Types Of Network Structures Research Articles

Cross-Linked Polyimide Aerogels with Excellent Thermal and Mechanical Properties.

With the increasing development of productivity, new materials that allow for the efficient use of energy are slowly becoming a sought-after goal, as well as a challenge that is currently being faced. For this reason, we have made aerogels as the target of our research and prepared different series (CLPI (1-5)) of cross-linked polyimide aerogels by mixing and cross-linking the heat-insulating cross-linking agent 1,3,5-tris(4-aminobenzylamino)benzene (TAB) with polyamic acid solution. We created a three-dimensional spatial organization by using vacuum freeze-drying and programmed high-temperature drying, then controlled the concentration of the polyamidate solution to investigate the concentration and TAB's influence on aerogel-related properties. Among them, the shrinkage is reduced from 40% in CLPI-1 to 28% in CLPI-5, and it also shows excellent mechanical characteristics, the highest compression strength (CLPI-5) reaches 0.81 MPa and specific modulus reaches 41.95 KN m/Kg. In addition, adding TAB improves the aerogel thermal resistance, T5 in N2 from PI-2 519 °C to CLPI-2 556 °C. The three-dimensional network-type structure of the aerogel shows an excellent thermal insulation effect, where the thermal conductivity can be as low as 24.4 mWm-1 K-1. Compared with some protective materials, cross-linked polyimide aerogel presents better flame-retardant properties, greatly improving the scope of its application in the industrial protection.

The Carbon Emission Reduction Effect and Spatio-Temporal Heterogeneity of the Science and Technology Finance Network: The Combined Perspective of Complex Network Analysis and Econometric Models

With the active promotion of the “carbon peaking and carbon neutrality” goals, science and technology finance (STF) is the important driving force of low-carbon development, and financial networks facilitate the aggregation and transformation of resources in space, so it is of great theoretical and practical significance to investigate the impact of science and technology finance networks (STFN) on carbon emissions (CE). Based on the 30 provinces of China from 2011 to 2019, this article used the STF development level in each province as the main indicator to construct the STFN. The complex network analysis and econometric models are combined, with the weighted degree values and betweenness centrality selected as typical network structure indicators incorporating into the econometric model to explore their impact on CE. Then, the Geographically and Temporally Weighted Regression (GTWR) model is applied to analyse the spatio-temporal heterogeneity of influencing factors. The results show the following: (1) From 2011 to 2019, the spatial structure of China’s STFN has changed significantly, and the status of the triangle structure consisting of Beijing–Tianjin–Hebei (BTH)–Yangtze River Delta (YRD)–Pearl River Delta (PRD) is gradually consolidated in the overall network, and the network structure tends to be stable. (2) The results of the benchmark regression show that the weighted degree value of the STFN has a significant inhibitory effect on CE, while betweenness centrality shows a certain positive effect on CE. (3) The weighted degree value has a more significant effect on CE reduction in the eastern region, while the betweenness centrality has a more significant effect on CE reduction in the central and western regions, but shows a significant promotion effect in the eastern region. (4) There is spatio-temporal heterogeneity in the effects of residents’ affluence, energy consumption, industrial structure, and environmental pollution on CE.

Dual-branch feature fusion S3D V-Net network for lung nodules segmentation.

Accurate segmentation of lung nodules can help doctors get more accurate results and protocols in early lung cancer diagnosis and treatment planning, so that patients can be better detected and treated at an early stage, and the mortality rate of lung cancer can be reduced. Currently, the improvement of lung nodule segmentation accuracy has been limited by his heterogeneous performance in the lungs, the imbalance between segmentation targets and background pixels, and other factors. We propose a new 2.5D lung nodule segmentation network model for lung nodule segmentation. This network model can well improve the extraction of edge information of lung nodules, and fuses intra-slice and inter-slice features, which makes good use of the three-dimensional structural information of lung nodules and can more effectively improve the accuracy of lung nodule segmentation. Our approach is based on a typical encoding-decoding network structure for improvement. The improved model captures the features of multiple nodules in both 3-D and 2-D CT images, complements the information of the segmentation target's features and enhances the texture features at the edges of the pulmonary nodules through the dual-branch feature fusion module (DFFM) and the reverse attention context module (RACM), and employs central pooling instead of the maximal pooling operation, which is used to preserve the features around the target and to eliminate the edge-irrelevant features, to further improve the performance of the segmentation of the pulmonary nodules. We evaluated this method on a wide range of 1186 nodules from the LUNA16 dataset, and averaging the results of ten cross-validated, the proposed method achieved the mean dice similarity coefficient (mDSC) of 84.57%, the mean overlapping error (mOE) of 18.73% and average processing of a case is about 2.07 s. Moreover, our results were compared with inter-radiologist agreement on the LUNA16 dataset, and the average difference was 0.74%. The experimental results show that our method improves the accuracy of pulmonary nodules segmentation and also takes less time than more 3-D segmentation methods in terms of time.

