Variation Of Conductivity Research Articles

Improving the proton conductivity of MOF materials by regulating the pore space

The construction of metal-organic frameworks (MOFs) with structural tunability, designability, and high stability is crucial in proton conduction studies. In this paper, MOF-801 and UiO-66 were used as precursors. A solvothermal synthesis method was employed to modulate the pore space of composite MOFs. The hydrogen-bonding network and structure within the pore cavities are adjusted by varying the molar ratios of fumaric acid and terephthalic acid to improve the proton transfer rate and proton conductivity of the materials. A total of 14 materials, MOF/MOG-801&UiO-66-X (X is the molar ratio of fumaric acid and terephthalic acid) were synthesised, the variation of proton conductivity with temperature of which was investigated at different tempertures and humidities. The results shows that MOG-801&UiO-66–1:1 has the best proton conductivity with 0.12 S cm−1 at 353 K and 100 % RH, which is 5 times higher than that of MOF-801&UiO-66–1:3 (2.54 × 10−2 S cm−1), 11 times higher than that of MOF-801 and 22 times higher than that of UiO-66. Based on the calculated activation energy and structural characterisation, the proton conduction mechanism of the composite was investigated. This work opens up novel research fields for the design of new proton-conducting MOF materials.

Dielectric, electrical and optical properties of a Ni-substituted double perovskite ceramic

In this report, iron based double perovskite oxide Ca2Fe0.85Ni0.15NbO6 was successfully synthesized adopting the solid state reaction method. The structure and microstructure along with the purity, frequency and temperature dependent dielectric properties, energy storage and band gap modulation properties of the ceramic is also discussed. The ceramic shows a small band gap with a high remnant polarization which aligns it toward ferroelectric photovoltaic device applications. The temperature stable dielectric constant of the material up to a high temperature range is useful for MLCC applications. The energy storage density of the material is calculated from room temperature hysteresis loop. The frequency variation of ac conductivity refers Jonscher’s law. The temperature variation of frequency exponent term n indicates CBH (correlated barrier hopping), model of the conduction mechanism in the sample. The presence of room temperature M-H loop with negligible magnetization and coercive value indicates the soft ferromagnetic behavior of the ceramic.

Molecular conjugation and crystallinity impact of designed novel chalcone based polyesters and polyesteramides on their thermal-electrical conductivity

Two polyesteramides were synthesized via polycondensation reactions involving 4′-amino-4-hydroxychalcone 3a with succinic acid (4) and terephthalic acid (5), utilizing phosphoryl chloride as a chlorinating agent. A parallel reaction with (E)-1,3-bis(4-hydroxyphenyl) prop-2-en-1-one chalcone (3b) yielded two polyesters under identical conditions. The resultant polymers were obtained with excellent yields ranging from 93 % to 98 %. Characterization of the obtained polyesteramides and polyesters was conducted using various analytical techniques. Fourier-transform infrared spectroscopy (FTIR) evidenced the disappearance of the OH group's broad band from the initial monomers, providing strong support for polymer formation. Thermal gravimetric analysis (TGA) results indicated high thermal stability of all prepared polymers at elevated temperatures, designating them as potential thermoplastic polymers. Powder X-ray diffraction (XRD) outcomes demonstrated a significant amorphous nature for all synthesized polymers, with degrees ranging from 67.56 % to 74.75 %. Analysis of current/voltage (I-V) curves revealed a wide spectrum of electrical conductivity for polymers containing chalcone in their repeating units, ranging from negligibly low values of 0.08 µS/cm to 0.31 μS/cm, indicating their potential as semiconducting materials. Variations in conductivity across synthesized polymers were attributed to the degree of conjugation in polymeric chains and their crystallinity levels.

Using graph neural networks for wall modeling in compressible anisothermal flows

Abstract Compressible anisothermal flows, which are commonly found in industrial settings such as combustion chambers and heat exchangers, are characterized by significant variations in density, viscosity, and heat conductivity with temperature. These variations lead to a strong interaction between the temperature and velocity fields that impacts the near-wall profiles of both quantities. Wall-modeled large-eddy simulations (LESs) rely on a wall model to provide a boundary condition, for example, the shear stress and the heat flux that accurately represents this interaction despite the use of coarse cells near the wall, and thereby achieve a good balance between computational cost and accuracy. In this article, the use of graph neural networks for wall modeling in LES is assessed for compressible anisothermal flow. Graph neural networks are a type of machine learning model that can learn from data and operate directly on complex unstructured meshes. Previous work has shown the effectiveness of graph neural network wall modeling for isothermal incompressible flows. This article develops the graph neural network architecture and training to extend their applicability to compressible anisothermal flows. The model is trained and tested a priori using a database of both incompressible isothermal and compressible anisothermal flows. The model is finally tested a posteriori for the wall-modeled LES of a channel flow and a turbine blade, both of which were not seen during training.

