Variation Of Conductivity Research Articles

Thermal conductivity assessment of cotton fibers from apparel recycling for building insulation

The impressive growth of the clothing market in the last decades comes along with an increase in the textile waste, nowadays mostly incinerated or landfilled. To improve the circularity of the sector, the possibility to recycle the textile waste into thermal insulation products for the building envelope has started to emerge. While the scientific literature agrees that the thermal conductivity of products based on textile waste is comparable to conventional thermal insulators, very few studies provide a comprehensive characterization of their heat transfer behavior in terms of the relevant parameters. This study focuses on post-consume cotton in the form of loose fibers with density 30, 50 and 70 kg/m3. By exploiting complementary experimental techniques, it is found that the effective thermal conductivity ranges between 0.0381 W/(m∙K) (ρ = 30 kg/m3, T = 10 °C, RH = 17 %) and 0.0546 W/(m∙K) (ρ = 50 kg/m3, T = 30 °C, RH = 80 %). The conductivity of the loose cotton fibers is predominantly sensitive to temperature and relative humidity, while the influence of density and the vertical/horizontal orientation appear less significant. These results highlight the complexity of the characterization of the thermal performance of such fibrous materials and the need to perform tests under fully controlled environmental conditions. Moreover, they are key for an accurate prediction of the performance of such insulating materials in real building operation, possibly achieved through dynamic energy simulations including heat and vapor transfer and modelling the conductivity variation with temperature and moisture.

Resurrected microorganisms: a plethora of resting bacteria underway for human interaction

Glaciers, which form due to the accumulation of snow, play a crucial role in providing freshwater resources, supporting river systems, and maintaining ecosystem stability. Pakistan is habitat to over 5000 glaciers, primarily located in the Hindukash, Himalaya, and Karakoram mountain ranges. Understanding the microbial communities thriving in these extreme environments becomes of utmost importance. These glaciers offer a unique perspective on extremophile adaptation, as they harbor microorganisms that are capable of surviving and thriving under harsh conditions. Glacial melting poses a significant threat to ancient microbiomes, potentially leading to the resurgence of epidemics and exposure of life to paleomicrobiota. Mostly glacial microbiome is evenly distributed and shows similar diversity. With the threat of resurrection of ages old microbiome and its incorporation into the waters have raised a major concern for revival of epidemics and exposure of life with paleanmicrobiota again. This has led the scientist to deeply observe the bacterial flora embedded in the cryonite holes of glaciers. This study aims to investigate the bacterial diversity within various glaciers of Pakistan using metagenomic techniques. Kamri, Burzil, Siachin, Baltoro, Shigar Basin, Biafo and Panama Glaciers designated from G1 to G7 respectively were chosen from Pakistan. Through rigorous physicochemical analyses, distinct characteristics among glaciers are revealed, including variations in temperature, depth, electrical conductivity, pH levels, and nutrient concentrations. The exploration of alpha diversity, employing metrics such as Chao1, Shannon, Simpson, and Inverse Simpson indices, offers valuable insights into the richness, evenness, and dominance of species within different samples. Beta diversity was calculated by using R software. The vegan package was used for NMSD, cluster and PCoA analysis based on Bray–Curtis distance. PCA analysis was done by using prcomp package from R software. Based on OTU abundance and environmental factor data, DCA analysis was done to determine the linear model from the gradient value (RDA) and the unimodal model (CCA). results were compiled by drawing cluster dendrogram which predicts the patterns of similarity and dissimilarity between different samples. Notably, phyla Proteobacteria emerge as the dominant phylum, accompanied by Actinobacteria, Firmicutes, and Bacteroidetes. The dendrogram shows five clusters, with close similarity between G1 and G4, glacier samples G3 and G8, and G2 and G7. Seasonal variations in glacier physicochemical properties were also observed, with summer samples having shallower depths, lower temperatures, and slightly acidic pH. In contrast, winter samples have higher electrical conductivity and sulfur content. Ultimately, this research provides a foundational framework for comprehending glacier ecosystems, their resident microbial communities, and their broader ecological significance. The study highlights the potential public health risks linked to the release of ancient microorganisms due to climate change, emphasizing the need for comprehensive monitoring and research to mitigate potential public health threats.

