Channel Connectivity Research Articles

Continuous Initial Breakdown Development of In‐Cloud Lightning Flashes

AbstractUsing a 30–250 MHz VHF interferometer, we observed a previously unreported mode of initial lightning development inside thunderclouds. This mode is defined by continuous VHF radiation spanning several km within the first few milliseconds of lightning initiation. Following flash initiation through fast positive breakdown at high altitudes above 9 km, the VHF radiation front of upward negative streamers ascended continuously at a speed of ∼1.0 × 106 m/s, forming a continuous initial breakdown burst (CIBB) about 2 km in length. For the two CIBBs analyzed, the long and narrow CIBB channel was traversed by dart leaders that occurred later in the flash, indicating that the CIBB channel belongs to what becomes the main conducting leader channel. In contrast to classic initial breakdown pulses (IBPs) with sub‐pulses superimposed on the rising edge, CIBBs produced a series of discrete, narrow LF pulses (<10 μs) with an average time interval of 0.20 and 0.14 ms, respectively. We speculate that a CIBB is a continuously developing negative streamer system in the high electric field region at high altitudes, with connections of internal plasma channels producing LF pulses. These results have implications for physical conditions conducive to the formation of a long and continuous negative streamer system.

Impact of Transition from Natural Grasslands Steppe to Monoculture Artificial Grasslands on Soil Food Webs in the Qinghai–Tibet Plateau

Addressing the imbalance of the livestock–forage–environment system on the Qinghai–Tibet Plateau (QTP), the extensive replacement of natural grasslands with artificial grasslands has been pursued to enhance forage yield and quality. Recognizing their pivotal role in soil ecology, soil nematodes serve as sensitive indicators of the soil ecosystem structure and function. In this context, we embarked on a field investigation aimed at discerning the impact of varying artificial grasslands on soil nematode communities and food webs, with the intent of identifying an optimal forage species through the lens of soil nematode dynamics in the temperate steppe of the QTP. Our findings indicate that artificial grasslands, on the whole, tend to augment the soil nematode diversity—as reflected in the increased Margalef richness—and modify the community structure. Notable enhancements were observed in the abundance of bacterivores and omnivores, the fungivore and omnivore biomass carbon, and the connectance within fungal and bacterial channels. Specific insights reveal that grasslands established with Elymus nutans and Elymus sinosubmuticus notably boost the Margalef richness, omnivore biomass carbon, and both functional and structural metabolic footprints, with E. sinosubmuticus grasslands uniquely elevating the fungal channel connectivity. Elymus sibiricus grasslands, in particular, were associated with increased fungivore biomass carbon and metabolic footprints, as well as increased connectance in fungal and omnivore–predator channels. In summation, E. sibiricus, E. nutans, and E. sinosubmuticus emerge as superior choices for artificial grassland cultivation on the QTP, as suggested by soil nematode indicators. The adoption of mixed-species sowing incorporating these three candidates potentially offers enhanced benefits to the soil food web, although this hypothesis warrants further investigation.

Enhanced diagnostics for generalized anxiety disorder: leveraging differential channel and functional connectivity features based on frontal EEG signals

Generalized Anxiety Disorder (GAD) is a chronic anxiety condition characterized by persistent excessive worry, anxiety, and fear. Current diagnostic practices primarily rely on clinicians’ subjective assessments and experience, highlighting a need for more objective and reliable methods. This study collected 10-minute resting-state electroencephalogram (EEG) from 45 GAD patients and 36 healthy controls (HC), focusing on six frontal EEG channels for preprocessing, data segmentation, and frequency band division. Innovatively, this study introduced the “Differential Channel” method, which enhances classification performance by enhancing the information related to anxiety from the data, thereby highlighting signal differences. Utilizing the preprocessed EEG signals, undirected functional connectivity features (Phase Lag Index, Pearson Correlation Coefficient, and Mutual Information) and directed functional connectivity features (Partial Directed Coherence) were extracted. Multiple machine learning models were applied to distinguish between GAD patients and HC. The results show that the Deep Forest classifier achieves excellent performance with a 12-second time window of DiffFeature. In particular, the classification of GAD and HC was successfully obtained by combining OriFeature and DiffFeature on Mutual Information with a maximum accuracy of 98.08%. Furthermore, it was observed that undirected functional connectivity features significantly outperformed directed functional connectivity when fewer frontal channels were used. Overall, the methodologies developed in this study offer accurate and practical identification strategies for the early screening and clinical diagnosis of GAD, offering the necessary theoretical and technical support for further enhancing the portability of EEG devices.

