High Edge Density Research Articles

Consensus Subspace Graph Regularization based on prior information for multiplex network clustering

Multiplex network clustering can identify the common cluster structure shared by all layers, which is of great significance for downstream research such as personalized recommendations of social relationships and the mining of social media communication behaviors. Traditional multiplex network clustering methods rely only on the topological structure and are suitable for networks with clear structures. However, real-world networks often suffer from sparse connectivity and noisy edges. In addition, the current methods based on prior information only use the obtained consensus prior information as a preprocessing method for multiplex networks, and do not fully utilize and integrate the prior information in the clustering process, resulting in a low accuracy. To solve the problem of insufficient utilization of prior information, we propose a Consensus Subspace Graph Regularization (CSGR) approach for multiplex network clustering, which integrates topological information with the consensus prior information of a network. We first construct the consensus prior matrix of a network by a non-overlapping greedy search method, which represents a subset of the network that consists of nodes with high edge density. Then we construct a graph regularization term, which encodes the consensus prior information, to optimize the generation of a joint low-dimensional representation within the consensus subspace for the entire multiplex network. Finally, we employ the consensus prior matrix to denoise each network layer, thereby yielding a precise low-dimensional representation of each layer for consensus subspace fusion. In addition, comprehensive experiments have demonstrated that CSGR improves the clustering accuracy of real-world networks by an average of 1.56%.

MoS2/graphdiyne nanotube/MXene 3D-interconnected ternary aerogel: A high-performance electrocatalyst for hydrogen evolution reaction

MoS2 is a promising challenger to Pt for the HER, but we must make it more durable before it can take over the hydrogen production scene. This work fabricated and evaluated a hybrid structure of MXene-graphydine nanotube-MoS2 (MGMX) aerogel toward HER. It showed efficient and stable activity H2 evolution with ƞ10 = 109 mV vs RHE and a Tafel slope of 55 mVdec−1. The catalytic performance results from a synergistic interplay between increased exposed active sites and improved charge transfer within the catalyst. The two-dimensional (2D) islands of double-phase MoS2 are directly deposited onto graphdiyne nanotubes (GDNTs) surface, creating a structurally interesting material with interconnected components. The unique combination of sp and sp2 carbon atoms in the graphdiyne (GDY) makes it an ideal support for catalyst material. The combination of high edge density in metallic MoS2, a conducting MXene framework, and the bridging structure of GDNT significantly enhance the overall HER performance of the ternary aerogel structure.

A motif-based probabilistic approach for community detection in complex networks

Community detection in complex networks is an important task for discovering hidden information in network analysis. Neighborhood density between nodes is one of the fundamental indicators of community presence in the network. A community with a high edge density will have correlations between nodes that extend beyond their immediate neighbors, denoted by motifs. Motifs are repetitive patterns of edges observed with high frequency in the network. We proposed the PCDMS method (Probabilistic Community Detection with Motif Structure) that detects communities by estimating the triangular motif in the network. This study employs structural density between nodes, a key concept in graph analysis. The proposed model has the advantage of using a probabilistic generative model that calculates the latent parameters of the probabilistic model and determines the community based on the likelihood of triangular motifs. The relationship between observing two pairs of nodes in multiple communities leads to an increasing likelihood estimation of the existence of a motif structure between them. The output of the proposed model is the intensity of each node in the communities. The efficiency and validity of the proposed method are evaluated through experimental work on both synthetic and real-world networks; the findings will show that the community identified by the proposed method is more accurate and dense than other algorithms with modularity, NMI, and F1score evaluation metrics.

