Multilevel Structure Research Articles

The influence mechanism of multi-government environmental regulation synergy on corporate environmental responsibility

The United Nations Development Programme (UNDP) calls for enhanced global efforts to achieve the 17 sustainable development goals by 2030, highlighting the crucial role of corporate environmental responsibility (CER) in advancing global sustainability. As a significant participant, China is leveraging its government-led environmental regulation (ER) system, which has a multi-level and multi-sectoral structure, to effectively combat climate change. However, the effect of this ER system, especially collaborative efforts among multiple government subjects, on CER remains unknown. Thus, this paper is the first attempt to investigate the effect and underlying mechanisms of multi-government ER synergy on CER from the perspective of government subject synergy. Using a dataset of Chinese A-share listed manufacturing firms from 2010 to 2021, this study finds the following. First, multi-government ER synergy boosts CER. Direct ER synergy, stemming from joint policy-making across departments, encourages CER in end-of-pipe governance. Indirect ER synergy, arising from administrative hierarchy constraints among multi-level governments, fosters CER in front-end governance. Second, financing constraints and executives’ attention to CER are two key channels through which multi-government ER synergy influences CER. Third, firms with political connections, those in heavily polluting industries, or those located in central and western regions adopt more proactive CER strategies under multi-government ER synergy. This paper expands research on government collaborative governance from the regional macro-perspective to the micro-firm level, providing valuable insights for countries worldwide, particularly emerging economies that share similar characteristics with China, to contain rapidly growing environmental challenges and advance global sustainability.

Biomimetic dendritic‐networks‐inspired oil-water separation fiber membrane based on TBAC and CNC/PVDF porous nanostructures

Inspired by the multi-level structure of trees in nature, using cellulose nanocrystals/polyvinylidene fluoride (CNC/PVDF) fibers as the support layer and CNC/PVDF/organic branched salt (TBAC) fibers as the filtering layer, to construct oil–water separation membrane with tree-like structure. The membrane's surface (nano spherical) and internal (interconnected pores) structures were regulated by phase separation of CNC induced. The high conductivity of TBAC was utilized to construct branched fibers (d = 53.3 nm) on the backbone fibers (d = 1.2 μm). The impact of TBAC on the microstructure, wetting behavior, and oil–water separation performance of membranes was investigated. Note, TBAC concentration was 0.02 mol/L, the membrane exhibits a highly porous dendritic network structure (with porosity of 85 % and pore size of 6.1 nm), and outstanding wetting properties (water contact angle underoil ˃ 150°). This distinctive structure endows membranes with a high oil adsorption capacity (64.1 g/g of engine oil), oil flux (22873 L/m−2h−1 of toluene). Following ten testing cycles, the toluene adsorption capacity remains at 83.1 % of the initial adsorption, while the separation flux of emulsion (5645 L/m−2h−1) remains nearly constant. Overall, successfully fabricating CNC/PVDF/T-0.02 fibers with a tree-like structure provides a novel approach to designing and developing efficient membrane separation materials.

How multi-level porous carbon structure affects electrocatalytic properties?

For zinc-air batteries, it is crucial to develop bifunctional electrocatalysts that exhibit exceptional activity, high stability, and low cost. Based on this, three-dimensional carbon-based catalysts with multiple pore sizes were successfully prepared by using three different combinations of soluble salts as hard templates for controlling morphology and structure. These catalysts are denoted as 3D Co/NCBMS. Because of the synergistic effect of heteroatomic doping and a multi-level porous structure, 3D Co/NCBMS exhibits better catalytic properties for OER and ORR, comparable to IrO2 and Pt/C. The half-wave potential of the 3D Co/NCBMS catalyst was 0.82 V (vs RHE) in the ORR reaction and 1.54 V in the OER reaction. 3D Co/NCBMS samples exhibit excellent stability, maintaining a charge and discharge efficiency of approximately 54 % after stable cycling for 225 h. It also demonstrates high resistance to methanol poisoning, with the current remaining at around 95 % in alkaline electrolyte solutions when assembled into zinc-air batteries.