A cellulose derived nanofibrous nitrogen-doped carbon/TiO2/SnO2/carbon composite anodic material for lithium storage

SnO2 with high theoretical specific capacity is regarded as a desirable anodic material for lithium storage. However, the serious volume variation during discharge/charge cycling and poor conductivity significantly constrain the electrochemical performance. Herein, nanofibrous nitrogen-doped carbon (NC)/TiO2/SnO2/carbon composite was fabricated through layer-by-layer assembly of SnO2 and TiO2-gel layers alternately using cellulosic filter paper as the structural scaffold, followed by deposition of polypyrrole on the surface by chemical polymerization method, and subsequent calcination of the as-obtained matter in inert atmosphere. The achieved composite possesses a typical three-dimensional fibrous porous network structure inherited from the cellulosic substrate, and complete NC nanolayer coating on each nanofiber by adjusting the amount of pyrrole, which can alleviate the volume change and increase the conductivity of SnO2. Furthermore, the interlaced TiO2 multilayer can enhance the cycling stability during cycling, as well as facilitate the lithium-ion transfer through abundant interface. The optimal NC/TiO2/SnO2/carbon composite delivers an initial discharge capacity of 1492.6 mAh g−1 at a current density of 100 mA g−1, and retains a reversible capacity of 680.8 mAh g−1 after 200 discharge/charge cycles. This work provides an efficient strategy for design and construction of metal-oxides/carbon composite anodic materials with hierarchical microstructure that hold great potential in lithium storage.

Diagnosability of multigraph composition networks

A common approach to scale up a network is by connecting several small-scale networks to form a larger network. A multigraph composition network is a typical network structure obtained by adding an edge set between several connected graphs in the same order. This paper investigates the diagnosability on two categories of multigraph composition networks, that is, multigraph alternating composition networks and multigraph 2-matching composition networks, under the PMC and MM⁎ models. As corollaries, the diagnosability of several known networks, such as alternating group graphs, k-ary n-cubes, and round matching composition networks, can be derived directly.

A Study on the Architecture of Artificial Neural Network Considering Injection-Molding Process Steps.

In this study, an artificial neural network (ANN) was established to predict product properties (mass, diameter, height) using six process conditions of the injection-molding process (melt temperature, mold temperature, injection speed, packing pressure, packing time, and cooling time) as input parameters. The injection-molding process consists of continuous sequential stages, including the injection stage, packing stage, and cooling stage. However, the related research tends to have an insufficient incorporation of structural characteristics based on these basic process stages. Therefore, in order to incorporate these process stages and characteristics into the ANN, a process-based multi-task learning technique was applied to the connection between the input parameters and the front-end of the hidden layer. This resulted in the construction of two network structures, and their performance was evaluated by comparing them with the typical network structure. The results showed that a multi-task learning architecture that incorporated process-level specific structures in the connections between the input parameters and the front end of the hidden layer yielded relatively better root mean square errors (RMSEs) values than a conventional neural network architecture, by as much as two orders of magnitude. Based on these results, this study has provided guidance for the construction of artificial neural networks for injection-molding processes that incorporates process-stage specific features and structures in the architecture.

