensemble Pruning, and Multi-Objective Molecular Dynamics Theory Optimization Algorithm (MMDTOA). First, the strategy splits high-dimensional industrial data into multiple sub-datasets. Subsequently, for the features of each sub-dataset, the proposed MMDTOA is applied to perturb the parameters of GRU to generate base learning machines that balance accuracy and diversity. Finally, these base learning machines are integrated by kernel ridge regression stacking. Among them, the two-stage selection strategy and co-evolutionary strategy are embedded into the MMDTOA to enhance the optimization searching effect; in the second stage, a combined error correction strategy is proposed by utilizing the residual information in the prediction results. By combining the dynamic Gaussian error correction function and GRU error correction model, the prediction accuracy of the ensemble model is further improved; Experimental results on real Korean datasets show that the proposed method achieves a minimum value of 3.684% and 7.266% in NMAE and NRMSE, which outperforms eight comparative models, such as SVM, ELM, and CNN, with higher accuracy and robustness.

Construction of highly robust amino orange peel cellulose for Cu2+ adsorption study

ABSTRACT In this paper, a kind of cellulose aerogel (HRCNFs) with high mechanical robustness and durability was prepared by using orange peel cellulose and polyethylenimine, and it was used for high-efficiency adsorption of Cu (II). Fourier analysed the structure and morphology of HRCNFs transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM), and the pore structure characteristics of the materials were evaluated by BET. The adsorption capacity of HRCNFs as adsorbent to remove Cu (II) ions from aqueous solution was further studied. The maximum adsorption capacity was 167.21 mg/g. Adsorption is mainly a single-layer chemisorption process. Based on FTIR and BET analysis, the adsorption mechanism is the strong surface complexation of amino, carboxyl, and hydroxyl groups with Cu (II). Through many cycles, HRCNFs retain their integrity and high mechanical robustness. Therefore, the synthesised HRCNFs material can be considered a promising adsorbent for the removal of Cu (II) ions from polluted water bodies.

Strong digital topological complexity of digital maps

In the paper, we study a digital topological complexity of a digital map and its properties. Firstly, we discuss a strong digital homotopy which allows iterative algorithms based on our previous work. As a generalization of the topological complexity in terms of the strong digital homotopy (we call it strong digital topological complexity), we next study the strong digital f-sectional category of a strong digital fibration. Then we investigate estimates of the upper and lower bounds for the strong digital topological complexity of digital maps. We also reveal the difference between the strong digital topological complexity and the ordinary digital ones. It has shown that the strong digital topological complexity is more similar to the classical continuous case than the ordinary digital ones. Moreover, arising from practical considerations in robotics, we consider the naive digital topological complexity of digital maps.

Investigation on structural, optical, thermal, and dielectric properties of nanocomposite films based on chitosan containing vanadium pentoxide/zinc oxide and their potential for optoelectronics devices

Chitosan (Cs), zinc oxide (ZnO), and vanadium pentoxide (V2O5) nanoparticles (NPs) have been used to create ternary nanocomposite materials to improve their optical and electrical characterization. Physical methods have been used to investigate the effects of varying vanadium pentoxide nanoparticle contents on the optical and electrical characterization of the prepared films. In XRD results obtained, the characteristic peaks of pure chitosan are seen to be broken and vanished (especially at 15 wt.% V2O5 NPs). This means that when chitosan is filled with different concentrations of V2O5 nanoparticles, chitosan is very weak and simply broken, and only characteristic peaks of V2O5 appear. The average crystal size of V2O5 has been determined to be 25.4 nm. FTIR spectra approve the strong complexation between the nanoparticles and hydrogen bonds in the composite. The study suggests that the addition of zinc and vanadium nanoparticles to chitosan-based films can improve their thermal stability, which has potential applications in various industries, including packaging, biomedical, and environmental. Electrical studies indicate that as the content of embedded zinc/vanadium-filled-in chitosan rises, the ε′ value decreases. The observed decrease in the insulating behavior of chitosan aligns with the notion that doping reduces its insulating properties. This aligns with the enhanced electrical conductivity evident from the DC conductivity measurements. The study demonstrates that incorporating zinc oxide (ZnO) and vanadium pentoxide nanoparticles effectively influences the dielectric properties and relaxation behavior of chitosan. The given results suggest Ternary nano-composite chitosan-ZnO/V2O5 films for specific requirements in optical, optoelectronic applications.