Independence in Europe: Regionalist Party Rhetoric and the EU in a Post-Brexit United Kingdom

Abstract In the wake of Brexit, how has the framing of the EU and independence evolved among regionalist parties in the devolved regions of the UK? The effects of a multilevel structure such as the EU on regionalist parties has been examined, yet European disintegration is novel and thus yet to be fully explored. Has the framing of the EU by these parties shifted, and if so, how? In this article, I analyze 19 regionalist party manifestos between 2011 and 2022 through the lens of rational choice and discursive institutionalism to examine the effects of Brexit on the framing of the EU. At a rhetorical level, these parties have engaged in the subsuming of the EU, rhetorically tying their independence or enhanced autonomy to Brexit. Sinn Féin, Scottish Nationalist Party, and Plaid Cymru have used the critical juncture of Brexit to incorporate the EU into their regionalist rhetoric. By examining the effects of European disintegration on regionalist political parties, we can better understand the role that current events play in the fluidity of party positions as presented in manifestos.

Learning dynamics of multi-level spatiotemporal graph data for traffic flow prediction

—Accurate and timely traffic flow prediction can effectively improve road network efficiency and alleviate a series of negative impacts caused by traffic congestion. Recent research has focused on exploring the spatiotemporal correlations of traffic data, and achieved some progress. However, due to the highly nonlinear and stochastic nature of traffic data, the most prediction methods can only learn the fixed physically connected relationships by predefined empirical matrices, but cannot fully capture the spatial correlation, especially the dynamic characteristics. In this paper, we propose a Multiscale Gated Spatiotemporal Graph Convolution Network (MS-GSTGCN). A self-learning adjacency matrix is constructed by the graph fusion module aggregating the information of the adaptive adjacency matrix and the innovative similarity adjacency matrix, which helps to capture the hidden features of the nodes and the dynamic spatial correlation. Meanwhile, a multilevel graph neural structure is designed to extract data features utilizing hierarchical framework, and a gating mechanism is introduced to minimize the accumulation of errors when the information propagates layer by layer, so as to enhance the model's ability to capture complex spatiotemporal correlations in long-term predictions. The experiments on two real world datasets show that it outperforms state-of-the-art related methods.

High performance Cu sintering joint for power devices enabled by in-situ generation of Cu particles with multi-level hierarchical structures

Copper (Cu) particle sintering is widely regarded as one of the most ideal interconnect technologies for wide band gap semiconductors owing to its potential for low-temperature curing while withstanding a higher working temperature. Nevertheless, the strict requirements for sintering conditions and high costs limit the widespread application of Cu sintering technology. In this paper, a simple vehicle was introduced into Cu paste formulation to achieve an effective sinter bonding even using commercially available sub-micron or even micrometer Cu particles (C-Cu particles) with the in-situ generation of composite Cu particles. This innovative vehicle comprises Cu salt, a combination of capping agents and reducing agents. The addition of the hybrid capping agent binds Cu atoms to the surface of the C-Cu particles and allows the generated Cu particles (G-Cu particles) produced from the vehicle to grow on their surface, resulting in the in-situ generation of composite Cu particles. Based on the in-situ generated composite Cu particles, sintering experiments were conducted at designated temperatures (200 °C, 225 °C, 250 °C) for various durations (3 min, 5 min, 10 min) in a N2 atmosphere under different pressures (10 MPa, 15 MPa, 20 MPa). A reliable Cu sintered joint with a high shear strength of 41.3 MPa can be achieved at a low temperature (200 °C) using 500 nm C-Cu particles. Moreover, the joints sintered with large size (>3 μm) particles at 250 °C could also reach 43.3 MPa. The in-situ generated composite Cu particles accomplish the transformation of low-energy interfaces of C-Cu/C-Cu to high-energy interfaces of G-Cu/G-Cu, which in turn facilitates low-temperature sintered connection of C-Cu particles and reliable sintered joints formed of large size C-Cu particles. These findings improve the feasibility of Cu sinter bonding in practical applications.

Current-Prediction-Controlled Quasi-Z-Source Cascaded Multilevel Photovoltaic Inverter

To address problems that traditional two-stage inverters suffer such as high cost, low efficiency, and complex control, this study adopts a quasi-Z-source cascaded multilevel inverter. Firstly, the quasi-Z-source inverter utilizes a unique impedance network to achieve single-stage boost and inversion without requiring a dead zone setting. Additionally, its cascaded multilevel structure enables independent control of each power unit structure without capacitor voltage sharing problems. Secondly, this study proposes a current-predictive control strategy to reduce current harmonics on the grid side. Moreover, the feedback model of current and system state is established, and the fast control of grid-connected current is realized with the deadbeat control weighted by the predicted current deviation. And a grid-side inductance parameter identification is added to improve control accuracy. Also, an improved multi-carrier phase-shifted sinusoidal PWM method is adopted to address the issue of switching frequency doubling, which is caused by the shoot-through zero vector in quasi-Z-source inverters. Finally, the problems of switching frequency doubling and high harmonics on the grid side are solved by the improved deadbeat control strategy with an improved MPSPWM method. And a seven-level simulation model is built in MATLAB (2022b) to verify the correctness and superiority of the above theory.