Multi-Domain Based Dynamic Graph Representation Learning for EEG Emotion Recognition.

Graph neural networks (GNNs) have demonstrated efficient processing of graph-structured data, making them a promising method for electroencephalogram (EEG) emotion recognition. However, due to dynamic functional connectivity and nonlinear relationships between brain regions, representing EEG as graph data remains a great challenge. To solve this problem, we proposed a multi-domain based graph representation learning (MD 2GRL) framework to model EEG signals as graph data. Specifically, MD 2GRL leverages gated recurrent units (GRU) and power spectral density (PSD) to construct node features of two subgraphs. Subsequently, the self-attention mechanism is adopted to learn the similarity matrix between nodes and fuse it with the intrinsic spatial matrix of EEG to compute the corresponding adjacency matrix. In addition, we introduced a learnable soft thresholding operator to sparsify the adjacency matrix to reduce noise in the graph structure. In the downstream task, we designed a dual-branch GNN and incorporated spatial asymmetry for graph coarsening. We conducted experiments using the publicly available datasets SEED and DEAP, separately for subject-dependent and subject-independent, to evaluate the performance of our model in emotion classification. Experimental results demonstrated that our method achieved state-of-the-art (SOTA) classification performance in both subject-dependent and subject-independent experiments. Furthermore, the visualization analysis of the learned graph structure reveals EEG channel connections that are significantly related to emotion and suppress irrelevant noise. These findings are consistent with established neuroscience research and demonstrate the potential of our approach in comprehending the neural underpinnings of emotion.

The influence of large wood on sediment routing and flow characteristics: A study in a low-order stream in the southern brazilian plateau

The coupling of sediment sources varies in terms of efficiency and availability based on the frequency and intensity patterns of rainfall events over time. It is commonly assumed that reaches with steeper gradients have higher longitudinal connectivity, while lower gradient reaches have lower longitudinal connectivity. However, considering that the longitudinal connectivity of channels is not always perfectly established, this conception regarding channel gradient may not always hold true. In forested regions, elements such as large wood can be the main agents of this disconnectivity. Thus, this study investigates the effects of large wood on sediment flux, flow characteristics and channel morphology in the context of the Atlantic Forest Biome in a low-order stream in the southern Brazilian plateau. To achieve this, measurements of hydrological variables, characterization of sediments, and channel morphology were conducted. Hydraulic and morphological variables were estimated to define hydraulic signatures for each river section. The influences of large wood barriers in the study section were highlighted through an analysis of hydraulic characteristics at different cross-sections and the assessment of morphological variables. The investigation revealed a tendency for a reduction in velocity of approximately 90 % in low-discharge conditions in sections where large wood barriers were present. In the case of high-magnitude events, the velocity reduction was considerably lower. Additionally, it was found that each large wood deposit presented uniqueness, and the structural characteristics of each reflected the potential for system disconnectivity. Finally, it was observed that both barriers have the capacity to store sediments, leading to the constriction of the cross-section due to the formation of sediment bars, although surveys revealed that fine sediments were poorly retained in both barriers.

