Multilevel Structure Research Articles

The Multi-level Hierarchical Structure Analysis of Influencing Factors of Coal Mine Managers' Blame Avoidance Behavior

BackgroundIn the Chinese coal industry, widespread blame avoidance behavior (BAB) greatly impacts coal mine accidents. Therefore, it is necessary to stop the BAB of coal mine managers and raise the management level of coal mine enterprises for the safe development of Chinese coal industry. MethodsBased on the semi-structured interviews and questionnaire surveys (20 middle-level managers in coal mines), this paper used the Grounded Theory and Nvivo Software qualitative research methodology to open, spindle, and selectively encode the interview data. Then, an index system of factors influencing BAB of coal mine managers was constructed. The influence degree, affected degree, centrality degree, cause degree of each influencing factor were calculated and the hierarchical model of influencing factors of BAB of coal mine managers was established by the DEMATEL-ISM method which is a decision support tool used to evaluate and analyze the interdependencies between influencing factors. ResultsIndex system of factors influencing BAB of coal mine managers included four levels and 12 influencing factors: individual, organizational, institutional environmental, and situational factors. The hierarchical model identified eight causal factors and four consequential factors, of which safety management (15.355), work attitude (14.380), and work group performance (14.281) in the top three of the centrality rankings are the key factors affecting the avoidance behavior of coal mine managers; A 3-level multilevel structure was constructed to reflect the interactions among the factors influencing the BAB of coal mine managers in terms of direct, indirect, and root causes, and corresponding improvement measures were proposed. ConclusionThis study offers a theoretical complement and practical guidance for stopping BAB of coal mine managers.

Protein multi-level structure feature-integrated deep learning method for mutational effect prediction.

Through iterative rounds of mutation and selection, proteins can be engineered to enhance their desired biological functions. Nevertheless, identifying optimal mutation sites for directed evolution remains challenging due to the vastness of the protein sequence landscape and the epistatic mutational effects across residues. To address this challenge, we introduce MLSmut, a deep learning-based approach that leverages multi-level structural features of proteins. MLSmut extracts salient information from protein co-evolution, sequence semantics, and geometric features to predict the mutational effect. Extensive benchmark evaluations on 10 single-site and two multi-site deep mutation scanning datasets demonstrate that MLSmut surpasses existing methods in predicting mutational outcomes. To overcome the limited training data availability, we employ a two-stage training strategy: initial coarse-tuning on a large corpus of unlabeled protein data followed by fine-tuning on a curated dataset of 40-100 experimental measurements. This approach enables our model to achieve satisfactory performance on downstream protein prediction tasks. Importantly, our model holds the potential to predict the mutational effects of any protein sequence. Collectively, these findings suggest that our approach can substantially reduce the reliance on laborious wet lab experiments and deepen our understanding of the intricate relationships between mutations and protein function.

The political economy of electricity market coupling: Comparing experiences from Europe and the United States

Advancing the integration of neighbouring power markets is widely identified in the literature as a key enabler of power system efficiency, flexibility, and variable renewable energy integration. Nonetheless, the existing economic literature offers only partial explanations of why greater market integration is difficult to achieve in the real world despite its evident advantages in terms of overall welfare, supply security, and competitiveness. It is argued that power market coupling is an inherently political process involving significant institutional contestation and adaptation, and hence requires a more nuanced political analysis to be fully understood. This paper adds to the existing literature by conducting a comparative political economy analysis of electricity market coupling processes in Britain, Italy, and California, spanning from 2013 to 2021. It seeks to unpack how differences in the political economy contexts of these jurisdictions influenced the electricity market coupling process with their respective neighbouring systems. The analysis draws on 86 key policy documents, and 53 in-depth interviews with senior power system stakeholders in the three jurisdictions. Results widely align with claims in the political economy literature that market coupling outcomes do not simply reflect the most efficient solution. While all three jurisdictions have shown a commitment to enhance regional integration of short-term wholesale energy markets, there is considerable variation in policy outcomes, particularly the extent to which different segments of the market have been coupled. These differences can largely be attributed to variations in the political economy contexts of the three jurisdictions, including multi-level governance structures, diplomatic relations with neighbouring countries, and interactions with national political priorities and contexts. The main implications for the governance of electricity market coupling are discussed in the conclusion.