Evaluating the feasibility of the spinel-based Li4Ti5O12 and LiNi0.5Mn1.5O4 materials towards a battery supercapacitor hybrid device

Hybrid devices consisting of energy-dense lithium-ion battery chemistry and power-dense supercapacitors in a single unit cell are vital to improve the energy density of supercapacitors. Herein, we demonstrate a lithium-ion capacitor containing a composite of LiNi0.5Mn1.5O4 and a high surface area carbon composite as the cathode and carbon-coated Li4Ti5O12 as an anode. The composite cathode combines redox and adsorption mechanisms to store ions, producing more capacity. In contrast, the nanostructure anode can store Li-ions through a faster diffusion-type intercalation process. As a result, the lithium-ion capacitor delivers energy densities of 48 and 30 Wh kg−1 at power densities of 250 and 7900 W kg−1, respectively. Furthermore, upon cycling at 1 A g−1, the full cell retains ∼70 % specific capacitance till 4000 cycles. The use of spinel materials in both electrodes boosts the electrochemical performances of this lithium-ion capacitor because of their inherent properties like stable phase and 3D network-type structure for Li-ion diffusion. Additionally, battery-type cathodes like LiNi0.5Mn1.5O4 (4.7 V vs. Li/Li+) with a wider potential window improve the energy density, whereas better safety and cycling stability arise from the zero-strain Li4Ti5O12 anode. The studies on impedance, self-discharge, and leakage current analyses prove the practical feasibility of the lithium-ion capacitor.

Drivable Area Detection in Unstructured Environments based on Lightweight Convolutional Neural Network for Autonomous Driving Car

Road detection technology is an important part of the automatic driving environment perception system. With the development of technology, the situations that automatic driving needs to consider will become broader and more complex. This paper contributes a lightweight convolutional neural network model, incorporating novel convolution and parallel pooling modules, an improved network activation function, and comprehensive training and verification with multiple datasets. The proposed model achieves high accuracy in detecting drivable areas in complex autonomous driving situations while significantly improving real-time performance. In addition, we collect data in the field and create small datasets as reference datasets for testing algorithms. This paper designs relevant experimental scenarios based on the datasets and experimental platforms and conducts simulations and real-world vehicle experiments to verify the effectiveness and stability of the algorithm models and technical solutions. The method achieves an MIoU of 90.19 and a single batch time of 340 ms with a batch size of 8, which substantially reduces the runtime relative to a typical deep network structure like ResNet50.

Desired highly efficient Eu3+ activated GdCaAl3O7 orange-red emanating nanophosphors for UV excitable forensic and advanced information encryption and decryption applications

In the present work, orange-red emitting Eu3+ (1–8 mol %) doped GdCaAl3O7 nanophosphors are prepared by a simple solution combustion route. X-ray diffraction studies reveal that the prepared phosphors are in the tetragonal phase without any additional impurity peaks. Morphological studies clearly show the irregularly shaped network-type structures of the phosphors. Under UV light excitation (∼393 nm), photoluminescence (PL) emission peaks are recorded at ∼578, 590, 617, 651, and 701 nm, which correspond to the 5D0→7F0, 1, 2, 3, 4 transitions of Eu3+ ions, respectively. The photometric properties of the prepared nanophosphors reveal color tuning from the orange-red region to the pure red region with a color purity of ∼88.14 %. The optimized GdCaAl3O7:Eu3+ (3 mol %) nanophosphor is evaluated from an application perspective by developing latent fingerprints and lip prints. The visualized latent fingerprints under ∼254 and 375 nm excitations undoubtedly reveal levels I–III ridge features on various surfaces. The developed lip prints can discriminate the Suzuki's and Tsuchihashi’s categories. The information encryption and decryption capabilities of the nanophosphor on various surfaces were investigated by following screen printing technology, dip-pen and brush mode. The printed patterns revealed sharp, high resolution, uniform ink distribution under ∼254 nm UV excitation and invisible upon normal light. The overall results revealed that prepared GdCaAl3O7:Eu3+ (1–8 mol %) NPs exhibit excellent structural stability and intensive PL emission endorsing its applicability wide scope of areas.

Spatial association network of PM2.5 and its influencing factors in the Beijing-Tianjin-Hebei urban agglomeration.

In this paper, we empirically study the spatial association network of PM2.5 and the factors influencing those correlations using the gravity model, social network analysis (SNA), and the quadratic assignment procedure (QAP) based on data from the Beijing-Tianjin-Hebei urban agglomeration (BTHUA) in China from 2005 to 2018. We draw the following conclusions. First, the spatial association network of PM2.5 exhibits relatively typical network structure characteristics: the network density and network correlations are highly sensitive to efforts to control air pollution, and there are obvious spatial correlations within the network. Second, cities in the center of the BTHUA have large network centrality values, while cities in the peripheral region have small centrality values. Tianjin is a core city in the network, and the spillover effect of PM2.5 pollution in Shijiazhuang and Hengshui is the most noticeable. Third, the 14 cities can be divided into four plates, with each plate having obvious geographical location characteristics and linkage effects. The cities in the association network are divided into three tiers. Beijing, Tianjin, and Shijiazhuang are located in the first tier, and a considerable number of PM2.5 connections are completed through these cities. Fourth, differences in geographical distance and urbanization are the main drivers of the spatial correlations of PM2.5. The greater the urbanization differences, the more likely the generation of PM2.5 links is, while the opposite is true for differences in geographical distance.