The relative influence of landscape features on maternity roost and hibernaculum selection in temperate bats

Many animal life-history stages center around residences (nests, roosts, etc.) where the availability of resources within an optimal range can affect fitness. Understanding factors influencing residence selection is fundamental for efficient management or recovery plans. Many bat species use permanent roosts during different periods of the year, and while most conservation plans aim to protect these roosts, the availability of suitable habitat near the roosts (e.g., foraging habitat) is also critical to consider. We evaluated the importance of landscape features at multiple scales surrounding seasonal bat roosts in two regions (north and south) of Québec (Canada), using data from participatory science and government databases. In the human-altered environment of south Québec, bats selected maternity roosts with high anthropogenic cover and water edge density at the 150 m and 2 km scales, respectively. Conversely, roost selection in north Québec, a forested area, could not be explained by any landscape features. In winter, fewer bats used hibernacula located in heavily human-modified landscapes—opposite to the trend observed with maternity roost selection. Our study demonstrates how considering landscape features at the appropriate temporal and spatial scales can promote more efficient conservation for bats.

Linking Anthropogenic Landscape Perturbation to Herbivory and Pathogen Leaf Damage in Tropical Tree Communities.

Anthropogenic disturbance of tropical humid forests leads to habitat loss, biodiversity decline, landscape fragmentation, altered nutrient cycling and carbon sequestration, soil erosion, pest/pathogen outbreaks, among others. Nevertheless, the impact of these alterations in multitrophic interactions, including host-pathogen and vector-pathogen dynamics, is still not well understood in wild plants. This study aimed to provide insights into the main drivers for the incidence of herbivory and plant pathogen damage, specifically, into how vegetation traits at the local and landscape scale modulate such interactions. For this purpose, in the tropical forest of Calakmul (Campeche, Mexico), we characterised the foliar damage caused by herbivores and pathogens in woody vegetation of 13 sampling sites representing a gradient of forest disturbance and fragmentation in an anthropogenic landscape from well preserved to highly disturbed and fragmented areas. We also evaluated how the incidence of such damage was modulated by the vegetation and landscape attributes. We found that the incidence of damage caused by larger, mobile, generalist herbivores, was more sensitive to changes in landscape configuration, while the incidence of damage caused by small and specialised herbivores with low dispersal capacity was more influenced by vegetation and landscape composition. In relation to pathogen symptoms, the herbivore-induced foliar damage seems to be the main factor related to their incidence, indicating the enormous importance of herbivorous insects in the modulation of disease dynamics across tropical vegetation, as they could be acting as vectors and/or facilitating the entry of pathogens by breaking the foliar tissue and the plant defensive barriers. The incidence of pathogen damage also responded to vegetation structure and landscape configuration; the incidence of anthracnose, black spot, and chlorosis, for example, were favoured in sites surrounded by smaller patches and a higher edge density, as well as those with a greater aggregation of semi-evergreen forest patches. Fungal pathogens were shown to be an important cause of foliar damage for many woody species. Our results indicate that an increasing transformation and fragmentation of the tropical forest of southern Mexico could reduce the degree of specialisation in plant-herbivore interactions and enhance the proliferation of generalist herbivores (chewers and scrapers) and of mobile leaf suckers, and consequently, the proliferation of some symptoms associated with fungal pathogens such as fungus black spots and anthracnose. The symptoms associated with viral and bacterial diseases and to nutrient deficiency, such as chlorosis, could also increase in the vegetation in fragmented landscapes with important consequences in the health and productivity of wild and cultivated plant species. This is a pioneering study evaluating the effect of disturbances on multitrophic interactions, offering key insights on the main drivers of the changes in herbivory interactions and incidence of plant pathogens in tropical forests.

Normal and stable approximation to subgraph counts in superpositions of Bernoulli random graphs

AbstractReal networks often exhibit clustering, the tendency to form relatively small groups of nodes with high edge densities. This clustering property can cause large numbers of small and dense subgraphs to emerge in otherwise sparse networks. Subgraph counts are an important and commonly used source of information about the network structure and function. We study probability distributions of subgraph counts in a community affiliation graph. This is a random graph generated as an overlay of m partly overlapping independent Bernoulli random graphs (layers) $G_1,\dots,G_m$ with variable sizes and densities. The model is parameterised by a joint distribution of layer sizes and densities. When m grows linearly in the number of nodes n, the model generates sparse random graphs with a rich statistical structure, admitting a nonvanishing clustering coefficient and a power-law limiting degree distribution. In this paper we establish the normal and $\alpha$ -stable approximations to the numbers of small cliques, cycles, and more general 2-connected subgraphs of a community affiliation graph.