Structure Design and Heat Transfer Performance Analysis of a Novel Composite Phase Change Active Cooling Channel Wall for Hypersonic Aircraft.

Efficient and stable heat dissipation structure is crucial for improving the convective heat transfer performance of thermal protection systems (TPSs) for hypersonic aircraft. However, the heat dissipation wall of the current TPS is limited by a single material and structure, inefficiently dissipating the large amount of accumulated heat generated during the high-speed maneuvering flight of hypersonic aircraft. Here, a convection cooling channel structure of TPS is proposed, which is an innovative multi-level structure inspired by the natural honeycomb. An active cooling channel (PCM-HC) is designed by using a variable-density topology optimization method and filled with phase change material (PCM). Numerical simulations are used to investigate the thermal performance of the PCM-HC wall, focusing on the influence of PCM properties, structural geometric parameters, and PCM types on heat transfer characteristics. The results demonstrate that the honeycomb-like convection cooling channel wall, combined with PCM latent heat of phase change, exhibits superior heat dissipation capability. With a heat flux input of 50 kW/m2, the maximum temperature on the inner wall of PCM-HC is reduced by 12 K to 20 K. Different PCMs have opposing effects on heat transfer performance due to their distinct thermophysical properties. This work can provide a theoretical basis for the design of high-efficiency cooling channel, improving the heat dissipation performance in the TPS of hypersonic aircraft.

SleepBoost: a multi-level tree-based ensemble model for automatic sleep stage classification.

Neurodegenerative diseases often exhibit a strong link with sleep disruption, highlighting the importance of effective sleep stage monitoring. In this light, automatic sleep stage classification (ASSC) plays a pivotal role, now more streamlined than ever due to the advancements in deep learning (DL). However, the opaque nature of DL models can be a barrier in their clinical adoption, due to trust concerns among medical practitioners. To bridge this gap, we introduce SleepBoost, a transparent multi-level tree-based ensemble model specifically designed for ASSC. Our approach includes a crafted feature engineering block (FEB) that extracts 41 time and frequency domain features, out of which 23 are selected based on their high mutual information score (> 0.23). Uniquely, SleepBoost integrates three fundamental linear models into a cohesive multi-level tree structure, further enhanced by a novel reward-based adaptive weight allocation mechanism. Tested on the Sleep-EDF-20 dataset, SleepBoost demonstrates superior performance with an accuracy of 86.3%, F1-score of 80.9%, and Cohen kappa score of 0.807, outperforming leading DL models in ASSC. An ablation study underscores the critical role of our selective feature extraction in enhancing model accuracy and interpretability, crucial for clinical settings. This innovative approach not only offers a more transparent alternative to traditional DL models but also extends potential implications for monitoring and understanding sleep patterns in the context of neurodegenerative disorders. The open-source availability of SleepBoost's implementation at https://github.com/akibzaman/SleepBoost can further facilitate its accessibility and potential for widespread clinical adoption.

A Generic Multilevel Structure for Educational Escape Rooms

The use of active learning activities for evaluation purposes has been reported to improve results in all areas within the education field. In this paper we describe a generic multilevel structure for educational escape rooms, along with a use case where such a design was applied for assessment in a course within the STEM area. Furthermore, a project-based learning activity was also added to complement that assessment. The results obtained expose an increase in both academic performance and success rate, where the percentage rises in both cases go in line to the literature. Additionally, a high level of engagement was measured during those active learning activities, which is reported to lead to better performance and improved learning. Hence, this high engagement seems to be the primary source of the increments experienced.

Identification of the continuum field structure at multiple scale levels.