The impact of spatiotemporal variation of composite conductivity in carbonate reservoirs on well production after fracturing

Composite technology of hydraulic and acid fracturing for carbonate reservoirs has been conducted in the Taiyuan formation of Changqing Oilfield and has achieved good production effects. However, in long-term production, the fracture flow conductivity will inevitably decrease, which will affect the overall production capacity. Therefore, it is necessary to conduct simulation work to clarify the fracture conductivity and production decline law under the composite technology of hydraulic and acid fracturing. The general discrete element method involves unstructured refinement of the mesh around the fracture, which can improve simulation accuracy but also increase computational workload and difficulty. Based on the above situation, we used the EDFM (Embedded Discrete Fracture Model) method to simulate the production of carbonate reservoirs containing artificial fractures. The results show that there is a spatiotemporal decrease in composite conductivity formed by the composite technology of hydraulic and acid fracturing, whether it is the main or branch fractures. The decrease in fracture conductivity has a significant impact on production. The more proppant is filled in the branch fracture, the greater the production of the gas well. Therefore, in actual production, filling some small particle size proppants in the branch fracture can improve the yield increase effect.

Sensitivity Matrix Update Method for Electrical Resistance Tomography Based on Error-Constrained Cross Fusion Residual Attention Network

Abstract Electrical resistance tomography (ERT) is an imaging technique of conductivity distribution. The image reconstruction algorithm based on the empty field sensitivity matrix is widely used because it provides an approximate solution to the complex ERT inverse problems. However, due to the soft field effect, the sensitivity matrix changes with the media distribution, leading to significant errors in image reconstruction. To address this issue, an error-constrained deep learning scheme for bubble flow, namely cross fusion residual attention network (CFRA-Net) is designed to update the sensitivity matrix during gas-liquid-solid fluidized bed operation. CFRA-Net employs a multi-head self-attention mechanism to enhance bubble features in boundary measurements, a multilevel cross fusion module to fuse empty field strength and boundary measurement information, and a novel serial-channel residual spatial attention block to boost the feature extraction capability of the model. A weighted loss function is designed to give more weight to the gas phase distribution region. Additionally, this paper establishes a dataset for gas-liquid-solid three-phase flow, considering background field conductivity variations. Validation and reliability of the proposed method are assessed through ablation, comparison, and noise experiments. The experimental results demonstrate that CFRA-Net achieves rapid updates of the sensitivity matrix, high imaging accuracy, and robust noise immunity.

Evaporated Copper‐Based Perovskite Dynamic Memristors for Reservoir Computing Systems

AbstractDynamic memristors are considered as the optimal hardware devices for reservoir computing (RC) enabled by their nonlinear conductance variations. This significantly reduces the extensive training workload typically required by traditional neural networks. Lead halide perovskites, with their tunable band structure and active ion migration properties, have emerged as highly promising materials for developing dynamic memristors. However, large‐scale and consistently stable production remains a challenge for perovskite functional films, while lead elements' toxicity and environmental impact also partly restrict their practical device utilization. In this work, lead‐free copper‐based perovskite (i.e., CsCu2I3) films are prepared by thermal evaporation for constructing dynamic memristors. The effective conductivity modulation of CsCu2I3‐based memristor can be utilized in artificial neural networks, achieving a high handwritten digit recognition accuracy of 91.2%. In addition, the RC system is also constructed based on the dynamic behavior of the devices, by which a letter recognition accuracy of 98.2% with simple training is achieved. This technology provides a feasible pathway to construct copper‐based perovskite dynamic memristors for future neural network information processing.

High photosynthesis rates in Brassiceae species are mediated by leaf anatomy enabling high biochemical capacity, rapid CO2 diffusion and efficient light use.