Enhanced torsional strength and plasticity synergy of multi-component alloy via multi-level gradient structures

This study investigates an approach to enhance the strength and torsional angle, as well as analyze the microstructure and mechanical properties of a multi-component Cu-based alloy. A unique gradient-structured alloy is successfully synthesized by subjecting CuAl10Fe5Ni4Mn0.23 to high-frequency large torsional deformation. The results showed that the multi-level gradient structure due to high-frequency deformation significantly enhances the torsional strength of CuAl10Fe5Ni4Mn0.23 alloy. With a torsional rate of 120°/min and a maximum torsional angle achieved at 40°, after 200 cycles, compared to non-torsional samples, the torsional strength increased by 75.69 % and the torsion angle also increased by 38.36 %. The combination of the alloy primary structures along with gradient structures forms a new microband structure that synergistically improves the mechanical properties of multi-component alloys. The proposed large torsion deformation alters the structure of multi-component alloys, resulting in a gradient structure that simultaneously enhances both strength and plasticity, resolving current reciprocal dilemmas between these two factors.

Organisation design in megaprojects: A systematic literature review and research agenda

Megaprojects have a complex and evolving web of organisations arranged in a multilevel and multiphase structure, with intra- and inter-organisational boundaries evolving during the life cycle. This systematic review explores organisation design in megaprojects, identifying the processes for designing temporary megaproject organisational systems. The review analyses 141 full articles from a sample of 10,078 papers and presents three main contributions: the main theoretical lenses used to develop organisation design research in megaprojects, eight decision dimensions influencing organisation design choices, and the most common organisational capabilities. We present the Megaproject Organisation Design (MegaOD) Framework, as a structure to design organisational boundaries, and to identify the organisational dynamics (design, development, and redesign). We suggest four research streams to advance the design of complex organisations: 1. Design of megaproject ecosystems, 2. Degree of organisational interdependence, 3. Design for integration of product and organisational architectures, and 4. The role of organisational designers.

The feasibility and rationale for utilizing high molecular weight PBT in the preparation of high-strength fiber

Polyethylene terephthalate (PET) industrial yarn is prone to damage during weaving due to its insufficient flexibility. Therefore, it is worth exploring whether polybutylene terephthalate (PBT) with improved flexibility can be utilized for the production of high strength and low modulus industrial yarn. In this study, a low velocity spinning multi-stage multi-ratio drawing technology route was employed for spinning, but PBT fibers only achieved a break strength of 5.1cN/dtex, which was lower than that of PET fibers (7.2cN/dtex). By comparing the multi-level structure evolution of PET and PBT during spinning, it was found that the core problem limiting the strength improvement of PBT fibers is due to the early formation of folded chain lamellar structure in PBT as-spun fibers during cooling, which hinders conformational transition during hot drawing and limits the drawing ratio. This has important implications for the development of PBT industrial yarn technology by adjusting the crystallization characteristics of PBT as-spun fibers.

Easy and fast preparation of superhydrophobic multi-level structures with high stability and oil-water separation efficiency

Superhydrophobic materials can solve the problem of oil pollution in water resources. In this paper, superhydrophobic PDMS-SiO2 with macro-micro-nano multi-level structures was prepared by impregnation method. The superhydrophobic PDMS-SiO2 material has good physical and chemical stability, and retains its superhydrophobicity after sandpaper abrasion, tape peeling, water immersion, acid or alkali immersion, and high temperature baking. The superhydrophobic PDMS-SiO2 material has a high oil-water separation efficiency, with the highest oil-water separation efficiency reaching more than 99%, and the oil-water separation efficiency of different oils all exceeding 96%. The preparation of superhydrophobic PDMS-SiO2 material is simple, inexpensive and environmentally friendly, which can achieve fast and large-area preparation, with good engineering application prospects.