Bone-inspired (GNEC/HAPAAm) hydrogel with fatigue-resistance for use in underwater robots and highly piezoresistive sensors

A novel bone-inspired fatigue-resistant hydrogel with excellent mechanical and piezoresistive properties was developed, and it exhibited great potential as a load and strain sensor for underwater robotics and daily monitoring. The hydrogel was created by using the high edge density and aspect ratio of graphene nanosheet-embedded carbon (GNEC) nanomaterials to form a three-dimensional conductive network and prevent the expansion of microcracks in the hydrogel system. Multiscale progressive enhancement of the organic hydrogels (micrometer scale) was realized with inorganic graphene nanosheets (nanometer scale). The graphene nanocrystals inside the GNEC film exhibited good electron transport properties, and the increased distances between the graphene nanocrystals inside the GNEC film caused by external forces increased the resistance, so the hydrogel was highly sensitive and suitable for connection to a loop for sensing applications. The hydrogels obtained in this work exhibited excellent mechanical properties, such as tensile properties (strain up to 1685%) and strengths (stresses up to 171 kPa), that make them suitable for use as elastic retraction devices in robotics and provide high sensitivities (150 ms) for daily human monitoring.

In Python available: St. Nicolas House Algorithm (SNHA) with bootstrap support for improved performance in dense networks

The St. Nicolas House algorithm (SNHA) finds association chains of direct dependent variables in a data set. The dependency is based on the correlation coefficient, which is visualized as an undirected graph. The network prediction is improved by a bootstrap routine. It enables the computation of the empirical p-value, which is used to evaluate the significance of the predicted edges. Synthetic data generated with the Monte Carlo method were used to firstly compare the Python package with the original R package, and secondly to evaluate the predicted network using the sensitivity, specificity, balanced classification rate and the Matthew's correlation coefficient (MCC). The Python implementation yields the same results as the R package. Hence, the algorithm was correctly ported into Python. The SNHA scores high specificity values for all tested graphs. For graphs with high edge densities, the other evaluation metrics decrease due to lower sensitivity, which could be partially improved by using bootstrap,while for graphs with low edge densities the algorithm achieves high evaluation scores. The empirical p-values indicated that the predicted edges indeed are significant.

The role of forest fragmentation in yellow fever virus dispersal

The intense process of deforestation in tropical forests poses serious challenges for the survival of biodiversity, as well as for the human species itself. This scenario is supported by the increase in the incidence of epidemics of zoonotic origin observed over the last few decades. In the specific case of sylvatic yellow fever (YF), it has already been shown that an increase in the transmission risk of the causative agent (yellow fever virus - YFV) is associated with areas with a high degree of forest fragmentation, which can facilitate the spread of the virus. In this study we tested the hypothesis that areas with more fragmented landscapes and a higher edge density (ED) but a high degree of connectivity between forest patches favor YFV spread. To this end, we used YF epizootics in non-human primates (NHPs) in the state of São Paulo to build direct networks, and used a multi-selection approach to analyze which landscape features could facilitate YFV spread. Our results showed that municipalities with the potential to spread the virus exhibited a higher amount of forest edge. Additionally, the models with greater empirical support showed a strong association between forest edge density and the risk of occurrence of epizootic diseases, as well as the need for a minimum threshold of native vegetation cover to restrict their transmission. These findings corroborate our hypothesis that more fragmented landscapes with a higher degree of connectivity favor the spread of YFV, while landscapes with fewer connections tend to act as dead zones for the circulation of the virus.