Influence of misfit dislocations on ionic conductivity at oxide interfaces

Mismatched complex oxide thin films and heterostructures have gained significant traction for use as electrolytes in intermediate temperature solid oxide fuel cells, wherein interfaces exhibit variation in ionic conductivity as...

EFFECT OF WASTEWATER TREATMENT ON SURFACE AND UNDERGROUND WATER SOURCES: A CASE STUDY OF ZHIDU SETTLEMENT IDU, ABUJA

Wastewaters form a major segment of the water cycle in an increasingly urbanised world. These wastewaters have the potential to pollute both surface and underground water bodies thereby posing risks to the health of humans and other animals, in additional to other environmental challenges. This thesis examined the effect of Wupa Wastewater Treatment Plant on surface and underground water sources around Zhidu Settlement in Abuja. The study encompasses a thorough analysis of physical, chemical, and microbiological parameters in both water sources. Surface water exhibited variations in pH, conductivity, and other parameters, while underground water displayed more stable characteristics. The findings from the surface water analysis revealed near-neutral to slightly alkaline pH levels of 7.32 - 7.92 and variable conductivity of 0.16 to 0.233uS/cm. Chemical analysis focused on parameters such as dissolved oxygen and BOD whose values indicated organic pollution load, with range from 5.5 to 11.5 mg/l. Similar analyses were conducted for underground water, showcasing differences in pH ranging from 5.66 to 7.43 indicating slightly acidic to neutral water. BOD for underground values ranged from 4.8 to 8.4mg/l which indicate high level of organic matter. Microbiological parameters were also tested for underground samples including total bacteria count, fecal coliform, and specific pathogens. The study concludes with recommendations for enhanced turbidity management and the need to identify the cause of high BOD in both surface and underground water bodies. These recommendations aim to enhance water management practices and ensure the sustainability of water resources in the Zhidu settlement.

Molecular dynamics simulation of thermal transport properties of phonons at interface of Au-TiO<sub>2</sub> photoelectrode

Thermoplasmonics originating from the relaxation process of plasmon resonances in nanostructures can be utilized as an efficient and highly localized heat source in solar-hydrogen conversion, but there have been few researches on the interfacial heat transport properties of photoelectrode with the thermoplasmonics effect in a photoelectrochemical water splitting system. In this work, the effects of temperature, interfacial coupling strength and the addition of graphene layers on the interfacial thermal conductance of Au-TiO<sub>2</sub> electrodes are investigated by the non-equilibrium molecular dynamics simulation, and the variation of interfacial thermal conductance is analyzed by the phonon density of states. The results show that the interfacial thermal conductivity is increased by 78.55% when the temperature increases from 300 to 800 K. This is related to the fact that more low-frequency phonons participate in the interface heat transport, allowing more heat to be transferred to TiO<sub>2</sub> to promote the interface reaction. As the coupling strength of the Au-TiO<sub>2</sub> interface increases, the interfacial thermal conductivity of the electrode increases and then tends to stabilize. The interfacial thermal conductivity can be optimized by increasing the degree of overlap of the phonon state densities of Au and TiO<sub>2</sub>. The addition of a single layer of graphene can increase the interfacial thermal conductivity to 98.072 MW⋅m<sup>–2</sup>⋅K<sup>–1</sup>, but the addition of 2 and 3 layers of graphene can hinder interfacial heat transfer in Au and TiO<sub>2</sub> due to the interaction between the layers of graphene. When adding graphene layer, medium-frequency phonons and high-frequency phonons are stimulated to participate in the interfacial heat transfer, but with the increase of the graphene layers, the number of low-frequency phonons in a range of 0—30 THz decreases, and these low-frequency phonons make the greatest contribution to the interfacial thermal conductivity. The obtained results are useful in regulating the thermal transport properties of the photoelectrode interface, which can provide new insights into and theoretical basis for the design and construction of composite photoelectrodes.