Certain species in the Brassicaceae family exhibit high photosynthesis rates, potentially providing a valuable route toward improving agricultural productivity. However, factors contributing to their high photosynthesis rates are still unknown. We compared Hirschfeldia incana, Brassica nigra, Brassica rapa and Arabidopsis thaliana, grown under two contrasting light intensities. Hirschfeldia incana matched B. nigra and B. rapa in achieving very high photosynthesis rates under high growth-light condition, outperforming A. thaliana. Photosynthesis was relatively more limited by maximum photosynthesis capacity in H. incana and B. rapa and by mesophyll conductance in A. thaliana and B. nigra. Leaf traits such as greater exposed mesophyll specific surface enabled by thicker leaf or high-density small palisade cells contributed to the variation in mesophyll conductance among the species. The species exhibited contrasting leaf construction strategies and acclimation responses to low light intensity. High-light plants distributed Chl deeper in leaf tissue, ensuring even distribution of photosynthesis capacity, unlike low-light plants. Leaf anatomy of H. incana, B. nigra and B. rapa facilitated effective CO2 diffusion, efficient light use and provided ample volume for their high maximum photosynthetic capacity, indicating that a combination of adaptations is required to increase CO2-assimilation rates in plants.

Junctionless accumulation-mode SOI ferroelectric FinFET for synaptic weights

In this work, a novel silicon-on-insulator (SOI) based junctionless-accumulation-mode (JAM) ferroelectric (FE) fin field effect transistor (FinFET) is proposed along with its fabrication process flow at a 3-nm node for synaptic weights. The proposed JAM FE FinFET device can be easily integrated with the fabrication flow of p-FinFET in SOI process flow. The proposed device can be easily incorporated into standard FinFET SOI technology and thus is very attractive with respect to previously proposed devices. Further, using a well-calibrated 3D TCAD simulation setup, we show that the device effectively replicates the behavior required for neuromorphic computing applications. The outcomes of the proposed study emphasize the significance of using the JAM FE FinFET as a synaptic weight device that exhibits a 76 % higher nonvolatile conductance range in the ON-state over existing junctionless and conventional FE FinFET devices. Our simulations show that the proposed device offers continuous linear conductance variation and symmetric switching characteristics, which are essential for neuromorphic applications.

Creating cell-specific computational models of stem cell-derived cardiomyocytes using optical experiments.

Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) have gained traction as a powerful model in cardiac disease and therapeutics research, since iPSCs are self-renewing and can be derived from healthy and diseased patients without invasive surgery. However, current iPSC-CM differentiation methods produce cardiomyocytes with immature, fetal-like electrophysiological phenotypes, and the variety of maturation protocols in the literature results in phenotypic differences between labs. Heterogeneity of iPSC donor genetic backgrounds contributes to additional phenotypic variability. Several mathematical models of iPSC-CM electrophysiology have been developed to help to predict cell responses, but these models individually do not capture the phenotypic variability observed in iPSC-CMs. Here, we tackle these limitations by developing a computational pipeline to calibrate cell preparation-specific iPSC-CM electrophysiological parameters. We used the genetic algorithm (GA), a heuristic parameter calibration method, to tune ion channel parameters in a mathematical model of iPSC-CM physiology. To systematically optimize an experimental protocol that generates sufficient data for parameter calibration, we created in silico datasets by simulating various protocols applied to a population of models with known conductance variations, and then fitted parameters to those datasets. We found that calibrating to voltage and calcium transient data under 3 varied experimental conditions, including electrical pacing combined with ion channel blockade and changing buffer ion concentrations, improved model parameter estimates and model predictions of unseen channel block responses. This observation also held when the fitted data were normalized, suggesting that normalized fluorescence recordings, which are more accessible and higher throughput than patch clamp recordings, could sufficiently inform conductance parameters. Therefore, this computational pipeline can be applied to different iPSC-CM preparations to determine cell line-specific ion channel properties and understand the mechanisms behind variability in perturbation responses.

Non‐Monotonic Variation of the Low Lattice Thermal Conductivity with Temperature in Penta‐HgO2 Sheet