G-EEGCS: Graph-based optimum electroencephalogram channel selection

Electroencephalography (EEG) is commonly used to measure brain activity in clinical research. However, the abundance of channels in EEG data poses several challenges, including increased computational complexity, noise interference, and decreased efficiency in data analysis. A new graph-based method called G-EEGCS has been developed to address these issues for EEG channel selection. The graph-based optimum EEG channel selection (G-EEGCS) method constructs a directed network by establishing channel connections based on their pairwise correlations. Statistical features are used to determine similarity, and an adjacency matrix is created to represent the connectivity between the EEG channels. The influence of each channel on information flow within the network is assessed using the centrality measures. By calculating the shortest paths between all channel pairs, the algorithm quantifies the probability of each channel serving as a bridge between different parts of the graph. Channels with high centrality scores, indicating their significance in the information flow, are given priority during the selection process. An adaptive threshold is applied to optimize channel selection, ensuring that only channels that exceed the threshold, exhibit specific characteristics, and align with the overall network structure are retained. This adaptive thresholding mechanism enhances the robustness and flexibility of the G-EEGCS method, enabling personalized channel selection tailored to individual EEG datasets. The G-EEGCS approach offers a promising solution for EEG channel selection, improving the interpretability and efficiency of EEG-based studies and applications for researchers. It achieved an average accuracy of 0.9125, outperforming state-of-the-art methods. This method provides a valuable means of addressing the challenges posed by the multitude of channels in EEG data, ultimately contributing to more effective and insightful analysis in clinical research.

Adaptive channel-weight dual-constrained semi-supervised EEG clustering

When processing Electroencephalography(EEG) for applications such as brain disease diagnosis, rehabilitation, and brain–computer interfaces, we encounter challenges related to missing labels or incorrect annotations. These challenges may arise from subjective factors, technical limitations, or data quality issues. In traditional EEG processing, unsupervised clustering algorithms are commonly used; however, due to the lack of labels, these methods may struggle to fully leverage latent information, resulting in uncertainty and instability in clustering outcomes. To address this issue, we propose a semi-supervised clustering method tailored for partially labeled EEG signals, named Adaptive Channel-weight Dual-constrained Semi-supervised EEG Clustering (ACDSEEGc). This method incorporates dual constraints: a connectivity constraint for the interconnections between EEG points and a neighborhood constraint for the spatial neighborhood relationships among EEG data points. Additionally, it integrates channel weights to emphasize information from important channels. The objective function of ACDSEEGc integrates cross-entropy evaluation, channel weight learning, pseudo-label learning, connectivity constraint, and least squares error minimization. Our aim is to maximize the use of limited prior knowledge to achieve better EEG clustering results. Experimental validation demonstrates that ACDSEEGc effectively produces superior clustering outcomes compared to state-of-the-art standard unsupervised and semi-supervised EEG/time-series clustering algorithms. Furthermore, we conducted sensitivity experiments and ablation studies to verify the performance of ACDSEEGc under different settings.

A General Image Super-Resolution Reconstruction Technique for Walnut Object Detection Model

Object detection models are commonly used in yield estimation processes in intelligent walnut production. The accuracy of these models in capturing walnut features largely depends on the quality of the input images. Without changing the existing image acquisition devices, this study proposes a super-resolution reconstruction module for drone-acquired walnut images, named Walnut-SR, to enhance the detailed features of walnut fruits in images, thereby improving the detection accuracy of the object detection model. In Walnut-SR, a deep feature extraction backbone network called MDAARB (multilevel depth adaptive attention residual block) is designed to capture multiscale information through multilevel channel connections. Additionally, Walnut-SR incorporates an RRDB (residual-in-residual dense block) branch, enabling the module to focus on important feature information and reconstruct images with rich details. Finally, the CBAM (convolutional block attention module) attention mechanism is integrated into the shallow feature extraction residual branch to mitigate noise in shallow features. In 2× and 4× reconstruction experiments, objective evaluation results show that the PSNR and SSIM for 2× and 4× reconstruction reached 24.66 dB and 0.8031, and 19.26 dB and 0.4991, respectively. Subjective evaluation results indicate that Walnut-SR can reconstruct images with richer detail information and clearer texture features. Comparative experimental results of the integrated Walnut-SR module show significant improvements in mAP50 and mAP50:95 for object detection models compared to detection results using the original low-resolution images.

Integrated Framework to Model Microstructure Evolution and Decipher the Microstructure-Property Relationship in Polymeric Porous Materials.

