Layered Framework Research Articles

Layered Fusion Reconstruction Based on Fuzzy Features for Multi-Conductivity Electrical Impedance Tomography.

In medical imaging, detecting tissue anomalies is vital for accurate diagnosis and effective treatment. Electrical impedance tomography (EIT) is a non-invasive technique that monitors the changes in electrical conductivity within tissues in real time. However, the current challenge lies in simply and accurately reconstructing multi-conductivity distributions. This paper introduces a layered fusion framework for EIT to enhance imaging in multi-conductivity scenarios. The method begins with pre-imaging and extracts the main object from the fuzzy image to form one layer. Then, the voltage difference in the other layer, where the local anomaly is located, is estimated. Finally, the corresponding conductivity distribution is established, and multiple layers are fused to reconstruct the multi-conductivity distribution. The simulation and experimental results demonstrate that compared to traditional methods, the proposed method significantly improves multi-conductivity separation, precise anomaly localization, and robustness without adding uncertain parameters. Notably, the proposed method has demonstrated exceptional accuracy in local anomaly detection, with positional errors as low as 1% and size errors as low as 33%, which significantly outperforms the traditional method with respective minimum errors of 9% and 228%. This method ensures a balance between the simplicity and accuracy of the algorithm. At the same time, it breaks the constraints of traditional linear methods, struggling to identify multi-conductivity distributions, thereby providing new perspectives for clinical EIT.

A Zn/Cd/Ni Metal-Organic Framework-Based Solid and Flexible Fluoroprobes for Highly Selective Detection of 4-Nitroaniline in Real Water Samples.

The combination of diazabicyclic compound i.e. 1,4-diazabicyclo[2.2.2]octane (DABCO) and dicarboxylate linker 1,4-dicarboxylic acid are employed to self-assemble with three divalent metal ions such as Zn2+, Cd2+, Ni2+ led to the formation of highly stable pillar layered luminescent metal-organic frameworks. The emissive response of as-synthesized MOFs was studied in organic solvents with different functionalities resulting in the development of fluoroprobes having excellent recognition ability for 4-nitroaniline with detection limit upto micromolar. All the MOFs display a good linear relation between the fluorescence intensity and concentration of 4-NA in the range of 10-90µmol L-1. The values of Ksv for Zn-MOF, Cd-MOF, and Ni-MOF are found to be 1.75 × 104mol-1L, 1.25 × 104mol-1L, and 28.0 × 104mol-1L, respectively. The calculated detection limit values are 19.7µmol L-1, 27.6µmol L-1, and 1.19µmol L-1 respectively. Moreover, the study was further extended to fabricate the solid membrane-based fluoroprobe using a linear chitosan polysaccharide and act as an efficient solid fluoroprobe for the detection of 4-NA. This proposed synthesis of polymeric membrane facilitates the MOF@chitosan fluorophore to transfer its fluorescence-emissive nature to a solid state.

Multitier scalable clustering wireless network design approach using honey bee ratel optimization

Wireless sensor networks (WSNs) have emerged as a one of the most fascinating areas of lookup in the past few years principally due to its massive quantity of workable applications. However limited energy, node deployment strategy, routing techniques has considerable impact on the overall performance of network. This paper proposes sustainable honey bee ratel based hybrid wireless sensor network (SHHWSN) layering framework for improving load balancing and scalability issues in network. In SHHWSN tessellated hexagonal shape clusters are formed to cover entire geographical region without any gaps. Hybrid routing schema constrained with redundant data identification policy used in this, seeks to optimize energy utilization. The proposed SHHWSN was evaluated and validated by simulation, and the results were compared with earlier schematics where mostly one sided stochastic node deployments were followed. The experimental results show that SHHWSN has superior performance in terms of network alive nodes, delay, energy and throughput. The proposed SHHWSN obtained values of 50 nodes is 3, 0.061, 0.14, 0.89, 100 nodes is 3,0.037, 0.19, 0.551, 150 nodes is 13, 0.081, 0.23, 0.415, and 200 nodes is 23, 1.01, 0.25, 0.356, which is significantly more effective than current techniques.