Multiscale MXene Engineering for Enhanced Capacitive Deionization via Adaptive Surface Charge Tailoring.

Capacitive deionization (CDI), renowned for its eco-friendly and low-energy approach to water treatment, encounters challenges in achieving optimal deionization efficiency and cycle stability despite recent advancements. In this study, the CDI electrodes were crafted with multilevel pore structures using modified cellulose (MCNF) and porous activated MXene (PAMX), aiming to the impact of surface modification on adsorption efficiency, stability, and overall performance. The experimental results demonstrated the superiority of the electrode, specifically the formulation integrating sulfonic acid-treated cellulose and PAMX (SCNF@PAMX). This configuration exhibited remarkably a higher desalination rate (3.91 mg·g-1·min-1) and enhanced desalination capacity (31.24 mg·g-1), with cycling performance exceeding 90%. Density functional theory calculations underscored the formidable adsorption energy of SCNF for Na+ (2.15 eV), surpassing that of other modified electrodes. The enhancement of deionization performance and efficiency through surface charge modification, altering Na+ electrostatic adsorption, lays a solid foundation for advancing more efficient and durable seawater desalination technologies.

A sequential multiple attribute three-way group decision-making method to heterogeneous MAGDM problems with unknown weight information

As a decision model to depict the human cognitive process, three-way decision (3WD) provides a reasonable semantic interpretation for solving practical multi-attribute group decision-making (MAGDM) problems. At the same time, there often exist different types of information due to the subjective and objective criteria in practical decision-making problems, thus it is necessary to develop some efficient frameworks to deal with the MAGDM problems with heterogeneous information. In this paper, based on 3WD theory and sequential strategy, we propose a sequential multiple attribute three-way group decision-making (SMA3WGD) method for the heterogeneous MAGDM problem with unknown weight information to achieve this goal. At first, we compute the weights of decision-makers by using the consensus degree between each of the decision-makers in a group. Then, we calculate the collective evaluation result of multiple decision-makers by utilizing the mathematical planning method with two objectives of minimum difference and high similarity between group evaluation values and individual evaluation values. Meanwhile, a nested sequence of the set of attributes is constructed according to the attribute values offered by decision-makers that have different degrees of consensus on different attributes. On this basis, a multilevel granular structure for the heterogeneous MAGDM problems is constructed, and then the compute of the cost functions and conditional probability is explored in a single level of granular structure, and the thresholds and three-way decision rules of alternatives are further determined depending on the principle of cost minimization. Furthermore, by using a sequential strategy, we obtain a sequential three-way decision result of the heterogeneous MAGDM problems under the multilevel granular structure. At last, the practicality and advantages of the suggested method are demonstrated via a blockchain service provider selection case study, a parameter sensitivity analysis along with a comparison analysis.

A Multielectrode Layout for High Reliability of Flexible Piezoresistive Sensor: One Stimulus Signal to Three Sensing Signals.

Flexible sensors have developed rapidly due to their great application potential in the intelligent era. However, the frequent bending work requirements pose a serious challenge to the mechanical reliability of flexible sensors. Herein, a strategy of using a new multielectrode layout to achieve multiple sensing signals based on one external signal is proposed for the first time to improve the reliability of flexible piezoresistive sensors. The multielectrode layout consists of a pair of interdigital electrodes and a bottom electrode. The interdigitated electrodes are used to sense the change in the surface resistance of the sensor, and the interdigital electrodes and the bottom electrode are used to sense the change in the bulk resistance of the sensor. As a result, without increasing the sensing unit area, the electrode layout allows the sensor to generate three response electrical signals when sensing an external pressure, thus improving the reliability of the sensor. Based on the electrode layout, a highly reliable flexible piezoresistive sensor with a multilevel porous structure is obtained by a microwave foaming method with a template. In the working state of sensing surface resistance, the sensor has a 22.12 kPa-1 sensitivity. Meanwhile, in the working state of sensing bulk resistance, the sensor shows a 55.17 kPa-1 sensitivity. Furthermore, the sensor is applied to monitor human pulse and speech signals, demonstrating its multisignal output characteristics and potential applications in flexible electronics. In conclusion, the new strategy of using the proposed electrode layout to improve the reliability of flexible sensors is expected to greatly promote the practical application of flexible electronics.