Unraveling the microstructure-property relationship is crucial for improving material performance and advancing the design of next-generation structural and functional materials. However, this is inherently challenging because it requires both the comprehensive quantification of microstructural features and the accurate assessment of corresponding properties. To meet these requirements, we developed an efficient and comprehensive integrated modeling framework, using polymeric porous materials as a representative model system. Our framework generates microstructures using a physics-based phase-field model, characterizes them using various average and localized microstructural features, and evaluates microstructure-aware properties, such as effective diffusivity, using an efficient Fourier-based perturbation numerical scheme. Additionally, the framework incorporates machine learning methods to decipher the intricate microstructure-property relationships. Our findings indicate that the connectivity of phase channels is the most critical microstructural descriptor for determining effective diffusivity, followed by the domain shape represented by curvature distribution, while the domain size has a minor impact. This comprehensive approach offers a novel framework for assessing microstructure-property relationships in polymer-based porous materials, paving the way for the development of advanced materials for diverse applications.

ICDW-YOLO: An Efficient Timber Construction Crack Detection Algorithm.

A robust wood material crack detection algorithm, sensitive to small targets, is indispensable for production and building protection. However, the precise identification and localization of cracks in wooden materials present challenges owing to significant scale variations among cracks and the irregular quality of existing data. In response, we propose a crack detection algorithm tailored to wooden materials, leveraging advancements in the YOLOv8 model, named ICDW-YOLO (improved crack detection for wooden material-YOLO). The ICDW-YOLO model introduces novel designs for the neck network and layer structure, along with an anchor algorithm, which features a dual-layer attention mechanism and dynamic gradient gain characteristics to optimize and enhance the original model. Initially, a new layer structure was crafted using GSConv and GS bottleneck, improving the model's recognition accuracy by maximizing the preservation of hidden channel connections. Subsequently, enhancements to the network are achieved through the gather-distribute mechanism, aimed at augmenting the fusion capability of multi-scale features and introducing a higher-resolution input layer to enhance small target recognition. Empirical results obtained from a customized wooden material crack detection dataset demonstrate the efficacy of the proposed ICDW-YOLO algorithm in effectively detecting targets. Without significant augmentation in model complexity, the mAP50-95 metric attains 79.018%, marking a 1.869% improvement over YOLOv8. Further validation of our algorithm's effectiveness is conducted through experiments on fire and smoke detection datasets, aerial remote sensing image datasets, and the coco128 dataset. The results showcase that ICDW-YOLO achieves a mAP50 of 69.226% and a mAP50-95 of 44.210%, indicating robust generalization and competitiveness vis-à-vis state-of-the-art detectors.

Soil freeze/thaw dynamics strongly influences runoff regime in a Tibetan permafrost watershed: Insights from a process-based model

The Tibetan Plateau (TP) is a region rich in extensive frozen ground and the source of many major Asian rivers. However, how soil freeze/thaw (F/T) dynamics influence runoff production at the catchment scale in the TP is poorly understood. This study employs a process-based permafrost hydrology model with a new soil parameterization to investigate soil F/T dynamics and their impact on runoff in a TP permafrost watershed, i.e., the source region of Yangtze River (SRYR). The revised model separates soil evaporation and plant transpiration, and accounts for the influence of soil gravel and organic carbon content, as well as variation in saturated hydraulic conductivity along the soil profile. Validation results demonstrate that the revised model accurately simulates daily soil temperature (mean RMSE of 1.3 °C), soil moisture (mean ubRMSE of 0.05 cm3 cm−3), and runoff discharge (NSE = 0.82). The results reveal different altitudinal patterns of warming trend between permafrost and seasonally frozen ground (SFG). Warming rates in SFG area increase monotonously with elevation, while a turning point is observed in permafrost region around 4800 m. With active layer deepening, deep-soil water content increases but primarily replenishes soil water storage rather than directly contributing to runoff recharge, while rootzone and the middle part of the active layer become drier. Soil F/T cycles in the permafrost region exert stronger influences on runoff process compared to SFG. Delayed soil thaw onset generally results in higher spring runoff coefficient, while delayed soil freeze onset is related to slower runoff recession. The freezing zero-curtain period is likely to impact the continuity of runoff recession processes by affecting the connectivity of groundwater flow channels. These findings uncover the regulatory mechanisms of soil F/T dynamics on runoff production and river discharge characteristics, providing a fundamental basis for predicting permafrost hydrology responses to future climate change in the TP.