Supramolecular frameworks assembled by [EuL2]3+ units with different coordination configurations

Self-assembly is an important strategy to construct supramolecular frameworks. Small change in the assembling units often leads to significant variations in the self-assembly structures. In the work, we obtain two supramolecular frameworks assembled by different [EuL2]3+ units (L = tris((5,6-dimethyl-1H-benzo[d]imidazol-2-yl)methyl) amine). One meso compound {[EuⅢL2]·(H2O)6·Cl3} (Meso-EuL2) is assembled by an achiral meso-[EuL2]3+ unit, forming a supramolecular three-dimensional framework through six-fold intermolecular π‒π interactions. The other is a racemic compound {[EuⅢL2]·(CH3OH)5·Cl3} (Rac-EuL2), assembled by two chiral rac-[EuL2]3+ units predominantly via hydrogen bonding, leading to a layered supramolecular framework. The luminescent properties are different between Meso-EuL2 and Rac-EuL2, with Meso-EuL2 exhibiting more excimer-like emissions, which is likely caused by its stronger π‒π interactions in the structure.

Understanding Electrolyte Ion Size Effects on the Performance of Conducting Metal-Organic Framework Supercapacitors.

Layered metal-organic frameworks (MOFs) have emerged as promising materials for next-generation supercapacitors. Understanding how and why electrolyte ion size impacts electrochemical performance is crucial for developing improved MOF-based devices. To address this, we investigate the energy storage performance of Cu3(HHTP)2 (HHTP = 2,3,6,7,10,11-hexahydroxytriphenylene) with a series of 1 M tetraalkylammonium tetrafluoroborate (TAABF4) electrolytes with different cation sizes. Three-electrode experiments show that Cu3(HHTP)2 exhibits an asymmetric charging response with all ion sizes, with higher energy storage upon positive charging and a greater charging asymmetry with larger TAA+ cations. The results further show that smaller TAA+ cations demonstrate superior capacitive performances upon both positive and negative charging compared to larger TAA+ cations. To gain further insights, electrochemical quartz crystal microbalance measurements were performed to probe ion electrosorption during charging and discharging. These reveal that Cu3(HHTP)2 has a cation-dominated charging mechanism, but interestingly indicate that the solvent also participates in the charging process with larger cations. Overall, the results of this study suggest that larger TAA+ cations saturate the pores of the Cu3(HHTP)2-based electrodes. This leads to more asymmetric charging behavior and forces solvent molecules to play a role in the charge storage mechanism. These findings significantly enhance our understanding of ion electrosorption in layered MOFs, and they will guide the design of improved MOF-based supercapacitors.

Chaos quantum optimization‐based layered diagnosis framework for faulty sensor node diagnosis and classification in wireless sensor networks

SummaryNode faults are often influenced by factors such as physical component failure, communication module errors, battery exhaustion, and environmental factors. Various researchers have contributed to addressing the problem of detecting, diagnosing, and classifying faults in sensor networks. However, computational complexity and fault occurrence probability increase with the size of the network. In this context, an enhanced optimized layered diagnosis framework (OLDF) is proposed for detecting, diagnosing, and classifying sensor node faults. OLDF utilizes a deep belief network (DBN) with chaos quantum‐behaved particle swarm optimization (CQ‐PSO) for efficient diagnosis and classification of various sensor faults in the network. The proposed OLDF consists of two layers. In the first layer, optimized DBN with CQ‐PSO algorithm is employed to determine the fault type of the sensor nodes. Then, the fault type of sensor nodes that are classified in the first layer is given as input into the second layer of the OLDF algorithm to determine the fault severity in the network. Experimentation has been carried out by using the NS3 simulator for the OLDF framework with performance metrics such as classification accuracy, diagnosis accuracy, false positives, fault alarm rate, and average energy consumption. The validity of the proposed framework has been verified through comparisons with existing works such as MFD, INSA, FDRFC, and HFD. From the comparative analysis carried out, it is apparent that the proposed framework achieves a high classification accuracy of 89.94% and diagnosis accuracy of 93.21%, with less false positive rate, and utilizes minimum energy when compared with its counterparts.