AbstractInspired by the experimental synthesis of bulk HgO2 with the potential of exfoliation to form a penta‐HgO2 sheet composed entirely of pentagonal motifs, a detailed theoretical study on the lattice thermal conductivity by using first‐principles calculations combined with the unified theory of thermal transport is performed. It is found that the penta‐HgO2 sheet is semiconducting with an indirect bandgap of 1.18 eV and possesses a low lattice thermal conductivity of 2.07 W m−1 K−1 (3.28 W m−1 K−1) along the x (y)‐direction at 300 K. More interestingly, the variation of its thermal conductivity with temperature is non‐monotonic, different from most cases. The phonon dispersion, phonon scattering, and phonon coherence is further systematically investigated to understand the underlying physics. This results suggest that the strong intrinsic anharmonicity resulting from its unique atomic configuration with the buckled structure and the heavy element of Hg leads to a high scattering rate, resulting in the ultralow particle‐like thermal transport of 0.20 W m−1 K−1 (0.01 W m−1 K−1) in the x (y)‐direction, while the narrow average frequency interval and strong phonon linewidth are responsible for the dominant coherent thermal transport and non‐monotonic variation of the low lattice thermal conductivity of the penta‐HgO2 sheet.

Effect of Solutionization and Deformation on Microstructural Evolution and Mechanical/Electrical Properties of Al–Mg–Si Alloy with Er + Sc Co‐Addition

Effect of solutionization (two‐step vs one‐step solution treatment) and deformation (one‐step solutionization + deformation vs no deformation) on microstructural evolution and mechanical/electrical properties of an aged Al–Mg–Si alloy with microalloying Er + Sc co‐addition is studied, respectively, in comparison with its counterpart with a single Sc addition at the same total addition content. Experimental results showed that the Er + Sc co‐added Al–Mg–Si alloy displayed a combination of aging hardness and electrical conductivity superior to the single Sc‐added alloy, under either two‐step or one‐step solution treatment. This highlights an effective microalloying way to improve the Al–Mg–Si alloy by partially using cheap Er rather than full addition of expensive Sc. While the introduction of deformation yielded to a higher aging hardness but a lower electrical conductivity in the Er + Sc co‐added alloy apparently than in the single Sc‐added alloy. When comparing among the Er + Sc co‐added alloys, it is found that, although the one‐step solutionization leads to the highest aging hardness (≈102 HV in peak aging) and the deformation introduction results in the highest electrical conductivity (up to 57.3% IACS), the two‐step solutionization brought about the best combination of aging hardness (≈87 HV) and electrical conductivity (up to 56.7% IACS). Microstructural evolution under different treatments is analyzed to rationalize the variation in aging hardness and electrical conductivity especially in the Er + Sc co‐added Al–Mg–Si alloy.

Experimental Investigation on Electrical Conductivity Variation of Carnosine and Zinc Chloride Aqueous Solutions under Microwave Irradiation.

The mechanism of biological effects of environmental electromagnetic radiation is still not completely clear. The chelation of biological small molecule peptides with metal ions plays a very important role in human metabolism. In this paper, a special experimental system was designed to measure the conductivity of carnosine and zinc chloride mixed aqueous solutions under different concentration ratios, microwave powers, and temperatures. The experimental results show that, first, different concentration ratios can alter the conductivity change rate of the mixed aqueous solution. The conductivity of the solution always increases under microwave irradiation at a concentration ratio of 1:1. However, the conductivity is reduced by -0.04% with a 1:5 concentration ratio and 6 W microwave power at 10 °C. Second, temperature can alter the conductivity change rate of the aqueous mixture. The higher the temperature, the smaller the conductivity change rate. Third, different microwave powers can alter the conductivity change rate of the mixed aqueous solution. In general, the conductivity change rate increases with an increase in microwave power. Experimentally observed reduction of the conductivity change rate in carnosine and zinc chloride aqueous solution under low microwave power and low temperature indicates that microwaves do affect the chelation of carnosine with zinc chloride. This work provides a new perspective for the mechanism of explanation of microwave biological effects.

The Role of α−β Quartz Transition in Fluid Storage in Crust From the Evidence of Electrical Conductivity