Role of metal chlorides in the gelation and properties of fucoidan/κ-carrageenan hydrogels

Metal ions play a crucial role in forming hydrogels, and their effects on fucoidan (FUC): κ-carrageenan (KC) mixed gels were investigated. The results indicated that the FUC: KC mixed gels (FC) were promoted by K+ and Ca2+ but destroyed by Fe3+. The gel strength of FC was enhanced by K+ and Ca2+, with G′ and G″ being highest at 50 mmol/L KCl and 25 mmol/L CaCl2, respectively. Water mobility was weakened after the addition of KCl and CaCl2 in accordance with the decrease in T23 relaxation time (free water, 100–1000 ms). After addition of KCl and CaCl2, the FC groups presented a typical three-dimensional network structure in contrast to the lamellar, disordered, and broken structure of FUC. Moreover, the FT-IR spectrum certified the enhancement of hydrogen bonds and the occurrence of electrostatic interactions during gel formation by the red-shift of the OH stretching vibration of the Ca2+ group and the blue-shift of the COS vibrations. The XRD results confirmed that the binding of Ca2+ to FC was tighter than that of K+ at the same charge content. These results provide a theoretical basis for understanding the interaction mechanism of FC with metal ions.

Phase formation and mechanical properties of graphene reinforced regolith composites

Exploration of outer space sparked interest in Lunar in-situ construction, which shows great advantages in material availability, low cost, and ease of construction. In this work, different weight ratios of graphene were added to regolith powder to investigate their effects on the sintering process and the mechanical properties of the regolith powder. After mixing and compacting, the graphene-regolith composite was sintered under high temperatures to obtain the consolidated solids. The phase distribution and formation of the sintered solids were characterized using energy-dispersive X-ray and X-ray diffraction spectroscopy. The mechanical properties of the modified regolith were studied by the nano and micro-indentation experiments. The results revealed that the dispersion of graphene could not only construct a typical continuous 3D hybrid network structure but also improve the mechanical property of the sintered composite.

Catalytic effect of trifluoroacetic acid on the CO2 transport properties of organic-inorganic hybrid silica membranes

Developing silica membranes that are highly selective for CO2 has always been a challenge due to the small sizes of the pores and less amount of CO2 philic sites in a typical silica network structure. Herein, we describe the fabrication of silica (tetraethoxysilane) membranes functionalized with 3-aminopropyltriethoxysilyl (APTES) and trifluoroacetic acid (TFA). An interaction generated among primary (NH2) amines and TFA was identified, which was then also revealed by the reversible nature of CO2 adsorption/desorption — an opposite trend from observations when using another catalyst (HCl). The resultant TEOS-APTES (TFA) membranes demonstrated CO2 permeance of 3.8 × 10−7 mol m − 2 s − 1 Pa−1 and CO2/N2 selectivity of 35 at 50 ⁰C via the effect of surface diffusion. This is attributed to the increased microporosity and structural variations affected by TFA, which enhanced molecular sieving and controls the CO2-philic sites (-NHCOCF3) via interaction with amines. This novel approach would be effective for the energy-efficient fabrication of highly CO2-permeable membranes.

Understanding resilience of urban food-energy-water nexus system: Insights from an ecological network analysis of megacity Beijing

Increasing resource crises force cities to scrutinize resilience status of urban food-energy-water (FEW) nexus system, which is highly depend on their metabolic processes. Taking Megacity Beijing as the case, we developed an ecological network model to explore structural and functional characteristics of urban FEW metabolic systems, with consideration of local availability, cross-region supply and resource nexus. The results show that the metabolism of food, energy and water exhibits different topological structures, and water presents typical loop network structure with APL and FCI values of 3.02 and 0.08 respectively, while food metabolizes in a loose topology and energy suffers dissipative losses in a uniflow form. This makes water metabolic network a robust organization with greater circulation, higher stability index (1.81) and better adaptive capacity to counter to vulnerability, yet energy network performs poorly for lacking redundancy and food network is more sensitive to changing conditions. Our findings enable decision-makers to find opportunities to build a more resilient city via improving FEW metabolic network synergistically, such as strengthening local resource reserves to provide buffering capacity when external supplies fail, as well as enhancing utilization efficiency of dominant industries and promoting treatment and reuse to make continuous survival and prosperity possible.