Protocol and preregistration for the CODEC project: measuring, modelling and mechanistically understanding the nature of cognitive variability in early childhood

BackgroundChildren’s cognitive performance fluctuates across multiple timescales. However, fluctuations have often been neglected in favour of research into average cognitive performance, limiting the unique insights into cognitive abilities and development that cognitive variability may afford. Preliminary evidence suggests that greater variability is associated with increased symptoms of neurodevelopmental disorders, and differences in behavioural and neural functioning. The relative dearth of empirical work on variability, historically limited due to a lack of suitable data and quantitative methodology, has left crucial questions unanswered, which the CODEC (COgnitive Dynamics in Early Childhood) study aims to address.MethodThe CODEC cohort is an accelerated 3-year longitudinal study which encompasses 600 7-to-10-year-old children. Each year includes a ‘burst’ week (3 times per day, 5 days per week) of cognitive measurements on five cognitive domains (reasoning, working memory, processing speed, vocabulary, exploration), conducted both in classrooms and at home through experience sampling assessments. We also measure academic outcomes and external factors hypothesised to predict cognitive variability, including sleep, mood, motivation and background noise. A subset of 200 children (CODEC-MRI) are invited for two deep phenotyping sessions (in year 1 and year 3 of the study), including structural and functional magnetic resonance imaging, eye-tracking, parental measurements and questionnaire-based demographic and psychosocial measures. We will quantify developmental differences and changes in variability using Dynamic Structural Equation Modelling, allowing us to simultaneously capture variability and the multilevel structure of trials nested in sessions, days, children and classrooms.DiscussionCODEC’s unique design allows us to measure variability across a range of different cognitive domains, ages, and temporal resolutions. The deep-phenotyping arm allows us to test hypotheses concerning variability, including the role of mind wandering, strategy exploration, mood, sleep, and brain structure. Due to CODEC’s longitudinal nature, we are able to quantify which measures of variability at baseline predict long-term outcomes. In summary, the CODEC study is a unique longitudinal study combining experience sampling, an accelerated longitudinal ‘burst’ design, deep phenotyping, and cutting-edge statistical methodologies to better understand the nature, causes, and consequences of cognitive variability in children.Trial registrationClinicalTrials.gov - NCT06330090

Preparation and size control of epoxy resin multilevel structures with SiO2 particles

In the process of preparing porous epoxy materials using reaction-induced phase separation (RIPS), the introduction of an appropriate amount of SiO2 particles enables the control of the phase separation behavior of the system and its ultimate phase structure. This study examined the role of SiO2 particles in the preparation and size control of epoxy resin multilevel structures by analyzing the phase separation and reaction kinetics in an epoxy resin blend system and characterizing the ultimate phase structure. It was found that fumed SiO2 particles (0–3 wt%) added to the DGEBA/DDCM/PEG200 blends resulted in significant interfacial stabilization, delaying phase separation. With an increase in the fumed SiO2 particle content, the phase structure stopped evolving at an earlier stage of phase separation. In addition, the fumed SiO2 particles dispersed in the porogen-enriched phase helped to stabilize the system, i.e., preventing or slowing down the segregation and aggregation of the components in the system, which may inhibit secondary phase separation in the porogen-enriched phase. When 350 nm SiO2 particles were used, an increase in the particle size resulted in a larger phase structure. The SiO2 particles were dispersed within the pore-forming agent phase, increasing the proportion of the pore-forming agent and exerting an occupancy effect, which diminished with decreasing SiO2 particle size and increasing hydrophobicity. Notably, the surface of the epoxy porous material skeleton was decorated with small pits formed by the acid etching of the SiO2 particles, and this multilevel structure contributed to the specific surface area. The multilevel structure can be precisely controlled by adjusting the size, content, and wettability of the SiO2 particles. The results of this study are universal and instructive for the preparation of porous materials using a phase separation method in combination with solid particles.