Gas-bearing prediction in tight sandstone reservoirs based on multinetwork integration

Tight sandstone reservoir gas is an important component of unconventional natural gas exploration and production in China. However, tight sandstone reservoirs have the characteristics of poor lateral continuity, strong vertical heterogeneity, complex lithology, and large changes in physical properties, which means traditional oil and gas evaluation sometimes suffers from low accuracy. Therefore, a method for predicting the gas content of tight sandstone reservoirs is developed. This method selects seismic attributes through Pearson coefficients, combines multiple attribute information, and inputs it into a deep neural network. This study constructed MultipleNet by combining a convolutional neural network, a bidirectional gated neural unit network, and a self-attention mechanism. This network takes advantage of the complementary advantages of the preceding network modules and can more effectively mine information on various seismic attributes and improve gas-bearing prediction accuracy. This method is applied to actual data from a tight sandstone gas exploration area in the Sichuan Basin. Experimental results indicate that the results of well-side predictions using this method are consistent with well data, providing a new approach and perspective for predicting gas bearing in tight sandstone reservoirs.

Homopolymers and copolymers with iminodiacetic acid chelating units for scale inhibition

Novel homopolymer and copolymer scale inhibitors with iminodiacetic acid chelating units were synthesized for inhibiting calcium carbonate and calcium sulfate precipitation. Static scale inhibition tests of SI-1 demonstrated excellent performance, with inhibition performance of 83 % at a concentration of 30 mg/L for CaCO3 and >99 % at a concentration of 8 mg/L for CaSO4. These novel inhibitors surpassed the performance of a commercially available industrial scale inhibitor, 2-phosphonobutane-1,2,4-tricarboxylic acid. The homopolymer significantly altered CaCO3 and CaSO4 crystal structures and reduced crystal size at low concentrations. The primary mechanism of inhibition appears to be a two-fold effect: strong and specific complexation between the iminodiacetic acid chelating units and free Ca2+ ions, coupled with the adsorption of the polymers on the crystal surfaces. These promising scale inhibitors offer a potential replacement for phosphorus-containing compounds currently used in industrial applications.

Catalytically Active SiO2 Aerogels Comprising Chelate Complexes of Palladium.

A series of silica-based aerogels comprising novel bifunctional chelating ligands was prepared. To produce target aerogels, two aminosilanes, namely (3-aminopropyl)trimethoxysilane (APTMS) and N-(2-aminoethyl)-3-aminopropyltrimethoxysilane (AEAPTMS), were acylated by natural amino acids ((S)-(+)-2-phenylglycine or L-phenylalanine), followed by gelation and supercritical drying (SCD). Lithium tetrachloropalladate was used as the metal ion source to prepare strong complexes of Pd2+ with amino acids covalently bonded to a silica matrix. Aerogels bearing chelate complexes retain the Pd2+ oxidation state after supercritical drying in CO2, but the Pd ion is reduced to Pd metal after SCD in isopropanol. Depending on the structure of amino complexes, Pd-containing aerogels showed catalytic activity and selectivity in the hydrogenation reactions of C=C, C≡C and C=O bonds.

Sorption of the Cu, Gd, V, Mn, and Fe elements by hydrogels with identification of their complexes in the solid phase of the polymer ligand using EPR