A comparative study of critical failure surface in unsaturated soil slopes

Landslides are rapid downslope movements triggered by factors such as rainfall, earthquakes, or human activity, where factors like relative permeability, air flow, and variations in hydraulic conductivity affect the mechanisms behind rainfall infiltration and landslide occurrence. This study investigates these phenomena using the finite difference method (FDM) and finite element method (FEM) in FLAC and PLAXIS software. These methods account for the coexistence of water and air, including the Two-phase flow option, in evaluating unsaturated slope stability and identifying critical failure surfaces. The efficiency of these approaches is gauged by varying soil parameters and exploring the impact of different values of suction and hydraulic conductivity, as well as soil-water characteristics, on the safety factor. It was observed that there are disparities between both software packages at high suction values, which impact the safety factor. FLAC is noted for its conservative approach and superiority in representing unfavourable conditions.

Mechanical regulation to interfacial thermal transport in GaN/diamond heterostructures for thermal switch.

Gallium nitride offers an ideal material platform for next-generation high-power electronics devices, which enable a spectrum of applications. The thermal management of the ever-growing power density has become a major bottleneck in the performance, reliability, and lifetime of the devices. GaN/diamond heterostructures are usually adopted to facilitate heat dissipation, given the extraordinary thermal conduction properties of diamonds. However, thermal transport is limited by the interfacial conductance at the material interface between GaN and diamond, which is associated with significant mechanical stress at the atomic level. In this work, we investigate the effect of mechanical strain perpendicular to the GaN/diamond interface on the interfacial thermal conductance of heterostructures using full-atom non-equilibrium molecular dynamics simulations. We found that the heterostructure exhibits severe mechanical stress at the interface in the absence of loading, which is due to lattice mismatch. Upon tensile/compressive loading, the interfacial stress is more pronounced, and the strain is not identical across the interface owing to the contrasting elastic moduli of GaN and diamond. In addition, the interfacial thermal conductance can be notably enhanced and suppressed by tensile and compressive strains, respectively, leading to a 400% variation in thermal conductance. More detailed analyses reveal that the change in interfacial thermal conductance is related to the surface roughness and interfacial bonding strength, as described by a generalized relationship. Moreover, phonon analyses suggest that the unequal mechanical deformation under compressive strain in GaN and diamond induces different frequency shifts in the phonon spectra, leading to an enhancement in phonon overlapping energy, which promotes phonon transport at the interface and elevates the thermal conductance and vice versa for tensile strain. The effect of strain on interface thermal conductance was investigated at various temperatures. Based on the mechanical tunability of thermal conductance, we propose a conceptual design for a mechanical thermal switch that regulates thermal conductance with excellent sensitivity and high responsiveness. This study offers a fundamental understanding of how mechanical strain can adjust interface thermal conductance in GaN/diamond heterostructures with respect to mechanical stress, deformation, and phonon properties. These results and findings lay the theoretical foundation for designing thermal management devices in a strain environment and shed light on developing intelligent thermal devices by leveraging the interplay between mechanics and thermal transport.

Three-Dimensional Unstructured Finite Element Modeling of Magnetotelluric Problems Allowing for Continuous Variation of Conductivity in Each Block

Three-Dimensional Unstructured Finite Element Modeling of Magnetotelluric Problems Allowing for Continuous Variation of Conductivity in Each Block

Precision agriculture predictive modeling and sensor analysis for enhanced crop monitoring

This research investigated the application of the Internet of Things (IoT) in precision agriculture and crop monitoring through a two-fold research methodology. A simulated dataset was generated, mirroring real-world IoT sensor readings of soil temperature, salinity level, soil moisture, and conductivity. Employing the Pandas and Matplotlib libraries in Python facilitated exploratory data analysis, including time series analysis through line plots to illustrate temporal variations in these critical parameters. The study then explored the evaluation of predictive models for soil moisture levels, extending the dataset to include simulated predicted values. Performance metrics such as Mean Squared Error (MSE) and R-squared (R2) were computed using the sci-kit-learn library, providing a comprehensive evaluative framework. Visual representations of actual versus predicted soil moisture levels, accompanied by the analysis of residuals, offered nuanced insights into the model’s efficacy. The results highlight dynamic variations in soil temperature, salinity, soil moisture, and conductivity, emphasizing the importance of continuous monitoring in precision agriculture. Fluctuations observed in these parameters are attributed to climatic conditions, agricultural practices, and soil properties. The study contributes valuable insights for stakeholders, emphasizing the significance of IoT technologies in providing actionable data for sustainable and adaptive farming practices. The visual representations offer practical tools for decision-making, while the performance evaluation of predictive models enhances the reliability of data-driven approaches in agriculture. The findings presented herein contribute to the ongoing discourse on precision agriculture, emphasizing the role of accurate predictions for efficient resource utilization and improved crop yield.