Plant trait networks reveal adaptation strategies in the drylands of China

BackgroundPlants accomplish multiple functions by the interrelationships between functional traits. Clarifying the complex relationships between plant traits would enable us to better understand how plants employ different strategies to adapt to the environment. Although increasing attention is being paid to plant traits, few studies focused on the adaptation to aridity through the relationship among multiple traits. We established plant trait networks (PTNs) to explore the interdependence of sixteen plant traits across drylands.ResultsOur results revealed significant differences in PTNs among different plant life-forms and different levels of aridity. Trait relationships for woody plants were weaker, but were more modularized than for herbs. Woody plants were more connected in economic traits, whereas herbs were more connected in structural traits to reduce damage caused by drought. Furthermore, the correlations between traits were tighter with higher edge density in semi-arid than in arid regions, suggesting that resource sharing and trait coordination are more advantageous under low drought conditions. Importantly, our results demonstrated that stem phosphorus concentration (SPC) was a hub trait correlated with other traits across drylands.ConclusionsThe results demonstrate that plants exhibited adaptations to the arid environment by adjusting trait modules through alternative strategies. PTNs provide a new insight into understanding the adaptation strategies of plants to drought stress based on the interdependence among plant functional traits.

Mapping crop damage by wild boars using multi‐scale risk modeling in Northeast China

AbstractBalancing development and conservation is one of the major challenges for modern societies. Negative human–wildlife interactions, especially crop raiding by wildlife, greatly undermine this objective. Jilin Province in Northeast China encompasses ecosystems of high biodiversity value but endures serious losses in crop yields due to wild boars (Sus scrofa). Mitigation measures have been hindered by knowledge gaps in wild boar damage patterns. Our objective was to investigate how wild boar damage responded to human‐dominated landscapes, evaluating landscape patterns of damage at multiple ecological scales, thus providing a provincial map of damage risk to assist with damage mitigation. We used 1,144 coordinates of wild boar damage from 2009 to 2013 to train multi‐scale risk models with the maximum entropy algorithm. Damage risk was highest on gentle slopes and in heterogenous landscapes composed of fragmented plantations and cropland, with high edge density among small discontinuous patches, and with dense settlements in countryside areas with sporadic road networks. Scale had a substantial effect on relationships between damage and landscapes. We recommend mitigating the conflict risk by improving the compensation system and reducing the damage frequency through repelling, electric fencing, and dissuasive feeding.

Nanomachining of van der Waals nanowires: Process and deformation mechanism

The edges of van der Waals materials exhibit unique physical and chemical properties, and they are promising for applications in many fields, such as optoelectronics, energy storage, and catalysis. Van der Waals material nanostructures with a controllable high density of edges are difficult to produce by current fabrication methods. In the present study, a simple nanomachining process to fabricate van der Waals nanowires with a high density of edges is proposed. This method used a linear-edge diamond tool to cut the basal plane of van der Waals film materials into a one-dimensional nanowire at the nanoscale. Experimental tests were performed to investigate the influences of the cutting thickness, film thickness, cutting direction, and material properties on the machining outcomes. The results showed that the van der Waals materials possessed low Young's moduli ranging from 24 to 238 GPa by cutting with a cutting thickness of larger than 30 nm, and the out-of-plane cutting direction led to the best machining quality and controllable preparation of van der Waals nanowires. To support the interpretation of the process outcomes, molecular dynamics simulation and transmission electron microscopy were performed to reveal the material-removal mechanism during nanocutting of van der Waals materials. From analysis of the chip-deformation process, interlayer slipping was found to dominate the plastic processing of the van der Waals materials, accompanied by intralayer bending and intralayer fracture in the out-of-plane cutting direction. By contrast, the brittle removal state occurred when cutting in the in-plane direction. This study provides important insights into the material-removal mechanism of van der Waals materials prepared by nanoscale mechanical cutting.