K2[(VOHPO4)2(C2O4)]·2H2O as a high‐potential cathode material for potassium‐ion batteries

AbstractPotassium‐ion batteries (KIBs) represent a promising energy storage solution owing to the abundance of potassium resources. The efficacy of KIBs relies significantly on the electrochemical attributes of both their electrode materials and electrolytes. In the current investigation, we synthesized a layered compound K2[(VOHPO4)2(C2O4)]·2H2O via a heterogeneous nucleation approach and assessed its viability as a cathode material for KIBs. When integrated with a salt‐concentrated electrolyte with oxidation stability over 6 V, the compounds exhibit a high discharge potential of 4.1 V (vs. K+/K) alongside a reversible capacity of 106.2 mAh g−1. Furthermore, there is no capacity decay after 500 cycles at 100 mA g−1. This study shows the promise of layered metal organic frameworks as high‐potential materials for KIBs.

Fully automated operational modal identification based on scale-space peak picking algorithm and power spectral density estimation

Modal analysis is a fundamental and essential research direction in the field of structural engineering. The ultimate goal is to determine the modal parameters of the structures. However, the existing modal analysis algorithms often require a large number of parameter adjustments and manual intervention during operation, which cannot be fully automated. In order to realize the automatic identification of modal parameters, the automatic operational modal identification method (AOMI) is proposed based on the interpolated power spectral density estimation (IPSE). To achieve more precise spectrum analysis in the low-frequency band, IPSE is employed to perform Fourier transform on the original frequency domain segment with optimized frequency resolution. This enhances the sharpness of the obtained spectrum in the low-frequency range, making peak frequencies more discernible. Subsequently, the scale-space peak picking algorithm is used to automatically obtain the peak of the power spectral density (PSD), thus enabling the automatic identification of the natural frequency. Finally, the frequency domain decomposition method (FDD) is used to identify modal parameters based on the natural frequencies. The effectiveness of AOMI is verified through the modal identification of the old steel truss bridge and the three layer framework. Under the environmental excitation, the frequencies identified by the IPSE method is close to that of FDD, Bayesian fast fourier transform (FFT) and covariance driven stochastic subspace identification (SSI-COV). Furthermore, the PSD obtained through IPSE has sharper peak than that of FDD and the Welch’s method. The damping ratio identification accuracy and modal assurance criterion (MAC) are satisfactory in AOMI, which can improve the automatic performance.

Covalent Organic Frameworks: Linkage Chemistry and Its Critical Role in The Evolution of π Electronic Structures and Functions.

Covalent organic frameworks (COFs) provide a molecular platform for designing a novel class of functional materials with well-defined structures. A crucial structural parameter is the linkage, which dictates how knot and linker units are connected to form two-dimensional polymers and layer frameworks, shaping ordered π-array and porous architectures. However, the roles of linkage in the development of ordered π electronic structures and functions remain fundamental yet unresolved issues. Here we report the designed synthesis of COFs featuring four representative linkages: hydrazone, imine, azine, and C=C bonds, to elucidate their impacts on the evolution of π electronic structures and functions. Our observations revealed that the hydrazone linkage provides a non-conjugated connection, while imine and azine allow partial π conjugation, and the C=C bond permits full π-conjugation. Importantly, the linkage profoundly influences the control of π electronic structures and functions, unraveling its pivotal role in determining key electronic properties such as band gap, frontier energy levels, light absorption, luminescence, carrier density and mobility, and magnetic permeability. These findings highlight the significance of linkage chemistry in COFs and offer a general and transformative guidance for designing framework materials to achieve electronic functions.

Covalent Organic Frameworks: Linkage Chemistry and Its Critical Role in The Evolution of π Electronic Structures and Functions

AbstractCovalent organic frameworks (COFs) provide a molecular platform for designing a novel class of functional materials with well‐defined structures. A crucial structural parameter is the linkage, which dictates how knot and linker units are connected to form two‐dimensional polymers and layer frameworks, shaping ordered π‐array and porous architectures. However, the roles of linkage in the development of ordered π electronic structures and functions remain fundamental yet unresolved issues. Here we report the designed synthesis of COFs featuring four representative linkages: hydrazone, imine, azine, and C=C bonds, to elucidate their impacts on the evolution of π electronic structures and functions. Our observations revealed that the hydrazone linkage provides a non‐conjugated connection, while imine and azine allow partial π conjugation, and the C=C bond permits full π‐conjugation. Importantly, the linkage profoundly influences the control of π electronic structures and functions, unraveling its pivotal role in determining key electronic properties such as band gap, frontier energy levels, light absorption, luminescence, carrier density and mobility, and magnetic permeability. These findings highlight the significance of linkage chemistry in COFs and offer a general and transformative guidance for designing framework materials to achieve electronic functions.