Power generation prediction of a geothermal-thermoelectric hybrid system using intelligent models

This study focuses on the most effective use of energy resources, which is the main goal of sustainable energy systems. The first contribution of the research is to ensure that the waste and geothermal fluids generated during the geothermal energy production process are evaluated in the most efficient way and reused for energy production. For this purpose, a geothermal-thermoelectric hybrid experimental system has been designed to produce electrical energy through heat exchange and how waste thermal energy could be reused in this process has been examined. Experimental tests have been performed under electrical load and in the presence of real-time uncertainties such as temperature changes, thermal conductivity, electrical noise, heat losses and random variations in thermal interaction. The second main contribution of the study is to predict and evaluate different power generation performances in the Thermoelectric Generator (TEG) system, which is affected by the mentioned experimental limitations based on artificial intelligence models. In addition, the optimal prediction performance of artificial intelligence models using hyper-parameter optimization has also been compared. Experimental results have shown that intelligent models are a successful tool for energy prediction in geothermal power generation. As a result of the hyper-parameter optimization, each model is improved by ∼5 % to ∼30 % in various error metrics. However, it was observed that the decision tree-based learning techniques resulted in ∼46 % to ∼96 % less inaccurate predictions on various error metrics compared to other methods. Power production predictions obtained using intelligent models have been obtained with 98.7 % accuracy with LightGBM learning algorithms. Most of the models used to classify different hot and cold water levels exhibit very high classification performance. The results show the successful integration of artificial intelligence models in effectively utilizing waste thermal energy and adopting a sustainable approach to energy production.

Spatio-temporal variation in soil thermal conductivity during the freeze-thaw period in the permafrost of the Qinghai–Tibet Plateau in 1980–2020

The Qinghai–Tibet Plateau (QTP) has the largest amount of permafrost in the low and middle latitudes, making it highly susceptible to the effects of global warming. In particular, the degradation of permafrost can be intensified by anomalous amplified warming. To accurately model the hydrothermal dynamics of permafrost and its future trends, the accumulation of high-precision, long-term data for the soil thermal conductivity (STC) in the active layer is crucial. However, no previous research has systematically investigated the spatio-temporal variation in the STC on the QTP over an extended period. Therefore, this study aims to fill this gap using the XGBoost model to analyze the STC in the permafrost on the QTP from 1980 to 2020. The findings of this study provide some preliminary insights. First, areas with high variation in the STC between the freeze–thaw periods over the 40 years gradually migrated from the western region to the central region. Second, since 2015, STC in more than 90 % of the permafrost region in the thawing period has shown positive growth. While, during the freezing period, the STC also exhibited an increase over most regions of the QTP, though the western region and parts of the northeastern region exhibited a decrease. Third, the spatial center of gravity for the STC during the freezing and thawing periods from 1980 to 2020 shifted. The mean STC was larger in the eastern and northeastern regions during the freezing period and larger in the western region during the thawing period. Fourth, both alpine swamp meadow and alpine meadow exhibited a gradual increase in the STC during the freeze–thaw period from 1980 to 2020. The conclusions and data products from this study are expected to support spatiotemporal modeling of the permafrost on the QTP and assist in the prognosis for its future.

Peapod-Like Structured B/N Co-Doped Carbon Nanotube Array Encapsulating MxPy (M = Fe, Co, and Ni) Nanoparticles for High-Rate Potassium Storage.