Enhanced urban roadside vegetation increases pollinator abundance whereas landscape characteristics drive pollination

Roads cause pollution, habitat degradation and fragmentation, while recent studies report that roadside vegetation can benefit plant and insect diversity. However, the factors that drive effects of roadside vegetation on pollinator abundance and pollination need further attention. Our main interest in this study was to evaluate urban roadside enhancement by identifying direct and indirect effects of local resource availability and landscape characteristics on pollinator abundance and quality of pollination, by using functionally different phytometer plants. Thus, we established 34 species-rich wildflower patches along urban roads, and monitored flowering species and floral density at the local scale. At the landscape scale we measured the amount of impervious surface within a radius of 500 m around the wildflower patches, as well as edge density of all major land-use types, and distance to the city center. As control, we used 34 patches with existing species-poor roadside green. The abundance of pollinators, i.e., honeybees, wild bees, and hoverflies, was sampled with colored pan traps. Pollination was monitored based on fruit number and weight of three phytometer species, i.e., Fragaria × ananassa, Ranunculus acris, and Trifolium pratense, that differ in flower morphology and color, and attract different pollinators. Pollinator abundance was positively related to local richness of flowering plants and floral density. At the landscape scale, pollinator abundance was positively related to the proportion of impervious surface but negatively to edge density. Phytometer-specific effects on pollination were moderated by landscape characteristics: Fruit mass of F. x. ananassa was highest in the inner city, fruit number of R. acris was lower in areas with high edge density, whereas in T. pratense fruit number increased with edge density, and was negatively related with the proportion of impervious surface. There were no indirect effects of local and landscape factors via pollinator abundance on pollination. The study of the three phytometer species revealed that roadside enhancement can directly support fruit production in wild plants and crops in cities. Thereby, different landscape characteristics influenced the quality of fruit set, which also depended on individual plant reproductive adaptations. This stresses the importance of using a set of complementary phytometer species in pollination experiments, especially to gain insights into ecosystem functioning at coarser spatial scales.

Landscape composition and configuration relatively affect invasive pest and its associator across multiple spatial scales

Landscape structures affect pests, depending on compositional heterogeneity (the number and proportions of different habitats), configurational heterogeneity (spatial arrangement of habitats), and spatial scales. However, there is limited information on the relative effects of compositional and configurational heterogeneity on invasive pests and their associates (species that can benefit from invasive pests), and how they vary across spatial scales. In this study, we assayed the invasive pest Bactrocera dorsalis (Hendel) and its associated fly Drosophila melanogaster in 15 landscapes centered on mango orchards. We calculated landscape composition (forest percentage, mango percentage, and Shannon's diversity) and configuration (edge density) using two methods: spatial distance scales and combined scales. Spatial distance scales included buffer rings with radii of 0.5, 1.0, and 1.5 km, and combined scales referred to cutting or not cutting a smaller ring from larger ones. Our results shown that compositional heterogeneity positively affected B. dorsalis and D. melanogaster due to forest cover percentage, whereas configurational heterogeneity with high edge density negative effect on B. dorsalis. Forest cover had less of an effect on B. dorsalis than configurational heterogeneity, but the opposite effect was observed for D. melanogaster. Importantly, the direction and strength of forest cover and configurational heterogeneity to species did not vary with spatial distance scales or spatial combined scales. Thus, compositional and configurational heterogeneity exhibit differential effects on this invasive pest and its associator, and revealed that the relative effects of landscape structures are consistent across multiple scales. These results provide new insights into landscape effects on interconnected species using a diverse spatial-scale approach.

Smaller agricultural fields, more edges, and natural habitats reduce herbicide-resistant weeds

The exponential growth of herbicide-resistant weeds poses enormous challenges to the sustainability of food systems. While great efforts in weed management are being performed at the plot level, the influence of the landscape context on the presence of herbicide-resistant weeds remains largely unknown. We tested these ideas through a large-scale sampling on two of the most important crops globally: maize and soybean. In Argentina, we co-developed with farmers the sampling of 2846 soybean and 1539 maize fields (covering an area of 159 million ha) and measured the presence of herbicide-resistant weeds, landscape context (field size, edge density, natural habitat size), management variables (e.g. fertilization), crop variety, farm identity and region. We found that smaller fields, with higher edge density, and neighboring larger natural habitats were associated to a lower presence of herbicide-resistant weeds. These results were not confounded with the influence of some other management variables (e.g. fertilization), crop variety, farm or region. Landscape design is an important, but underrepresented, management tool that could help to achieve a sustainable control of weeds.