Significant enhancement of proton conductivity in solid acid at the monolayer limit

Proton transport in nanofluidic channels is not only fundamentally important but also essential for energy applications. Although various strategies have been developed to improve the concentration of active protons in the nanochannels, it remains challenging to achieve a proton conductivity higher than that of Nafion, the benchmark for proton conductors. Here, taking H3Sb3P2O14 and HSbP2O8 as examples, we show that the interactions between protons and the layer frameworks in layered solid acid HnMnZ2O3n+5 are substantially reduced at the monolayer limit, which significantly increases the number of active protons and consequently improves the proton conductivities by ∼8 ‒ 66 times depending on the humidity. The membranes assembled by monolayer H3Sb3P2O14 and HSbP2O8 nanosheets exhibit in-plane proton conductivities of ~ 1.02 and 1.18 S cm−1 at 100% relative humidity and 90 °C, respectively, which are over 5 times higher than the conductivity of Nafion. This work provides a general strategy for facilitating proton transport, which will have broad implications in advancing both nanofluidic research and device applications from energy storage and conversion to neuromorphic computing.

Inner-outer layer co-optimization of sizing and energy management for renewable energy microgrid with storage

Conventional microgrid optimization schemes fall short in achieving global optimality for both sizing and scheduling aspects. In response to the demand for simultaneous optimization, this paper presents a novel inner-outer layer framework that includes an outer layer dedicated to sizing optimization and an inner layer focused on Energy Management System (EMS) operational optimization. This approach has a distinctive mechanism of two-layer interaction, where the outer layer focuses on the size assignment and renewal, while the inner layer is constrained by the size assignment and provides feedback on the corresponding operational cost to the outer layer. It enhances the capability of global optimization since the optimal sizes are searched within a fixed space. To establish the range of initial generation values, the framework commences with a pre-optimization phase. Based on the convexity nature of optimization problems, a combined solution methodology involving Rolling Horizon Optimization (RHO) and Particle Swarm Optimization (PSO) is implemented. The case study demonstrates that the algorithm exhibits a good convergence within 30 generations, reaching a global satisfactory solution with a population size of 50. Furthermore, our analysis of optimal sizes across diverse scenarios fills the research gap in understanding the relationship between optimal sizes and operational scenarios. These results underscore that the impact of the time-of-day tariff strategy is significant, and it is risky to determine the optimal sizes by rule-based strategies. Finally, the comparison to other co-optimization methods shows that the proposed framework can reduce the annual total cost by up to 23.1%, which highlights its greater capability of global optimization.

Flexible interdigitated layered framework with multiple accessible active sites for high-performance CH3I capture

Flexible interdigitated layered framework with multiple accessible active sites for high-performance CH3I capture

A Layered Hydrogen-Bonded Organic Framework with C3H6-Preferred Pores for Efficient One-Step Purification of Methanol-to-Olefins (MTO) Products

A Layered Hydrogen-Bonded Organic Framework with C₃H₆-Preferred Pores for Efficient One-Step Purification of Methanol-to-Olefins (MTO) Products

Ultra-sensitive and stable All-Fiber iontronic tactile sensors under high pressure for human movement monitoring and rehabilitation assessment

Interfacial capacitance sensing technology shows great potential in the fields of flexible electronics and healthcare. However, further advancements are necessary to enhance the sensitivity and mechanical stability of all-fiber iontronic tactile sensors (ITSs), especially under high pressure. This study addresses these challenges by optimizing the electrode structure and composition, incorporating hydrophilic cotton fibers as the dielectric layer framework. As a result, the ITSs exhibit ultrahigh sensitivity (58853 kPa−1), rapid response times (below 2 ms), and improved mechanical stability, enduring 10,000 cycles at 195.2 kPa. Moreover, these sensors have proven effective in detecting human motion and physiological signals in both air and aquatic environments. Integration into a motion state recognition system demonstrates potential applications in sports injury recovery, stroke rehabilitation, and chronic pain management.