Potassium-ion batteries (PIBs) have become the desirable alternatives for lithium-ion batteries (LIBs) originating from abundant reserves and appropriate redox potential, while the considerable radius size of K+ leading to poor reaction kinetics and huge volume expansion limits the practical application of PIBs. Hybridization of transition-metal phosphides and carbon substrates can effectively optimize the obstacles of poor conductivity, sluggish kinetics, and huge volume variation. Thus, the peapod-like structural MxPy@BNCNTs (M = Fe, Co, and Ni) composites as anode materials for PIBs were synthesized through a facile strategy. Notably, the unique architecture of B/N codoped carbon nanotube array as fast ion/electron transfer pathways effectively improves the electronic conductivity of composites. The MxPy nanoparticles (NPs) are encapsulated in BNCNTs with an amorphous carbon layer (5-10 nm), which discernibly alleviate the volume changes during potassiation/depotassiation. In conclusion, the composites show a commendable cycling performance, possessing reversible capacities of 111, 152, and 122 mA h g-1 after 1000 cycles at 1.0 A g-1 with a negligible capacity loss for FeP@BNCNT, CoP/Co2P@BNCNT, and Ni2P@BNCNT electrodes, respectively. Especially, after 1000 cycles at 2.0 A g-1, the CoP/Co2P@BNCNT electrode still possesses a capacity of 87.9 mA h g-1, demonstrating excellent rate performance and long-term life. This work may offer an innovative and viable route to construct a stable architecture for solving the issue of poor stability of TMP-based anodes at a high current density.

Using Magnetic Field Analysis for Localizing Defects of Fuel Cell & Electrolyzer Components

Production capacities of electrolysis and fuel cell technologies are expected to increase significantly in the upcoming years, reducing costs of stack components through numbering-up. To ensure reliability and durability and reduce costs along the manufacturing process, developing suitable in-line non-destructive diagnostic methods for quality control becomes essential for their industrialization.In both fuel cell and electrolyzer stacks, the main components' electrical resistance and current density distribution greatly influence stack efficiency and longevity. Here, the flowing electric current generates a magnetic field depending on its strength and direction. Potential defects caused by the manufacturing process, including cracks, defective welding spots, coating adhesion issues, delamination, and contamination, influence the components' electric conductivity and current density distribution and hence the magnetic field distribution.This work investigates non-destructive diagnostic methods using magnetic field analysis (MFA) to identify changes in the magnetic field distribution and, thus, local electrical resistance and current density distributions of fuel cell and electrolyzer components. The methods used are magnetic field imaging (MFI) and Eddy Current Testing (ECT). MFI analyzes the magnetic field distribution in all three spatial directions to trace electrical currents and identify electrical defects, creating a magnetic field signature that can potentially distinguish between functional and non-functional components along the manufacturing process. ECT measures variations in local conductivity by inducing eddy currents in the sample through an alternating magnetic field generated by a sender coil. These eddy currents create a detectable magnetic field in a receiver and are affected by defects and changes in electrical conductivity. Therefore, the method can characterize sheet resistance and material homogeneity through electrical anisotropy in electrically conductive components.In initial experiments, ECT measurements on coated and uncoated hollow embossed stainless steel bipolar half plates showed systematic differences depending on quality characteristics resulting in unique patterns and inhomogeneities which require further investigation. Furthermore, experiments with both methods on other stack components will be presented and discussed. In addition, their in-line capability will be assessed and evaluated. However, these first promising results imply that MFA is potentially useful as an in-line, non-destructive diagnostic method for quality control.

A Metal-Organic Framework-Derived Strategy for Constructing Synergistic N-Doped Carbon-Encapsulated NiCoP@N-C-Based Anodes toward High-Efficient Lithium Storage.

Transition metal phosphides (TMPs) have been regarded as the prospective anodes for lithium-ion batteries (LIBs). However, their poor intrinsic conductivity and inevitable large volume variation result in sluggish redox kinetics and the collapse of electrode structure during cycling, which substantially hinders their practical use. Herein, an effective composite electrodes design strategy of "assembly and phosphorization" is proposed to construct synergistic N-doped carbon-encapsulated NiCoP@N-C-based composites, employing a metal-organic frameworks (MOFs) as sacrificial hosts. Serving as the anodes for LIBs, one representative P-NCP-NC-600 electrode exhibits high reversible capacity (858.5mAhg-1, 120 cycles at 0.1Ag-1) and superior long-cycle stability (608.7mAhg-1, 500 cycles at 1Ag-1). The impressive performances are credited to the synergistic effect between its unique composite structure, electronic properties and ideal composition, which achieve plentiful lithium storage sites and reinforce the structural architecture. By accompanying experimental investigations with theoretical calculations, a deep understanding in the lithium storage mechanism is achieved. Furthermore, it is revealed that a more ideal synergistic effect between NiCoP components and N-doped carbon frameworks is fundamentally responsible for the realization of superb lithium storage properties. This strategy proposes certain instructive significance toward designable high-performance TMP-based anodes for high-energy density LIBs.