Configurational landscape heterogeneity: Crop-fallow boundaries enhance the taxonomic diversity of carabid beetles and spiders

Arthropod biodiversity in agricultural landscapes is favoured by high levels of compositional and configurational heterogeneity. In terms of composition, the high value of non-crop habitats is firmly established. In contrast, the causes underlying positive effects of configurational heterogeneity, i.e. patch size and boundary length, are less well understood. We here test the hypothesis that boundaries comprise valuable habitats in its own right by enhancing the taxonomic diversity of carabid beetles and spiders. We used pitfall traps placed along transects running from the interior of wheat fields across the habitat boundary to the interior of unmanaged grassland fallows. Taxonomic diversity was highest around the boundaries due to (1) spillover effects but also (2) species showing a distinct preference for edge habitat. For carabids, habitat boundaries harboured distinct species assemblages. Grassland fallows had, especially in spiders, positive effects on the taxonomic diversity of crop fields close to the boundary, while no negative effects of crop fields on adjacent fallows could be found. Overall, taxonomic diversity was higher in grassland fallows than in crop fields, though differences were surprisingly small. The high taxonomic diversity found for boundaries suggest that these may comprise valuable habitats in its own rights, which in turn may causally underpin positive effects of configurational heterogeneity on arthropod diversity in agroecosystems. Thus, increasing configurational heterogeneity, e.g. by promoting small field sizes and high edge density, seems an important tool for preserving farmland biodiversity.

Estuarine microbial networks and relationships vary between environmentally distinct communities.

Microbial interactions have profound impacts on biodiversity, biogeochemistry, and ecosystem functioning, and yet, they remain poorly understood in the ocean and with respect to changing environmental conditions. We applied hierarchical clustering of an annual 16S and 18S amplicon dataset in the Skidaway River Estuary, which revealed two similar clusters for prokaryotes (Bacteria and Archaea) and protists: Cluster 1 (March-May and November-February) and Cluster 2 (June-October). We constructed co-occurrence networks from each cluster to explore how microbial networks and relationships vary between environmentally distinct periods in the estuary. Cluster 1 communities were exposed to significantly lower temperature, sunlight, NO3, and SiO4; only NH4 was higher at this time. Several network properties (e.g., edge number, degree, and centrality) were elevated for networks constructed with Cluster 1 vs. 2 samples. There was also evidence that microbial nodes in Cluster 1 were more connected (e.g., higher edge density and lower path length) compared to Cluster 2, though opposite trends were observed when networks considered Prokaryote-Protist edges only. The number of Prokaryote-Prokaryote and Prokaryote-Protist edges increased by >100% in the Cluster 1 network, mainly involving Flavobacteriales, Rhodobacterales, Peridiniales, and Cryptomonadales associated with each other and other microbial groups (e.g., SAR11, Bacillariophyta, and Strombidiida). Several Protist-Protist associations, including Bacillariophyta correlated with Syndiniales (Dino-Groups I and II) and an Unassigned Dinophyceae group, were more prevalent in Cluster 2. Based on the type and sign of associations that increased in Cluster 1, our findings indicate that mutualistic, competitive, or predatory relationships may have been more representative among microbes when conditions were less favorable in the estuary; however, such relationships require further exploration and validation in the field and lab. Coastal networks may also be driven by shifts in the abundance of certain taxonomic or functional groups. Sustained monitoring of microbial communities over environmental gradients, both spatial and temporal, is critical to predict microbial dynamics and biogeochemistry in future marine ecosystems.