AbstractAqueous fluids are extensively present in the middle to lower crust, as revealed by seismic and magnetotelluric soundings. The α−β quartz phase transition significantly affects many physical properties and leads to substantial microcracks that can provide pathways for the migration of crustal fluids. A systematic investigation of macroscopic physical properties and microstructure of quartz is crucial to elucidate their correlation. In the present study, the effects of water content, trace elements, orientations, and phase transition on the electrical conductivity of quartz were thoroughly evaluated at 400−900°C and 1 GPa. Individual annealing experiments were simultaneously conducted on quartz single crystals at different peak temperatures and 1 GPa to investigate the evolution and spatial distribution of microcracks using X‐ray microtomography (CT) and backscattered electron imaging. We found that trace element content and orientations, rather than H2O, are the dominant factors controlling the conductivity of quartz. The distinct changes in conductivity of single crystals at around α−β phase transition temperature are attributed to the transformation of microcracks from isolated to interconnected networks, as confirmed by two‐dimensional (2‐D) and three‐dimensional (3‐D) microstructure images. Based on the variation in electrical conductivity and microstructure across the transition, it thus is proposed that the intragranular microcracks caused by quartz phase transition can serve as fluid or melt pathways within highly conductive zones present in the middle to lower crust, while α‐quartz acts as an impermeable cap.

Turbulent pipe flow and heat transfer of a binary mixture at supercritical pressure: Influences of cross-diffusion effects

Cross-diffusion effects, including Soret and Dufour effects, are enhanced around the pseudo-critical temperature (Tpc) of a binary mixture. Their influences on heat transfer at supercritical pressure have been scarcely studied. To bridge this gap, large-eddy simulations (LES) are conducted to investigate forced convective heat transfer of a CO2–ethane mixture at supercritical pressures in a circular pipe subject to a uniform heat flux. Both heating and cooling conditions, along with varying initial concentrations and thermodynamic pressures, are included in the simulations. The LES results reveal that the Soret effect causes concentration separation, resulting in a concentration boundary layer. The magnitudes of the thermodiffusion factor (kT) and the radial temperature gradient control the intensity of separation, which is more pronounced at near-critical pressure and high heat flux. Since kT is significant only around Tpc, downstream decay of the concentration separation is observed as the loci of T=Tpc migrate away from the wall so that the local radial temperature gradient diminishes. The primary factors affecting heat transfer are the variations in thermal conductivity and isobaric specific heat resulting from concentration separation. In contrast, the Dufour effect and the accompanying inter-diffusion play negligible roles. In deterioration scenarios, the bulk Nusselt number (Nub) shows a maximum relative drop of 8%, whereas in enhancement scenarios, Nub shows a maximum relative increase in 10%, with both deterioration and enhancement decaying downstream. Cross-diffusion effects have negligible impacts on density and streamwise velocity, but noticeably alter streamwise velocity fluctuation and turbulent kinetic energy.

Impacts of fossil fuel thermophoretic convective heat transfer on climate change with variable viscosity and thermal conductivity

The main aim of the current study is to analyze the impacts of fossil fuel thermophoretic convective heat transfer on climate change with variable viscosity and thermal conductivity. Furthermore, the purpose of the proposed problem is to develop a mathematical model based on three regions: source region (in terms of rectangular coordinates), plume region (in terms of cylindrical coordinates), and atmospheric region (in terms of spherical coordinates). The fossil fuels release thermophoretic particles, such as carbon dioxide, methane, black carbon, and many others, during burning process in the source region, and then release through the plume region. These particles are then distributed into the atmosphere, where the impact of thermophoretic particles on climate change is analyzed. The modeled nonlinear partial differential equations are transformed into a dimensionless form using suitable non-dimensional scaling variables. The proposed model is solved using finite difference approach in order to analyze the impacts of fossil fuel thermophoretic particles in the atmosphere in terms of climate change. In this regard, the effect of dimensionless parameters, viscosity variation parameter γ, Schimdt number Sc, thermal conductivity variation parameter ε, coefficient of thermophoretic process k, and thermophoresis parameter Nt on the velocity, temperature, and thermophoretic concentration fields are discussed. The main novelty of current work is that three models in three regions are coupled via trans-boundaries in term of temperature differences. It is very interesting to note that the concentration of thermophoretic particles, along with temperature profile, is maximum at α=π rad and minimum at α=1.5 rad in the atmospheric region.