Electrical conductivity of geopolymer-graphite composites: Percolation, mesostructure and analytical modeling

The incorporation of carbon fillers into geopolymers enhances conductivity, forming intricate networks for integrated energy conversion in construction materials. This study employs a novel computational modeling approach to investigate the electrical conductivity of geopolymer-graphite composites. Utilizing a particle-based Monte Carlo method, percolation thresholds in virtual composite mesostructures are simulated, and Bruggeman's effective medium model is applied to predict conductivity. The research explores how mesostructural characteristics, including pore and particle size distribution, influence percolation and conductivity in multi-phase mixtures. The findings indicate that graphite polydispersity increases the percolation threshold from 10 vol% to 24.4 vol%, while the introduction of two additional (segregated) phases, metakaolin and pores, decreases the percolation threshold to 11.9 vol%. The concept of effective volume fraction is introduced to assess the influence of metakaolin and porosity on the percolation threshold. This enabled to establish a linear relationship between the electrical conductivity and effective volume fraction of graphite with a slope of 2 S/m/vol%. In a three-phase mixture, the percolation threshold is 12.18 vol%, with a 2.35% increase due to capillary water drying, enhancing graphite particle aggregation in the dried pores. Finally, Bruggeman's analytical effective medium model is used to simulate conductivity variation with increasing graphite content, aligning well with conductivity measurements. These findings provide valuable insights into percolation behavior and offer a relatively simple quantitative modeling approach to understand the factors influencing the effective conductivity of geopolymer-graphite composites.

Energy storage and hydrophobicity characteristics of cement-based materials containing paraffin-pumice at low air pressure

Pumice is an environmental friendliness and low-cost carrier material for development of paraffin-pumice phase change energy storage composites (PP) at low air pressure in this work. PP was incorporated into cement to obtain CBM-PP. Microstructural, chemical, and thermal characteristics of PP were investigated by scanning electron microscopy (SEM), Brunauer-Emmett-Teller (BET), Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC) tests. Moreover, thermal energy storage and hydrophobicity characteristics of CBM-PP were performed by thermal conductivity, drip, and water absorption experiments. Results show that paraffin is effectively impregnated into pumice pores. There exists a physical interaction between paraffin and pumice. And PP possesses excellent compatibility and thermal stability, while its phase change temperatures and thermal energy storage capacities are in the ranges of 51.84–54.69 °C and 77.22–110.60 J/g, respectively. Furthermore, CBM-PP possesses excellent thermal energy storage and hydrophobicity at low air pressure in plateau areas, thus exhibiting a good application prospect in building thermal management and impermeability. Moreover, the thermal conductivity variation of CBM-PP is studied by the thermal conductivity model, which can predict the thermal conductivity with an error of no >9 %.

Integrating water quality monitoring and ecological assessment for wetland classification and risk evaluation in the Kirala Kele wetland, Sri Lanka

Abstract We conducted the present study to assess the spatial and temporal variations of selected physicochemical water quality parameters, to quantify the level of ecological risks of disturbances occurring, and to understand the relationship between the selected disturbances observed and physicochemical parameters of surface water of sampling sites of the Kirala Kele wetland. We selected sampling sites to reflect different land uses in the Kirala Kele wetland using purposive sampling and collected triplicated surface water samples to assess quality of water. The sampling sites of wetland characterization and risk assessment were followed using the protocol by Escom. We found that there was a significant temporal variation of pH, electrical conductivity, water temperature, and dissolved oxygen content among sampling sites (p > 0.05, One-Way ANOVA), however, spatial variation of electrical conductivity and dissolved oxygen was significantly higher in site 9, site 10, and site 11, respectively (p > 0.05, One-Way ANOVA). Site 2, site 5, site 10, and site 11 showed a category B level in wetland characterization and risk assessment highlighting the few levels of modifications and largely natural status of sites. The study demonstrates the applicability of this model for rapid assessment of wetland characteristics and risks to sustainably conserve and manage wetlands.

Estimation of the Amount of CO2 Chemically Absorbed in Real Time by Measuring the Electrical Conductivity Variation of Monoethanol-Amine Aqueous Solutions

Estimation of the Amount of CO₂ Chemically Absorbed in Real Time by Measuring the Electrical Conductivity Variation of Monoethanol-Amine Aqueous Solutions