Development of polyimide nanofiber aerogels with a 3D multi-level pore structure: A new sensor for colorimetric detection of breath acetone

Polyimide nanofiber aerogels loaded with thymol blue and hydroxylamine sulfate (TB-HAS@PI NFAs) were synthesized and employed as a novel sensor for colorimetric detection of breath acetone (BrAce). The enhancement of 3D multi-level pore structure of TB-HAS@PI NFAs for sensing performance was elucidated through systematical characterization results. Because the designed sensing system relied on the specific reaction of HAS with acetone, TB-HAS@PI NFAs exhibited a more sensitive response ability to acetone, ensuring that the colorimetric sensing detection will not be interfered by other coexisting gas in exhaled breath. Moreover, the sensor demonstrated a rapid response, with color changes quantitatively measured within 30 s of exposure to acetone gas. Importantly, the feasibility and accuracy of established method were confirmed through comparison with GC–MS. This research provided a promising strategy for daily health management of healthy people and diabetics.

Fabrication of resveratrol-loaded soy peptide nanogels with transglutaminase and in vitro gastrointestinal delivery and cholesterol-lowering effect

To improve the intestinal delivery of resveratrol, soy protein peptides (SPP) and transglutaminase (TGase) were used to prepare nanogels (NGs). The results demonstrated that, compared with native soy protein isolate (SPI) NG, SPP-NG showed a lower encapsulation efficiency (EE, 70.26%), a higher release efficiency (RE, 95.83%), and a smaller particle size (194.43 nm). However, the addition of TGase increased the EE and particle size to 74.37% and 217.73 nm, respectively. The RE of SPP-TGNG was also maintained at as high as 93.28%. Interestingly, SPP-TGNG exhibited optimal bile acid–binding capacity and cholesterol-lowering digests. The differences in delivery effects and morphology were attributed to structural changes in NGs. The unfolding and exposure of hydrophobic residues and weakened hydrogen bonds by hydrolysis resulted in a disordered and loose network structure, but it was beneficial to the production of cholesterol-lowering peptides and amino acids after digestion. The presence of TGase strengthened the crosslinking network by increasing β-sheet contents, hydrogen bonds, hydrophobic interactions, and isopeptide bonds. The alteration in multilevel structure would further affect the digestive products. The exposure of more hydrophobic residues and isopeptide crosslinking may be the intrinsic reason for the production of more hypocholesterolemic peptides and amino acids. This study indicated that the combination of hydrolysis and TGase treatment enhanced the delivery efficiency and bioactive properties of peptide-based NGs to resveratrol and revealed the internal mechanism between the structural changes and delivery efficiency. This work offers a theoretical foundation and technical suggestions for the production of peptide-based cargo systems and hydrophobic active substances.

Topological Polymer Networks-Enabled Mechanically Strong Polyamide-Imide Aerogel Fibers for Thermal Insulation in Harsh Environments.