Assessment of geomorphic status and recovery potential in anthropogenically altered river of Eastern India

ABSTRACT The study monitors chronological changes (1922–36 to 2023) in channel morphological status of the Mayurakshi River in Eastern India using the index of Geomorphic Status (GS). It encompasses Changes in Sedimentary Units (SU), Changes in Sediment Availability (SA), Changes in Bar Stability (BS) and Changes in Channel Flow (CF). The Index of Geomorphic Status (GS) reveals a sharp morphological degradation of the Mayurakshi River in the recent past. Therefore, a detailed checklist of channel width, bed-level, configuration dynamics and channel connectivity was prepared to identify medium- and short-term Channel Adjustment (CA). The study reveals that instream sand mining and structural interventions coupled with low discharge are the key factors hindering channel morphological recovery. Finally, a new Channel Recovery Potential Index (CRP) has been proposed by considering short-term channel adjustment, sediment connectivity, geomorphological setup and anthropogenic stresses. Preferred alternatives and optional groups assessed from the AHP-TOPSIS and AHP-VIKOR models were used to quantify the channel Recovery Potential. Outcome of the study reveals that reach 1 has more chance of channel recovery (TOPSIS: 0.77 and VIKOR: 0.00) compared to other sectors.

Fabrication of high-performance recrystallized silicon carbide ceramic membrane based on particle packing optimization

Overcoming the challenges associated with achieving high uniformity and connectivity of pore channels in ceramic membranes, we designed silicon carbide ceramic membrane derived from the recrystallization process based on the Dinger-Funk equation of the closest-packing model with various grain grading. Furthermore, the effects of particle size distribution on the resulting microstructure and pore architecture of the ceramic membrane was also explored. The findings corroborated the critical importance of raw material particle size distribution in controlling pore size distribution and morphology. After sintering at 1900°C, the silicon carbide ceramic membrane, benefiting from ideal particle packing, exhibited a remarkably uniform pore structure. Notably, the most probable pore size constituted over 70 %, while achieving an open porosity of 51.3 % even without the addition of pore-forming agents. The silicon carbide ceramic membrane also demonstrated exceptional hydrophilicity (water contact angle:∼0°), impressive water permeation (1210 L m−2 h−1·bar−1), coupled with efficient turbidity removal (∼100 %) in carbon black wastewater treatment applications. Additionally, membrane regeneration proved effective using a dilute NaOH solution backwash, achieving a flux recovery efficiency of 98 %. This strategy had directive significance for designing high-performing silicon carbide ceramic membranes.

Remote Sensing Image Classification Based on Neural Networks Designed Using an Efficient Neural Architecture Search Methodology

Successful applications of machine learning for the analysis of remote sensing images remain limited by the difficulty of designing neural networks manually. However, while the development of neural architecture search offers the unique potential for discovering new and more effective network architectures, existing neural architecture search algorithms are computationally intensive methods requiring a large amount of data and computational resources and are therefore challenging to apply for developing optimal neural network architectures for remote sensing image classification. Our proposed method uses a differentiable neural architecture search approach for remote sensing image classification. We utilize a binary gate strategy for partial channel connections to reduce the sizes of the network parameters, creating a sparse connection pattern that lowers memory consumption and NAS computational costs. Experimental results indicate that our method achieves a 15.1% increase in validation accuracy during the search phase compared to DDSAS, although slightly lower (by 4.5%) than DARTS. However, we reduced the search time by 88% and network parameter size by 84% compared to DARTS. In the architecture evaluation phase, our method demonstrates a 2.79% improvement in validation accuracy over a manually configured CNN network.

Numerical investigation of fluid flow behavior in steel cord with lattice Boltzmann methodology: The impacts of microstructure and loading force.