Spatial correlation and influencing factors of the urban land transaction market in the Yangtze River Delta urban agglomeration

PurposeLand transactions are a key indicator of urban sustainable development and urban space expansion. Therefore, this paper aims to study the spatial correlation of different types of land transactions.Design/methodology/approachBased on the big data of land micro transactions in Yangtze River Delta urban agglomeration, this paper uses the generalized forecast error variance decomposition (GFEVD) method to measure the correlation level of urban land markets. Also, social network analysis (SNA) is used to describe spatial correlation network characteristics of an urban agglomeration land market. In the meantime, the factors that influence the spatial correlation of urban land markets are investigated through a quadratic assignment procedure (QAP).FindingsThe price growth rate of urban residential land was higher than that of industrial land and commercial land. The spatial relevance of urban residential land is the highest, while the spatial relevance of the urban commercial land market is the lowest. The urban industrial land market, commercial land market and residential land market all present a typical network structure. Population distance (POD) and Engel coefficient distance (EGD) are negatively correlated with the correlation degree of the urban residential land network; traffic distance (TRD) and economic distance (ECD) are negatively correlated with the correlation degree of the urban industrial land network and commercial land network.Originality/valueThis paper uses a systematically-integrated series of problem-solving models to better explain the development path of urban land markets and to realize the integration of the interdisciplinary methods of geography, statistics and big data analysis.

Probabilistic model for the impact of fear of flying on airline network structures

Many passengers experience nervousness and/or anxiety when they fly and, on average, they make fewer trips than those who are not afraid. This might reduce airline revenues and profits. In this paper, a new mathematical model of profitability of different airline network choices in a liberalized market is proposed in terms of two particular personal features of passengers: the loyalty to a preferred airline and the fear of flying, which are assumed to be random. A detailed analysis of different network-type structures is presented and optimal solutions depending on the impact of these two random attributes for each of the networks are characterized in terms of the model parameters. The main finding is that the symmetric networks (hub-and-spoke or point-to-point) may not always be the optimal configuration, but a hybrid network configuration may be favoured in some cases. It is also shown that under certain conditions, the point-to-point network structure provides a higher total number of flights due to the fear of flying, which pushes the airlines to reduce the number of indirect routes.

Design consideration and optimization of process parameters in fiber extraction unit via modelling studies

AbstractDemand for natural fibers as an alternative to synthetic fibers is on the rise. Constraints of current fiber extraction units are limited to single raw material and has low energy efficiency. Hence, this study was taken up for the design and development of a multiple fiber extractor, modelling and optimization of the machine operating parameters, and economic analysis of the developed prototype. The designed machine operates based on the raspador principle. Box Behnken design (BBD) and artificial neural network (ANN) were used to evaluate the performance and optimize the variables of the designed multiple fiber extractor. The input variables included roller speed (550, 735, and 920 rpm), gap/clearance of the roller (2, 3, and 4 mm) and the pitch/distance between the spline slots (0.5, 1.5, and 2.5 mm), while the output variables were yield (kg), capacity (kg/h), energy consumption (kJ), and efficiency (%).The prediction accuracy of quadratic models (R2 = 0.962–0.985) developed in BBD were better than the ANN models (R2 = 0.831–0.946). The optimized combination was found to be 714.23 rpm roller speed, 3.12 mm gap, and 1.49 mm pitch of the blade, which yielded (1.48 kg) a desirability value of 0.95. The XRD results indicated the highest crystallinity index was recorded for Pineapple leaf fiber compared to banana fiber. SEM results revealed that extracted BF having typical network structure (cellulose, hemicellulose, lignin, and waxes) compared to smooth PLF (lignin and hemicellulose). The fibers extracted using the developed machine was utilized for making biodegradable cutlery.Practical ApplicationsThis study was taken up to design a multifiber extractor, to extract fibers from agricultural residues after their useful parts are taken for consumption or processing. Thus, a single machine capable of handling multiple byproducts can utilize those residues, which were previously discarded as waste. Moreover, the extracted fibers are used for the manufacture of biodegradable cutlery. With a small investment, this machine can provide an additional income to the farmers, along with taking away the concern regarding waste disposal.