Aerogel fibers have sparked substantial interest as attractive candidates for thermal insulation materials. Developing aerogel fibers with the desired porous structure, good knittability, flame retardancy, and high- and low-temperature resistance is of great significance for practical applications; however, that is very challenging, especially by using an efficient method. Herein, mechanically strong and flexible aerogel fibers with remarkable thermal insulation performance are reported, which are achieved by constructing stiff-soft topological polymer networks and a multilevel hollow porous structure. The combination of polyamide-imide (PAI) with stiff chains and polyurethane (PU) with soft chains is first found to be able to form a topological entanglement architecture. More importantly, multilevel hollow pores can be constructed synchronously through just a one-step and green wet-spinning process. The resultant PAI/PU@340 aerogel fibers show an ultrahigh breaking strength of 94.5 MPa and superelastic property with a breaking strain of 20%. Furthermore, they can be knitted into fabrics with a low thermal conductivity of 25 mW/(m·K) and exhibit attractive thermal insulation property under extremely high (300 °C) and low temperatures (-191 °C), implying them as promising candidates for next-generation thermal insulation materials.

Construction of an S-scheme electron transfer channel in Cu0/CuFe2O4 magnetic plate column reactor for the LEV degradation: New strategy of visible Photo-Fenton system application

The complicated loading process and easy falling off of powder catalysts still restrict the wide application of Photo-Fenton technology in practical water treatment. In this study, a magnetic fixed film plate column water treatment equipment is designed as a visible Photo-Fenton reactor to remove levofloxacin (LEV). The effect of magnetic force can ensure that the catalyst is firmly fixed, and the multi-level shallow column plate structure achieves full contact and efficient reaction between the catalyst and wastewater. Simultaneously, the Cu0/CuFe2O4 (STCCF) utilizes Cu0 to construct an S-scheme electron transfer channel, which improves the separation efficiency of photo-generated carriers and provides sufficient photo-generated electrons for the reduction of Fe (Ⅲ) and Cu (Ⅱ). The pseudo-first-order reaction kinetic constant k for the degradation of LEV in the visible Photo-Fenton system is 0.0349 min−1, which is 15.9 times that of the photocatalytic system and 4.8 times that of the Fenton system. After continuous operation for 72 h, the magnetic fixed film plate column reactor can still remove more than 90 % of LEV and 82 % of COD in the secondary effluent of simulated antibiotic pharmaceutical wastewater treatment process, and the effluent is stable and meets the standard. The magnetic fixed film plate column reactor can be used for advanced treatment of antibiotic pharmaceutical wastewater. This study provides a new insight into the application of the Photo-Fenton process.

Evaluation of the Hydraulic Fracturing Tendencies of Consolidated Sandstone Reservoirs Based on the Catastrophe Theory

The evaluation of rock hydraulic fracturing tendency plays a crucial role in the selection of fracturing layers within reservoirs and the evaluation of post-compression capacity. The sandstone reservoirs in the Yihuang New Area have poor physical properties and are deeply buried. It is necessary to increase the production of oil and gas by hydraulic fracturing. Regarding the sandstones in the region, the following parameters were considered: combined compressive strength, bulk modulus, shear modulus, fracture index, horizontal-stress difference coefficient, and fracture toughness. In accordance with the catastrophe theory, a multi-level structure was established for the hydraulic fracturing-tendency evaluation of sandstone reservoirs, consisting of a target layer, a guide layer, and an indicator layer. A catastrophic model for evaluating the hydraulic fracturing tendency of sandstone reservoirs was established. The results are consistent with those obtained from the Analytic Hierarchy Process. However, the catastrophe theory significantly reduces subjective interference. The results indicate that when the hydraulic fracturing-tendency evaluation value is greater than 0.8, the reservoir can be fractured well; when the hydraulic fracturing-tendency evaluation value is between 0.7 and 0.8, the fracture reservoir is moderate; and when the hydraulic fracturing-tendency evaluation value is less than 0.7, the fractured reservoir is poor. The optimal fracture intervals for the Yi 70 well are 1320–1323 m, 1350–1355 m, and 1355–1360 m. The optimal fracture planes for the Yi 76 well are 1921–1925 m and 1925–1930 m. The optimal fracture planes for the Yi 10-1-26 well are 2487–2495 m, 2585–2587 m, and 2589–2591 m. The hydraulic fracturing-tendency model developed in this study has been applied to several well sections of sandstone reservoirs in the Yihuang New Area. Additionally, the model was compared with existing hydraulic fracturing-tendency evaluation models. The evaluation results are in agreement with the post-pressure capacity-monitoring data. The accuracy of the model presented in this study has been verified, as has its applicability to other sandstone reservoirs.