The search for new sorbents for the concentration of substances is among the important tasks of analytical chemistry regarding the monitoring of water bodies in modern times. It is necessary to regularly control water supply sources, and the quality of natural water is also an indicator of the health of ecosystems. The use of sorbents when collecting water for analysis simplifies the preparation of complex samples, allowing to use a solid concentrate to determine the absorbate and quickly record the results using multi-element in situ instrumental methods. We suggested using cross-linked polyacrylates (CLPs) based on polyacrylamide as a new type of sorbent for these purposes. Unlike traditional sorbents, hydrogels noticeably swell in water (рН 4-7), which allows conducting the sorption of elements not only on the surface but also inside CLP granules. Sorption is conducted in a static mode by placing a weighed portion of CLP in a certain volume of water sample and by further drying in air at 70-100°C. The purpose of this work was to obtain and study solid polymer concentrates of CLP using EPR spectroscopy in order to confirm the sorption of elements in CLP for the further development of methods for the preparation of samples of various water bodies. To do this, we obtained individual and binary polymer complexes of paramagnetic elements Cu(II), Gd(III), V(IV), Mn(II), and Fe(III) from model solutions of their salts (from 10-7 (INAA) to 10-3 (EPR) mol/dm3) and dried them To confirm the sorption and reliable binding of elements into polymer complexes, we used ESR spectroscopy at 293 K. The experimentally obtained new EPR spectra were compared with the theoretical ones. Complex EPR spectra were simulated using original software developed at the Kurnakov Institute of General and Inorganic Chemistry of the Russian Academy of Sciences. It was shown that upon drying CLPs completely disengaged from water molecules, while the sorption of metals depended on the рН and grew with an increase in the solution’s рН. In case of joint presence, metal ions formed more complex compounds as compared to individual sorption. The hyperfine structure constant (HSC) was calculated for the spectra of complexes with CLPs and g-factor was calculated for all samples. It was determined that in the course of the formation of metal complexes with CLPs, the nearest coordination sphere was greatly distorted, while the nature of the distortion of the ligand environment depended on the element. In case of the Cu(II) and V(IV) system, we observed additivity of the EPR spectra, but in case of Cu and Gd(III) it was not recorded. In any case, the formation of strong and stable polymer complexes was confirmed. Sorption concentration using CLPs was proposed as a method of sample preparation of natural and technogenic water systems by transferring the determined ions into a solid hydrogel concentrate in order to be further analysed using multi-element instrumental methods (for example, X-ray fluorescence analysis (XRF), instrumental neutron activation analysis (INAA), etc.).

Optimization of chaotic light output in semiconductor laser systems based on multi-objective optimization algorithm.

Aiming at the weak performance of chaotic light output in semiconductor laser systems, the study designed a power control algorithm for semiconductor laser drive systems based on linear self-disturbance rejection control. Then the optimization parameters and scope were determined, and multi-objective optimization and direction preference algorithms were introduced. A chaotic optical performance optimization model based on improved multi-objective genetic algorithm was constructed using adaptive functions as evaluation indicators. These results confirmed that the larger the bandwidth of the controller, the faster the response speed of the resonant converter, but the stability was poor. When the input voltage underwent a sudden change, the current ripple coefficient of the PID algorithm was 0.55%. The linear active disturbance rejection control algorithm could ensure that the voltage and current maintained at the set values, and the output current of the algorithm was more stable when the load underwent sudden changes. The directional preference algorithm could further provide more valuable solutions on the basis of adaptive genetic algorithms. When the peak value of the autocorrelation function was equal to 0.2, the delay characteristics of chaotic light were effectively suppressed, having strong signal bandwidth and complexity. In summary, the constructed model has good application effects in optimizing chaotic optical performance and has certain positive significance for communication security.

Construction of multilayer carbon magnetic heterostructures on the surface of carbon fibers to improve the absorption performance

AbstractThis study utilizes the strong complexation effect of polydopamine on metal ions and constructs multilayer carbon magnetic heterostructures on the surface of carbon fibers through the in situ reduction method, thereby obtaining a new type of multilayer carbon‐based composite absorbing material. The absorption properties at different layers were analyzed and compared through various characterization methods. Finally, the double‐layer structure demonstrated outstanding electromagnetic wave absorption characteristics, exhibiting a wide effective absorption bandwidth of 7.24 GHz and robust absorption of −44.48 dB, all within a mere thickness of 3.5 mm. Therefore, excellent market prospects for this material are anticipated.Highlights A multilayer carbon magnetic heterostructure absorbing material was constructed on micrometer‐scale carbon fibers. The test results show that a wide effective absorbing bandwidth of 7.24 GHz and a minimum reflection loss of −44.48 dB are obtained when the composite thickness is 3.5 mm. The prepared multilayer absorbing composites can adapt to a variety of application scenarios and have excellent application prospects in the industry. The self‐polymerization of dopamine and complexation of metal ions were used to construct multilayer absorbing materials, and the dielectric loss and magnetic loss capacity of the carbon fibers improved.