Thermal properties and microstructure of Al–Sn alloys

The Al–Sn alloys feature excellent wear and corrosion resistance, along with good mechanical properties. They are mostly used as bearing materials. However, the application of these alloys as phase change materials (PCMs) for thermal energy storage (TES) has recently been suggested. To assess the adequacy of these alloys in a given area, a detailed evaluation of their thermal properties is required. In the present study, Al–Sn alloys with 11.7, 22.4, 32.8, 41.1, and 53.4 at.% Sn were produced by melting of pure metals. The melt was continuously stirred to avoid segregation, after which casting was carried out in a stainless steel mold. The obtained ingots had a homogeneous microstructure without the appearance of cracks and pores. The thermal diffusivities of the solid Al–Sn alloys in the temperature interval 25–150 °C were measured using the light flash method. The densities at room temperature were measured by the Archimedes method, and the specific heat capacities at different temperatures were calculated using the calculation of phase diagrams (CALPHAD) method. Then the thermal conductivities for studied Al–Sn alloys were obtained by the specific conversion equation. The phase transition temperatures and related heat effects were studied using differential scanning calorimetry (DSC). Variations of thermal conductivity with composition and temperature were determined as well as variation of latent heat of fusion with alloy composition. Moreover, the microstructures and phase compositions of the alloys were examined using scanning electron microscopy (SEM) combined with energy dispersive spectrometry (EDS). The present research results provide important information on the thermal properties and microstructure of the Al–Sn alloys for designing new PCM for thermal energy storage.

Quantitative analysis of soil quality around brick kilns using pollution indices and ANOVA in Jammu district of Jammu and Kashmir, India

The study investigated soil quality around brick kilns in the Jammu district of Jammu and Kashmir, analyzing 200 samples from 50 sites for selected parameters such as pH, electrical conductiv1ity, soil temperature, organic carbon content, organic matter, macronutrients, and heavy metals. The findings revealed that soil electrical conductivity ranged from 0.33 to 0.63 dS/m, with significant differences observed at varying distances from the kilns. Copper concentrations were highest at 5.32 mg/kg near the kilns, while iron and lead levels also varied significantly, indicating potential contamination. The mean soil temperature was recorded to be 27.69°C.The pH values ranged from 6.5 to 7.8, and the average pH of 8.22 indicated the slightly alkaline nature of the soil around the brick kilns. The organic carbon ranged from 0.34% to 1.02%.Soil temperature and electrical conductivity decreased with increasing distance from the kilns, with temperature showing positive correlations with organic carbon, organic matter, nitrogen, potassium, manganese, and iron and negative correlations with pH, phosphorus, zinc, copper, lead, and cadmium. A perfect positive correlation was noted among nitrogen, organic carbon, and organic matter. Heavy metals, except for zinc and manganese, showed positive correlations with each other. The average Zn, Cu, Mn, Fe, Pb and Cd concentration was recorded as 1.07, 1.03, 6.71, 10.30, 37.04 and 1.91 ppm, respectively. The contamination factor indicated moderate contamination with lead and cadmium, while the geo-accumulation index also suggested moderate contamination. The pollution load index reflected unpolluted soil and enrichment factor values for heavy metals ranked as Cd > Pb > Cu > Zn > Mn > Fe.ANOVA results revealed significant variations in electrical conductivity, copper, iron, and lead, underscoring the potential environmental impacts at different distances from the kilns. However, no significant differences were found between agricultural and non-agricultural sites in other physicochemical parameters. These variations highlight the considerable impact of brick kilns on soil health, emphasizing the need for enhanced environmental management and further research to mitigate these effects.

Study of the effect of green sand properties on thermal conductivity variation of sand mould

Thermal conductivity of moulding sands is very poorly known. In addition, the study of thermal conductivity variation of moulding sand as a function of its properties is a crucial step in the prediction of moulding defects. So, this study investigates the effects of moisture content, compactibility and permeability of green sand on its thermal conductivity variation, with a specific focus on the combination of bentonite, sand and water for thermal characterization of the transient hot-bridge (THB) method. Three levels of active clay content were selected: 8.1%, 8.98% and 10.03%. For each level of active clay, different mixes were made by varying the amount of added water. Compactibility, permeability and thermal conductivity tests were carried out for each combination of active clay and moisture contents. The experimental results show that thermal conductivity increases with water content and compactibility for different active clay contents, whereas thermal conductivity decreases with permeability, and this variation is highly significant especially for 8.07% of active clay content.