Manipulating Atomic‐Coupling in Dual‐Cavity Boride Nanoreactor to Achieve Hierarchical Catalytic Engineering for Sulfur Cathode

AbstractThe catalytic process of Li2S formation is considered a key pathway to enhance the kinetics of lithium‐sulfur batteries. Due to the system‘s complexity, the catalytic behavior is uncertain, posing significant challenges for predicting activity. Herein, we report a novel cascaded dual‐cavity nanoreactor (NiCo−B) by controlling reaction kinetics, providing an opportunity for achieving hierarchical catalytic behavior. Through experimental and theoretical analysis, the multilevel structure can effectively suppress polysulfides dissolution and accelerate sulfur conversion. Furthermore, we differentiate the adsorption (B−S) and catalytic effect (Co−S) in NiCo−B, avoiding catalyst deactivation caused by excessive adsorption. As a result, the as‐prepared battery displays high reversible capacity, even with sulfur loading of 13.2 mg cm−2 (E/S=4 μl mg−1), the areal capacity can reach 18.7 mAh cm−2.

Manipulating Atomic-Coupling in Dual-Cavity Boride Nanoreactor to Achieve Hierarchical Catalytic Engineering for Sulfur Cathode.

The catalytic process of Li2S formation is considered a key pathway to enhance the kinetics of lithium-sulfur batteries. Due to the system's complexity, the catalytic behavior is uncertain, posing significant challenges for predicting activity. Herein, we report a novel cascaded dual-cavity nanoreactor (NiCo-B) by controlling reaction kinetics, providing an opportunity for achieving hierarchical catalytic behavior. Through experimental and theoretical analysis, the multilevel structure can effectively suppress polysulfides dissolution and accelerate sulfur conversion. Furthermore, we differentiate the adsorption (B-S) and catalytic effect (Co-S) in NiCo-B, avoiding catalyst deactivation caused by excessive adsorption. As a result, the as-prepared battery displays high reversible capacity, even with sulfur loading of 13.2 mg cm-2 (E/S=4 μl mg-1), the areal capacity can reach 18.7 mAh cm-2.

Multi-bionic strategy combined with surface for anti-fouling

More and more bio-inspired methods are used to obtain anti-fouling surfaces, in which the structure plays a pivotal role in bionic coatings. It is a trend to prepare dynamic anti-fouling surfaces to improve the anti-fouling performance of the surface and synthesis the large-scale surface. Therefore, based on the special multilevel structure of Folium Sennae, the Folium Sennae-inspired surface was prepared and modified by the active molecule from Folium Sennae here (GFSF-x, AGFSF-y). Based on the excellent anti-fouling of Folium Sennae-inspired surfaces, a series of arrayed dynamic surfaces with structure-controlling liquid behavior were prepared (VMxFy). Among them, AGFSF-5 shows an excellent anti-fouling performance, whose anti-diatom attachment rate is as high as 97.8 %. The arrayed-structured coatings (MxFy) exhibit an ability to hinder the adhesion of plaque in mussel adhesion experiments. In addition, the inhibition attachment rate of diatoms of AGMPF-4 (MxFy was subjected to optimal concentration assembly) is 83.7 %, while it is 99.5 % of VMxFy. Besides, the liquid grows dynamically along the arrayed structure, and the height of VMNF-4 increased from 15.12 to 206.26 μm. The bio-inspired surfaces based on the two methods here show outstanding anti-fouling ability, in particular, the arrayed-structured surface controlling liquid behavior. This study provides a new idea for the construction of dynamic structural surfaces and shows the practical application potential due to its ability to large-scale surface texture.