For continuum fields such as turbulence, analyses of the field structure offer insights into their kinematic and dynamic properties. To ensure the analyses are quantitative rather than merely illustrative, two conditions are essential: space-filling and structure quantification. A pertinent example is the dissipation element (DE) structure, which is however susceptible to noisy interference, rendering it inefficient for extracting the large-scale features of the field. In this study, the multi-level DE structure is proposed based on the multi-level extremal point concept. At a given scale level, the entire field can be decomposed into the corresponding space-filling and non-overlapping DEs, each characterized by its length scale l and the scalar difference Δϕ between its two extremal points. We will first elaborate on the fundamental principles of this method. Results from an artificially constructed two-scale field indicate that the decomposed units adequately represent the geometry of the original field. In examining the fractal Brownian motion, a structure function equivalent ⟨Δϕ|l⟩ and an energy spectrum equivalent are introduced. The scaling relation derived from ⟨Δϕ|l⟩ corresponds with the Hurst number. Furthermore, the multi-level DE structure distinctly reveals the two different inertial ranges in two-dimensional turbulence. Overall, this novel structure identification approach holds significant potential for complex analyses concerning the field geometry.

Mechanism of the solubility increase of rice protein-ovalbumin co-assembly and its great potential as an emulsifier in high internal phase emulsion

The functionality (e.g., emulsifying ability) of rice protein (RP) is restricted largely by its low solubility. The formation of a co-assembly with RP and an exogenous protein through pH-cycle is one promising method, but the mechanism behind this has not been revealed. This study utilized ovalbumin (OVA) to improve the solubility of RP and investigated the effect of OVA on the physicochemical characteristics of RP, the interactions between RP-OVA co-assembly and water, the RP-OVA interactions, the key subunits related to the solubility improvement of RP, and the emulsifying capacity of the co-assembly. The formation of RP-OVA co-assembly considerably improved the solubility of RP (up to 73.12%), and the multi-level structure and surface property of RP were altered by OVA. The mechanism for the solubility increase was the hydrophobic interactions between OVA and RP, resulting in the exposure of some hydrophilic amino acids to protein surface and further generating a low surface hydrophobicity that allow more water molecules to permeate. Eight subunits in RP were critical for its solubility increase. The RP-OVA co-assembly prepared in this study showed an emulsifying ability even better than whey protein isolate (a commercial emulsifier) in high internal phase emulsion. This study may guide the solubility improvement of RP and widen the application of RP in the food industry as an emulsifier.

Study on Thermodynamic Coupling Performance of Multi-Level Lattice Structures

With the development of hypersonic vehicle, the design of heat-resistant and load-bearing surface of hypersonic vehicle has gradually become a key technology affecting its combat effectiveness. As a new type of lightweight and high-strength structure, lattice structure has excellent mechanical properties such as ultra-light, high specific strength and high specific stiffness, and special properties such as vibration reduction, heat dissipation and sound absorption. Among them, the multi-level lattice structure can increase the bearing efficiency of the structure at low relative density, and increase the heat transfer area at the same time, so as to improve the heat protection and bearing capacity of the structure. This study includes three aspects of high temperature out-of-plane compression test, high temperature numerical simulation comparison, and structural optimization for the designed multi-level pyramid lattice, in order to obtain the bearing capacity of the multi-level pyramid lattice at 350°C, and further improve the bearing performance by optimizing the core structure. Through experiments and verification, the multi-level pyramid lattice designed and optimized in this study can further meet the requirements of thermal protection and load-bearing performance of aircraft compared with the single-level pyramid lattice, and can provide a reference for the study of high temperature mechanical properties of lattice structures, the design of new multi-level lattice structures and the engineering application of lattice structures.

Nonclassical crystallization of goethite nanorods in limpet teeth by self-assembly of silica-rich nanoparticles reveals structure–mechanical property relations

The intricate organization of goethite nanorods within a silica-rich matrix makes limpet teeth the strongest known natural material. However, the mineralization pathway of goethite in organisms under ambient conditions remains elusive. Here, by investigating the multi-level structure of limpet teeth at different growth stages, it is revealed that the growth of goethite crystals proceeds by the attachment of amorphous nanoparticles, a nonclassical crystallization pathway widely observed during the formation of calcium-based biominerals. Importantly, these nanoparticles contain a high amount of silica, which is gradually expelled during the growth of goethite. Moreover, in mature teeth of limpet, the content of silica correlates with the size of goethite crystals, where smaller goethite crystals are densely packed in the leading part with higher content of silica. Correspondingly, the leading part exhibits higher hardness and elastic modulus. Thus, this study not only reveals the nonclassical crystallization pathway of goethite nanorods in limpet teeth, but also highlights the critical roles of silica in controlling the hierarchical structure and the mechanical properties of limpet teeth, thus providing inspirations for fabricating biomimetic materials with excellent properties.