Steel cord materials were found to have internal porous microstructures and complex fluid flow properties. However, current studies have rarely reported the transport behavior of steel cord materials from a microscopic viewpoint. The computed tomography (CT) scanning technology and lattice Boltzmann method (LBM) were used in this study to reconstruct and compare the real three-dimensional (3D) pore structures and fluid flow in the original and tensile (by loading 800 N force) steel cord samples. The pore-scale LBM results showed that fluid velocities increased as displacement differential pressure increased in both the original and tensile steel cord samples, but with two different critical values of 3.3273 Pa and 2.6122 Pa, respectively. The original steel cord sample had higher maximal and average seepage velocities at the 1/2 sections of 3D construction images than the tensile steel cord sample. These phenomena should be attributed to the fact that when the original steel cord sample was stretched, its porosity decreased, pore radius increased, flow channel connectivity improved, and thus flow velocity increased. Moreover, when the internal porosity of tensile steel cord sample was increased by 1 time, lead the maximum velocity to increase by 1.52 times, and the average velocity was increased by 1.66 times. Furthermore, when the density range was determined to be 0-38, the pore phase showed the best consistency with the segmentation area. Depending on the Zou-He Boundary and Regularized Boundary, the relative error of simulated average velocities was only 0.2602 percent.

Using dynamic spatio-temporal graph pooling network for identifying autism spectrum disorders in spontaneous functional infrared spectral sequence signals

Background:Autism classification work on fNIRS data using dynamic graph networks. Explore the impact of the dynamic connection relationship between brain channels on ASD, and compare the brain channel connection diagrams of ASD and TD to explore potential factors that influence the development of autism. Method:Using dynamic graph construction to mine the dynamic relationships of fNIRS data, obtain spatio-temporal correlations through dynamic feature extraction, and improve the information extraction capabilities of the network through spatio-temporal graph pooling to achieve classification of ASD. Result:A classification effect with an accuracy of 97.2% was achieved using a short sequence of 1.75s. The results showed that the dynamic connections of channel 5 and 19, channel 12 and 25, and channel 7 and 34 have a greater impact on the classification of autism. Comparison with previously used method(s): Compared with previous deep learning models, our model achieves efficient classification using short-term fNIRS data of 1.75s, and analyzes the impact of dynamic connections on classification through dynamic graphs. Conclusion:Using Dynamic Spatio-Temporal Graph Pooled Neural Networks (DSTGPN), dynamic connectivity between brain channels was found to have an impact on the classification of autism. By modeling the brain channel relationship maps of ASD and TD, hyperlink clusters were found to exist on the brain channel connections of ASD.

A novel swarm intelligence‐based fuzzy logic in efficient connectivity of vehicles

SummaryA vehicular ad hoc network (VANET) is a wireless network composed of a collection of mobility devices that link via a wireless channel without the need for permanent infrastructure or centralized administration. Multicasting is a method of sending data from a single node to multiple recipients at the same time. Because of its energizing network topology and constricted resources, a mobile ad hoc network (MANET) presents a number of issues. Because network execution measurements such as latency, bandwidth, throughput, and energy fluctuate so rapidly owing to node movement, these network properties in a wireless mobile ad hoc network are ambiguous. Because of the influence of these uncertainty issues, determining the top‐grade path from the originating node to a set of recipient nodes is challenging. In order to preserve network resources, we used a Swarm Intelligence‐based fuzzy logic strategy in this study that includes channel caliber factor and connection termination time to try to alleviate these uncertainty issues. This strategy integrates all of the network indicators available for the routes into an individual quantity named as fuzzy cost or cost of communication. The routes with the lowest fuzzy cost will be regarded optimum, and data will be delivered along this route from the originating point to a set of receivers. The simulation was carried out using NS‐3.38 and MATLAB, and the results reveal that the suggested protocol, HACOFLM, outperforms ODMRP, OFAODV, MAODV, and DFMCRP by means of packet delivery ratio, latency, control packet overhead, and throughput. DFMCRP outperforms MAODV by 3% to 6%, OFAODV by 13% to 19%, and ODMRP by 16% to 23%. According to HACOSMO, based on the number of vehicles and velocities, routing overhead is 7% to 10% less than DFMCRP, 9% to 13% less than MAODV, 17% to 29% less than OFAODV, and 22% to 36% less than ODMRP. It improvise throughput by 4% to 6% when contrasted to DFMCRP, 5% to 9% when contrasted to MAODV, 10% to 14% when contrasted to OFAODV, and 14% to 23% when contrasted to ODMRP.