Deep-SDM: A Unified Computational Framework for Sequential Data Modeling Using Deep Learning Models

Deep-SDM is a unified layer framework built on TensorFlow/Keras and written in Python 3.12. The framework aligns with the modular engineering principles for the design and development strategy. Transparency, reproducibility, and recombinability are the framework’s primary design criteria. The platform can extract valuable insights from numerical and text data and utilize them to predict future values by implementing long short-term memory (LSTM), gated recurrent unit (GRU), and convolution neural network (CNN). Its end-to-end machine learning pipeline involves a sequence of tasks, including data exploration, input preparation, model construction, hyperparameter tuning, performance evaluations, visualization of results, and statistical analysis. The complete process is systematic and carefully organized, from data import to model selection, encapsulating it into a unified whole. The multiple subroutines work together to provide a user-friendly and conducive pipeline that is easy to use. We utilized the Deep-SDM framework to predict the Nepal Stock Exchange (NEPSE) index to validate its reproducibility and robustness and observed impressive results.

A layered multifunctional framework based on polyacrylonitrile and MOF derivatives for stable lithium metal anode

Composite Li metal anodes based on three-dimensional (3D) porous frameworks have been considered as an effective material for achieving stable Li metal batteries with high energy density. However, uneven Li deposition behavior still occurs at the top of 3D frameworks owing to the local accumulation of Li ions. To promote uniform Li deposition without top dendrite growth, herein, a layered multifunctional framework based on oxidation-treated polyacrylonitrile (OPAN) and metal-organic framework (MOF) derivatives was proposed for rationally regulating the distribution of Li ions flux, nucleation sites, and electrical conductivity. Profiting from these merits, the OPAN/carbon nano fiber-MOF (CMOF) composite framework demonstrated a reversible Li plating/stripping behavior for 500 cycles with a stable Coulombic efficiency of around 99.0% at the current density of 2 mA/cm2. Besides, such a Li composite anode exhibited a superior cycle lifespan of over 1300 h under a low polarized voltage of 18 mV in symmetrical cells. When the Li composite anode was paired with LiFePO4 (LFP) cathode, the obtained full cell exhibited a stable cycling over 500 cycles. Moreover, the COMSOL Multiphysics simulation was conducted to reveal the effects on homogeneous Li ions distribution derived from the above-mentioned OPAN/CMOF framework and electrical insulation/conduction design. These electrochemical and simulated results shed light on the difficulties of designing stable and safe Li metal anode via optimizing the 3D frameworks.

Fluorination Effect on Lithium- and Manganese-Rich Layered Oxide Cathodes.

Lithium- and manganese-rich (LMR) layered oxides are promising high-energy cathodes for next-generation lithium-ion batteries, yet their commercialization has been hindered by a number of performance issues. While fluorination has been explored as a mitigating approach, results from polycrystalline-particle-based studies are inconsistent and the mechanism for improvement in some reports remains unclear. In the present study, we develop an in situ fluorination method that leads to fluorinated LMR with no apparent impurities. Using well-defined single-crystal Li1.2Ni0.2Mn0.6O2 (LNMO) as a platform, we show that a high fluorination level leads to decreased oxygen activities, reduced side reactions at high voltages, and a broadly improved cathode performance. Detailed characterization reveals a particle-level Mn3+ concentration gradient from the surface to the bulk of fluorinated-LNMO crystals, ascribed to the formation of a Ni-rich LizNixMn2-xO4-yFy (x > 0.5) spinel phase on the surface and a "spinel-layered" coherent structure in the bulk where domains of a LiNi0.5Mn1.5O4 high-voltage spinel phase are integrated into the native layered framework. This work provides fundamental understanding of the fluorination effect on LMR and key insights for future development of high-energy Mn-based cathodes with an intergrown/composite crystal structure.