Detecting differential item functioning in presence of multilevel data: do methods accounting for multilevel data structure make a DIFference?

Assessment practices are, among other things, concerned with issues of fairness and appropriate score interpretation, in particular when making claims about subgroup differences in performance are of interest. In order to make such claims, a psychometric concept of measurement invariance or differential item functioning (DIF) ought to be considered and met. Over the last decades, researchers have proposed and developed a plethora of methods aimed at detecting DIF. However, DIF detection methods that allow multilevel data structures to be modeled are limited and understudied. In the current study, we evaluated the performance of four methods, including the model-based multilevel Wald and the score-based multilevel Mantel–Haenszel (MH), and two well-established single-level methods, the model-based single-level Lord and the score-based single-level MH. We conducted a simulation study that mimics real-world scenarios. Our results suggested that when data were generated as multilevel, mixed results regarding performances were observed, and not one method consistently outperformed the others. Single-level Lord and multilevel Wald yielded best control of the Type I error rates, in particular in conditions when latent means were generated as equal for the two groups. Power rates were low across all four methods in conditions with small number of between- and within-level units and when small DIF was modeled. However, in those conditions, single-level MH and multilevel MH yielded higher power rates than either single-level Lord or multilevel Wald. This suggests that current practices in detecting DIF should strongly consider adopting one of the more recent methods only in certain contexts as the tradeoff between power and complexity of the method may not warrant a blanket recommendation in favor of a single method. Limitations and future research directions are also discussed.

Factor Structure, Reliability, and Convergent Validity of an Ecological Momentary Assessment Binge-Eating Symptoms Scale.

Ecological momentary assessment (EMA) of binge-eating symptoms has deepened our understanding of eating disorders. However, there has been a lack of attention on the psychometrics of EMA binge-eating symptom measures. This paper focused on evaluating the psychometric properties of a four-item binge-eating symptom measure, including multilevel factor structure, reliability, and convergent validity. Forty-nine adults with binge-eating disorder and/or food addiction completed baseline questionnaires and a 10-day EMA protocol. During EMA, participants completed assessments of eating episodes, including four binge-eating symptom items. Analyses included multilevel exploratory factor analysis, computation of omega and intraclass correlation coefficients, and multilevel structural equation models of associations between contextual factors and binge-eating symptoms. A one within-subject factor solution fit the data and showed good multilevel reliability and adequate within-subjects variability. EMA binge-eating symptoms were associated with baseline binge-eating measures as well as relevant EMA eating characteristics: including greater unhealthful food and drink intake; higher perceived taste of food; lower likelihood to be planned eating; and lower likelihood of eating to occur at work/school and other locations and greater likelihood to occur at restaurants compared to home. In conclusion, the study findings support the psychometrics of a 4-item one-factor EMA measure of binge-eating symptoms.

Metal–organic framework–derived trimetallic particles encapsulated by ultrathin nitrogen-doped carbon nanosheets on a network of nitrogen-doped carbon nanotubes as bifunctional catalysts for rechargeable zinc–air batteries

Economical oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) bifunctional catalysts with high activity aimed at replacing precious metal catalysts for rechargeable zinc-air batteries (ZABs) must be developed. In this study, a multiple hierarchical-structural material is developed using a facile dielectric barrier discharge (DBD) plasma surface treatment, solvothermal reaction, and high-temperature carbonization strategy. This strategy allows for the construction of nanosheets using nitrogen-doped carbon (NC) material-encapsulated ternary CoNiFe alloy nanoparticles (NPs) on a network of NC nanotubes (NCNTs), denoted as CoNiFe–NC@p–NCNTs. Precisely, the presence of abundant CoNiFe alloy NPs and the formation of M–N–C active sites created by transition metals (cobalt, nickel, and iron) coupled with NC can provide superior OER/ORR bifunctional properties. Moreover, the prepared NC layers with a multilevel pore structure contribute to a larger specific surface area, exposing numerous active sites and enhancing the uniformity of electron and mass movement. The CoNiFe0.08–NC@p–NCNTs show remarkable dual functionality for electrochemical oxygen reactions (ORR half-wave potential of 0.811 V, limiting current density of 5.73 mA cm−2 measured with a rotating disk electrode at a rotation speed of 1600 rpm, and OER overpotential of 351 mV at 10 mA cm−2), which demonstrates similar ORR performance to 20 wt% Pt/C and better OER performance than the commercial RuO2. A liquid ZAB prepared using the proposed material has excellent bifunctionality with an open-circuit voltage of 1.450 V and long-term cycling stability of 230 h@10 mA cm−2.