Au ETHH@ZIF-8 based “three-in-one” multifunctional substrate with analyte enrichment, filtration and enhanced SERS performance

The application of surface-enhanced Raman scattering (SERS) spectroscopy to ultrasensitively and quantitatively analyze small molecules of interest in biological samples has been a challenging endeavor, on account of the signal contamination caused by a variety of macromolecules including lipids, proteins and nucleic acids. Herein, we designed a “three-in-one” multifunctional substrate (Au ETHH@ZIF-8) composed of Au elongated tetrahexahedron (Au ETHH) and zeolitic imidazolate framework-8 (ZIF-8) with analyte enrichment, filtration and enhanced SERS performance for the detection of 4-nitrobenzenethiol (4-NBT). Au ETHH nanocrystals with a high density of sharp edges and tips enhance SERS performance. The ZIF-8 shell with the porous structure surrounding the Au ETHH, allows those analytes of comparable or smaller size to the pore size to reach the surface of the Au ETHH and possesses moderate analyte enrichment property. In addition, the prepared Au ETHH@ZIF-8-based SERS sensor realized the ultrasensitive and quantitative detection of 4-NBT in whole blood.

Topology Prediction and Structural Controllability Analysis of Complex Networks Without Connection Information

In this article, we consider complex networks without connection information. The absence of global structure induces a great obstacle in the structural controllability analysis of these networks. Thus, a topology predicting method based on the connection probability matrix is proposed to provide a global structure for structural controllability analysis in this article. Furthermore, the modified principles of predicting global network topologies are established to acquire a more accurate global connection relationship. Eventually, the drive node set of these networks is determined by predicting global topologies. The accuracy of the proposed topology predicting method is verified by numerical simulations in the context of artificial networks and real networks. The results reveal that the global topology and structural controllability of complex networks with large scale and high edge density could be accurately predicted by utilizing the proposed method.

(Digital Presentation) Graphenated Carbon Nanotube Based MEMS Supercapacitors

The inherent advantages of MEMS (micro-electromechanical system) technology, including small size and cost-effective fabrication, make it ideal for numerous applications in a wide range of industries ranging from defense, automotive, medical, to consumer industries. For applications that require self-powered MEMS electronics, an integrated energy storage device is required. Due to their small size, excellent cycle life and high-power density, miniature supercapacitors are an excellent choice for such an integrated energy storage device. The development of electrode materials and electrode fabrication processes for supercapacitors are thus critical for the practical applications of MEMS technology in electronics.In this presentation, Faraday Technology Inc. and Duke University will discuss a novel 3D graphenated carbon nanotube (g-CNT) network with pseudocapacitive coatings as the electrode materials for fabricating high energy density MEMS supercapacitors. The g-CNT has a high surface area three-dimensional framework of the CNTs coupled with the high edge density of graphene (Figure 1 A), which represents a potential maximum in both charge density and surface area, and thus provide the enhanced capacitance.An innovative electrophoretic deposition (EPD) manufacturing process, based on the use of pulsed electric fields, has been developed for controlled, reproducible, and scalable deposition of g-CNTs on interdigitated electrodes (Figure 1 B). In addition, pseudocapacitive coatings (such as MnO2) have been electrodeposited on the g-CNT coated electrode to further increasing supercapacitor energy density. Figure 1 C shows no redox peaks, which is important for using this structure as a supercapacitor application. The square shape of the cyclic voltammogram shows that ions experience free flow through the 3-D g-CNT structure. The Charge/Discharge curves (Figure 1 D) indicate the areal energy density of g-CNT/MnO2 coated electrodes (either 50 or 100 cyclic voltametric deposition cycles) are 45 and 93 times higher than g-CNT coated electrodes, respectively.In summary, a scalable manufacturing process for fabricating g-CNT network with pseudocapacitive coatings as electrodes has been demonstrated and shown great potential in producing high energy density MEMS supercapacitors for energy harvesting applications. Acknowledgements: The financial support of DOD DMEA STTR program through grant No. HQ0727-21-P-0029 is acknowledged and National Institutes of Health under award number 1R21EY031271 is acknowledged. The information, data, or work presented herein was funded in part by National Aeronautics and Space Administration under Grant No. 80NSSC19K1027, the views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. Figure 1