Monoliths for gas storage manufactured with precision pore engineering using 3D-printed templates

A fast, customizable, and systematic route for producing activated carbon monoliths for gas storage with low interparticle porosity (9 % to 24 %) containing tailored macro channels is disclosed. The production uses a three-dimensional (3D)-printed polymeric lattice, rich in oxygen, as a sacrificial template. Its interstitial voids are filled with a phenolic resin that is carbonized and activated at 1173 K under carbon dioxide (CO2). Sequential pyrolysis occurs via thermal treatment with the 3D-printed lattice removed first, rendering macro channels with the same shape as the template in accurately placed positions. Resulting in adsorbents with interparticle porosities ranging from 10 % to 23 %, calculated from X-ray micro-computed tomography (micro-CT) images. The axial resolution of these macro channels was similar to the SLA printing layer thickness used of 100 µm. Making it the best resolution attained to date in fabricating monoliths via a 3D-printed sacrificial templating method. Breakthrough experiments indicated healthy macro channel connectivity due to the reduced mass transfer zone. Moreover, excellent mechanical integrity was displayed, with a monolith withstanding 900 N before excessive fracturing occurred despite the presence of macro channels. Therefore, this method emphasizes its use for gas storage applications by enabling the adsorbent material to efficiently utilize the volumetric space while allowing a fast rate of adsorption over the entire structure.

Screen-printed electrodes on paper using copper nano- and micro-particles

Screen-printed copper electrodes have the potential to replace silver and lithium electrodes in printed batteries and sensors owing to the abundance of copper, its competitive electrical properties, and the low cost of raw materials. Herein, copper microparticle-glycerol-based inks were screen-printed onto paper to develop highly conductive electrodes. The conductivity and particle connectivity enhancement of screen-printed electrodes are the main research topics. These parameters translate to usability including increased sensitivity for sensor applications and reduced resistivity for battery electrode applications. CuO nanoparticles of 50 nm were deposited onto the electrodes at a concentration of 1 μl/cm2, which led to a 4-fold decrease in sheet resistivity from 200 to 50 Ω/m2. The increased connectivity and formation of new channels due to the addition of nanoparticles were visualised via field emission scanning electron microscopy. The CuO nanoparticles were developed via a hybrid approach incorporating wet chemistry for high yield and laser ablation for colloidal conductivity and Zeta potential enhancement which reached up to −45 mV. An optimal screen-printing viscosity of 62 mPa s was achieved through a precise 1.95:1 mass ratio of Cu microparticles to glycerol. Heat treatment at 100 °C after screen-printing enhanced particle connectivity by reducing glycerol content in the ink.

Monitoring surface-water connectivity in a coastal lowland river using field-based data loggers

The concept of hydrologic connectivity has commonly been used as an indicator of river habitat health and considered in environmental flow policies and river management schemes. Field-based data loggers, used to infer flow intermittency, have been used to study the connectivity in ephemeral channels in a variety of physiographic environments; however, no previous studies have utilized and analyzed them in portions of perennial channels and floodplains that experience inundation intermittently. To address this gap, we developed an array of Stream Temperature, Intermittency, and Conductivity Loggers (STICL) and deployed them in a coastal lowland river to analyze temporal changes to hydrologic connectivity induced by surface water in the longitudinal, lateral, and vertical dimensions of the channel and the floodplain. We examine the efficacy of STICLs to monitor surface-water connectivity within the floodplain that experience intermittent inundation. We also examine influence of hydrologic regime and geomorphology on the variations in connectivity. Our results show that the electrical conductivity (EC) records from our STICLs can capture flood inundation dynamics and how these dynamics affect surface-water connectivity. Our estimates of flood inundation duration and the dynamics of surface water connectivity can be improved by better accounting for how EC can vary with temperature and by handling the uncertainty in our STICLs readings in a more explicit manner. The STICLs provide a promising tool to study river hydromorphological processes in detail. Thus, we recommend the development of formal approaches and frameworks for deploying and analyzing these types of data loggers within lowland landscapes.