Multistage structural self-supported electrode via structural engineering on carbon nanofibers for efficient hydrogen evolution reaction at high current density

Pt-based catalysts suffered from the wastage of active sites, resulting in unsatisfactory actual catalytic performance and poor durability as non-self-supporting catalysts for alkaline hydrogen evolution reaction (HER). In this work, we designed a self-supporting electrode material with multi-level structure that utilized carbon nanofibers anchored with cobalt to load and stabilize Pt nanoparticles (Pt@Co/CNFs) for achieving high activity and stability in alkaline HER. The carbon nanofibers served as a framework, with cobalt nanoparticles as an electron donor that can transfer electrons to the metal Pt to accelerating water splitting. In addition, the morphology of the multi-level structure surface was controlled by the content of the reducing agent to giving the material excellent superhydrophilic/underwater superaerophobic properties with ultra-low Pt load (0.29 mg cm−2), which was beneficial for HER. HER test results showed that Pt@Co/CNFs exhibited great catalytic activity, with η10 = 15 mV and η100 = 101 mV. Besides, the stable mechanical structure and the stability of the designed catalytic surface and electron donor in the multi-level structure gave the material exceptional stability. During the 100-hour stability test at 100 mA cm−2, the voltage change was only 50 mV. Besides, utilizing the Pt@Co/CNFs self-supporting electrode directly in the AEM water electrolysis system, a cell voltage as low as 2.18 V was achieved at 500 mA cm−2. Impressively, it demonstrated operational stability for over 200 h without noticeable decay. This work provides a promising approach for the development of advanced self-supporting electrode catalysts.

Re-Entrant Green Scheduling Problem of Bearing Production Shops Considering Job Reworking

To solve various reworking and repair problems caused by unqualified bearing product quality inspections, this paper introduces a green re-entrant scheduling optimization method for bearing production shops considering job reworking. By taking into account quality inspection constraints, this paper establishes an integrated scheduling mathematical model based on the entire processing–transportation–assembly process of bearing production shops with the goals for minimizing the makespan, total carbon emissions, and waste emissions. To solve these problems, the concepts of the set of the longest common machine routes (SLCMR) and the set of the shortest recombination machine combinations (SSRMC) were used to propose the re-entrant scheduling optimization method, based on system reconfiguration, to enhance the system stability and production scheduling efficiency. Then, a multi-objective hybrid optimization algorithm, based on a neighborhood local search (MOOA-LS), is proposed to improve the search scope and optimization ability by constructing a multi-level neighborhood search structure. Finally, this paper takes a bearing production shop as an example to carry out the case study and designs a series of experimental analyses and comparative tests. The final results show that in the bearing production process, the proposed model and algorithm can effectively realize green and energy-saving re-entrant manufacturing scheduling.

Zif-derived Co@hollow carbon nanofibers boost CO2 chemical fixation

In pursuit of carbon neutrality, the development of highly efficient catalysts for the CO2 cycloaddition with epoxides represents a prominent approach of utilizing CO2 to produce valuable cyclic carbonates. Herein, Co@hollow carbon nanofibers (Co@HCNFs) catalysts with multi-level pore structures were fabricated by coaxial electrospinning, in-situ growth of zeolitic imidazolate framework-67 (ZIF-67) and subsequent high temperature pyrolysis. The hollow structure and the formation of the active sites of Co@N-doped carbon (Co@CN) were regulated by changing the feeding ratio of core to shell and the Co(NO3)2·6H2O concentration during the ZIF-67 growth, thereby fine-tuning the catalyst structures and its catalytic performance. The as-prepared Co@HCNFs-0.6–0.1 catalyst exhibits superior catalytic performance in the CO2 cycloaddition and epichlorohydrin without a co-catalyst, achieving superior specific activity of 838 mmol⋅min−1⋅g−1. The outstanding catalytic performance is attributed to its multi-level pore structures with rich N content and well-distributed Co@CN nanoparticles. Moreover, Co@HCNFs-0.6–0.1 exhibits good stability over ten reaction cycles.