Fresh insights for sustainable development: Collaborative governance of carbon emissions based on social network analysis

AbstractGlobal climate change caused by carbon emissions has brought severe challenges to the sustainable development of global economy. In this context, this study constructs a weighted and directed carbon emission network for the European Union (EU). We use social network analysis to measure the indicators of the structural characteristics of the EU carbon emission network, and employ cluster analysis to investigate the role of countries in the carbon emission network. Furthermore, we use linear regression to explore the impacts of structural characteristics of carbon emission network on carbon emissions. The results show that, first the carbon emissions ties among the EU countries show a typical network structure during the sample period. Countries such as Germany, France, and Belgium are at the center of the carbon emission network, while Malta, Cyprus, and Croatia are at the edge. Second, different countries play the roles of “net spillover,” “broker,” “two‐way spillover,” and “net receiver” in the EU carbon emission network. Last, the increase of network density, the decrease of network hierarchy and the decrease of network efficiency all significantly reduce the carbon emission intensity of the EU, and narrow the spatial difference of carbon emissions among countries. Improving the weighted centrality indicators of nodes in the carbon emission network is helpful to reduce the carbon emission intensity of the EU countries. Based on the findings, this article puts forward policy suggestions for the collaborative governance of EU carbon emission abatement.

Preparations of cyclodextrin polymer and MgO jointly entrapped iron(III)-TAML catalysts for the removal of aromatic pollutants in water

Iron(III)-tetra-amido-macrocyclic (Fe(III)-TAML) activators generally activate H2O2 to efficiently degrade electron-rich pollutants (e.g., phenols) in water under basic conditions, though Fe(III)-TAML is readily demetallized and inactivated under acidic or nearly neutral conditions. Fe(III)-TAML/H2O2 systems often display low reactivity toward electron-deficient pollutants (benzenes) widespread in water. Fe(III)-TAML catalysts with improved pH adaptations and substrate spectra are highly desirable. Herein, we proposed one-pot preparations of cyclodextrin polymer (CDP) and MgO jointly supported Fe(III)-TAML catalysts for the removal of various aromatic contaminants in water. The composite catalysts exhibited typical polymeric network structure of CDP, embedded with basic MgO/Mg(OH)2 particles and three different Fe(III)-TAML species. The composites repeatedly used for 3–4 times still activated H2O2 to completely remove 4-chlorophenol in water during initial pH 2.0–9.0 conditions or in the presence of main water constituents. Despite low Fe(III)-TAML loadings (0.03%–0.10%), these composites displayed great pH adaptations and diverse reactive regions toward organic compounds, compared to other supported catalysts containing 0.24%–1.27% Fe(III)-TAML. Column experiments demonstrated that the composite/H2O2 completely removed electron-rich 4-chlorophenol and electron-deficient benzenes in simulated groundwater. This study indicates that taking CDP as a support may open new prospects for the design of supported catalysts.

Ultrasmall Gold Nanoclusters-Enabled Fabrication of Ultrafine Gold Aerogels as Novel Self-Supported Nanozymes.

Metal aerogels represent an emerging type of functional porous materials with promising applications in diverse fields, but the fabrication of metal aerogels with specific structure and property still remains a challenge. Here, the authors report a new approach to fabricate metal aerogels by using ultrasmall metal nanoclusters (NCs) as functional building blocks. By taking D-penicillamine-stabilized gold NCs (AuNCs) with a diameter of 1.4nm as an example, Au aerogels with ultrafine ligament size (3.5nm) and good enzyme-mimic properties are synthesized. Detailed characterization shows that the obtained Au aerogels possess typical 3D self-supported porous network structure with high gold purity and surface area. Time-lapse spectroscopic and microscopic monitoring of the gelation process reveal that these ultrasmall AuNCs first grow into large nanoparticles before fusion into nanowire networks, during which both pH and the precursor concentration are identified to be the determining factor. Owing to their highly porous structure and abundant metal nodes, these self-supported Au aerogels display excellent peroxidase-like properties. This work provides a strategy for fabricating advanced metal aerogels by taking ultrasmall-sized metal NCs as building blocks, which also opens new avenues for engineering the structure and properties of metal aerogels for further advancing their applications.