Investigating the Electrochemical Performance of MnFe2O4@xC Nanocomposites as Anode Materials for Sodium-Ion Batteries.

Transition metal oxides (TMOs) are important anode materials in sodium-ion batteries (SIBs) due to their high theoretical capacities, abundant resources, and cost-effectiveness. However, issues such as the low conductivity and large volume variation of TMO bulk materials during the cycling process result in poor electrochemical performance. Nanosizing and compositing with carbon materials are two effective strategies to overcome these issues. In this study, spherical MnFe2O4@xC nanocomposites composed of MnFe2O4 inner cores and tunable carbon shell thicknesses were successfully prepared and utilized as anode materials for SIBs. It was found that the property of the carbon shell plays a crucial role in tuning the electrochemical performance of MnFe2O4@xC nanocomposites and an appropriate carbon shell thickness (content) leads to the optimal battery performance. Thus, compared to MnFe2O4@1C and MnFe2O4@8C, MnFe2O4@4C nanocomposite exhibits optimal electrochemical performance by releasing a reversible specific capacity of around 308 mAh·g-1 at 0.1 A·g-1 with 93% capacity retention after 100 cycles, 250 mAh·g-1 at 1.0 A g-1 with 73% capacity retention after 300 cycles in a half cell, and around 111 mAh·g-1 at 1.0 C when coupled with a Na3V2(PO4)3 (NVP) cathode in a full SIB cell.

The Effective Vertical Anisotropy of Layered Aquifers.

Many sedimentary aquifers consist of small layers of coarser and finer material. When groundwater flow in these aquifers is modeled, the hydraulic conductivity may be simulated as homogeneous but anisotropic throughout the aquifer. In practice, the anisotropy factor, the ratio of the horizontal divided by the vertical hydraulic conductivity, is often set to 10. Here, numerical experiments are conducted to determine the effective anisotropy of an aquifer consisting of 400 horizontal layers of which the homogeneous and isotropic hydraulic conductivity varies over two orders of magnitude. Groundwater flow is simulated to a partially penetrating canal and a partially penetrating well. Numerical experiments are conducted for 1000 random realizations of the 400 layers, by varying the sequence of the layers, not their conductivity. It is demonstrated that the effective anisotropy of the homogeneous model is a model parameter that depends on the flow field. For example, the effective anisotropy for flow to a partially penetrating canal differs from the effective anisotropy for flow to a partially penetrating well in an aquifer consisting of the exact same 400 layers. The effective anisotropy also depends on the sequence of the layers. The effective anisotropy values of the 1000 realizations range from roughly 5 to 50 for the considered situations. A factor of 10 represents a median value (a reasonable value to start model calibration for the conductivity variations considered here). The median is similar to the equivalent anisotropy, defined as the arithmetic mean of the hydraulic conductivities divided by the harmonic mean.

Triphasic heterostructured Zn–Sn–S hollow nanoboxes encapsulated by N, S-codoped carbon as anodes for high-performance sodium-ion batteries

Metal sulfides have been expected huge practical potential for sodium-ion batteries (SIBs), which are mainly owing to their admirable merits of natural abundance, low price, and high theoretical capacity. However, the inferior electrical conductivity and enormous volume variation originating from sodiation/desodiaziton reactions usually result in unsatisfied rate and cycling properties. In this work, a coprecipitation with a following sulfurization method has been rationally designed to prepare the triphasic heterostructured hollow Zn–Sn–S nanoboxes encapsulated by N, S-codoped carbon (ZSS@NCS) as anodes for SIBs. The coexistence of triphasic ZSS heterostructures that consist of ZnS, SnS2, and Sn2S3 effectively facilitates the fast Na + diffusion. The N, S-codoped carbon (NSC) derived from the polydopamine is coated outside of the ZSS heterostructures, which provides infinite affinity between ZSS and NSC that efficiently accelerates the electron transport and maintains the structural stability via the confinement effect. The synergistic effects of the heterostructured ZSS and NSC endow the ZSS@NSC anode with favorable sodium storage properties including decent discharge capacity (683.8 mAh g−1 at 0.1 A g−1), satisfying rate (232.6 mAh g−1 at 10.0 A g−1) and cycling properties (402.2 mAh g−1 over 150 cycles).