Social Dimensions of Antimicrobial Resistance and an Anthropological Approach: Analytic Review

Purpose: The objective of this paper is to devise a comprehensive and layered framework to tackle antimicrobial resistance (AMR). It aims to weave together the strands of ethnomedicine, healthcare system analysis, and anthropological perspectives on illness, aligning with universal healthcare principles to encourage community participation and address the societal roots of health inequalities. The framework seeks to address AMR through a lens that views health disparities not merely as medical issues, but as complex phenomena shaped by cultural, social, and structural factors.
 Methodology: The research employs a single-case study design to synthesize and analyse interdisciplinary insights into AMR. This approach facilitates an in-depth understanding of how various elements, such as cultural beliefs, healthcare practices, and community dynamics, interact and influence the spread and management of AMR. By focusing on a single case, the study intends to meticulously document and interpret the nuanced interactions between these factors, providing a detailed narrative that captures the essence of the AMR challenge in a global health context. The study utilizes 66 varied references, such as journal articles, book excerpts, theses, reports, and websites, sourced from academic venues and the internet, published between 1946 and 2023.
 Findings: Through its investigation, the study presents a healthcare approach that marries the traditional wisdom of ethnomedicine with the precision of biomedicine, underscoring the significance of cultural competence in formulating AMR mitigation strategies. It dissects the intricate relationship between the three primary health sectors—popular, professional, and folk—and disentangles the sociocultural concepts of 'disease' and 'illness' as distinguished by medical anthropologists. The research calls for a reconceptualization of healthcare systems that goes beyond the biomedical model, advocating for an integration of the sociocultural, economic, and political dimensions that influence health and illness manifestations.
 Unique contribution to theory: This study's unique theoretical contribution lies in its interdisciplinary approach to health disparities and AMR. It proposes a model that balances the rigor of scientific research with the insights gleaned from traditional health practices, placing a premium on the dynamics of community involvement and the myriad influences on health. By doing so, it offers a more equitable, sustainable, and contextually relevant paradigm for health practices and policies. This paradigm shift is intended to provide actionable insights for policymakers and health practitioners, enabling them to devise strategies that are not only scientifically sound but also culturally sensitive and broadly applicable. The framework envisages a future where health interventions are tailored to the lived experiences of diverse populations, potentially transforming the landscape of global health and AMR strategy.

Imidazole/pyridine-based ionic liquids modified metal-organic frameworks for efficient adsorption of Congo red in water

In this work, a metal-organic framework (MOF), {[Ba2L(NO3) (DMF)2] (DMF·H2O)}n, denoted as HBU-167, was synthesized under solvothermal conditions. HBU-167 obtained a 3D layer framework comprising the imidazole carboxylic acid ligand and Ba2+ as nodes. It was demonstrated to have an adsorption capacity for Congo red (CR), with an adsorption capacity of 407.37 mg•g−1 and a removal rate of 65.46 %. To improve the adsorption capacity of HBU-167, simply impregnated three imidazole/pyridine-based ionic liquids (ILs) onto HBU-167 to obtain IL@HBU-167 for efficient CR adsorption. The results of Powder X-ray diffraction, Fourier transform infrared spectroscopy, and scanning electron microscopy revealed that ILs successfully bonded to HBU-167. Adsorption experiments on CR revealed that IL@HBU-167 exhibited increased stability as well as improved adsorption capacity. The adsorption capabilities of the IL@HBU-167 were 440.70 mg•g−1, 491.23 mg•g−1, and 544.56 mg•g−1, respectively, while the removal rates were 72.96 %, 75.00 %, and 83.28 %, respectively. Batch experiments were applied to analyze the impact of factors such as contact time, initial concentration, pH, temperature, and ionic strength on the adsorption of CR by IL@HBU-167. The maximum adsorption capacity was observed at 25 °C and pH = 6. The kinetic data indicated that the adsorption conformed to the pseudo-second-order kinetic model. The Langmuir isothermal model accurately described isothermal data. Furthermore, the adsorption capacity of IL@HBU-167 was investigated in real water samples from various sources. The results showed that IL@HBU-167 can be used as an innovative and inexpensive adsorbent for the removal of CR from water. The adsorption capacity was mainly derived from π-π, electrostatic interactions, and hydrogen bonding between IL@HBU-167 and CR.