Experimental Curves Research Articles

Relationship between the Intrinsic Properties of Sands and the Parameters of Mathematical Particle Size Distribution Models for Predicting Geotechnical Quantities

This work characterizes the relationships between the intrinsic properties of sands and the parameters of four mathematical models that best simulate the experimental curves and geotechnical properties of sands used in construction. Origin.Pro.2019" software was used to smooth the grading curves, define the parameters of the mathematical models and link them to the geotechnical data. To achieve this objective, the correlations between the intrinsic properties of the sands are developed using mathematical models with the highest coefficient of determination (R2) and the lowest statistical coefficient (χ²). The correlations used are those with a coefficient of determination greater than or equal to 0.9. The results obtained show that the models used provide a good description of the experimental curves. The model parameters are correlated with the granulometric fractions and the geotechnical parameters. The evolution of the points expressing the parameters of the Gaussian and exponential models (A1, Xc, A, W, Yo) and the parameter (t1) as a function of seven randomly chosen geotechnical quantities, are polylinear and linear fits, respectively. This study is important for predicting a geotechnical quantity from a modelled grading curve, by solving the mathematical expressions of the models used.

Influence of the thickness of p-GaN ohmic contact layer on the performance of AlGaN-based deep-ultraviolet light-emitting diodes

276 nm AlGaN-based deep-ultraviolet light-emitting diodes (DUV-LEDs) with varying p-GaN thicknesses were investigated. The simulation results demonstrate that DUV-LEDs with an ultra-thin 4 nm p-GaN layer exhibit a 96.9% increase in full-angle total radiant intensity compared to those with a 350 nm p-GaN layer. Meanwhile, the total radiant intensity of the DUV-LEDs with 4 nm p-GaN were all elevated during the degree of polarization (DOP) transition from 1 to −1. The experimental results demonstrate that the maximum external quantum efficiency (EQE) and wall-plug efficiency (WPE) values of 5.3% and 4.0%, respectively, at 4 A/cm2 for the DUV-LEDs with a 4 nm p-GaN layer. These figures represent remarkable improvements of 60.6% and 42.8% when compared to DUV-LEDs featuring a 350 nm p-GaN layer. Nevertheless, it's worth noting that the adoption of an ultra-thin p-GaN ohmic contact layer introduced some surface irregularities, potentially affecting the ohmic contact of the p-type layer and resulting in a slight voltage increase, thus tempering the WPE improvement. The light extraction efficiency (LEE) values of 10.0%, 7.4%, 6.7% and 6.3% were extracted for the four samples after fitting the experimental EQE curves by the ABC model.

The effect of carbon nanoparticles on cellulosic handsheets

Abstract This article describes the study of the effects of carbon nanoparticles on fibrous cellulosic webs. The handsheets were coated with coating colours containing graphene oxide (GO), carbon nanotube (CNT) and super conductive black carbon (SCBC) nanoparticles at different concentrations. The Raman method was used to assess the quality of the carbon materials and the changes in the band gap of the nanoparticles deposited on the handsheets. The influence of the type and content of the nanoparticles on the structural, mechanical, chemical and electrical properties of the composites was investigated and discussed. It was found that the range of failure modes and fracture surfaces seen in the SEM image is due to the influence of the different carbon materials as the main source of coating colour. The experimental percolation threshold curve and the total surface electrical resistivity of the samples were investigated. The SCBC coated samples exhibited low percolation threshold and comparatively high electrical conductivity at a certain maximum concentration. Each composite exhibited a pronounced stress distribution along the surface layer and the nanoparticle distribution was uniform. The surface resistance of the coated handsheets declined by about 14.103 times with the addition of only 1.0 wt% SCBC compared to the control sample. The composites coated with GO and SCBC nanoparticles at a concentration of 1.0 wt% had the highest tensile strength values.

Gamma-irradiated Aspergillus conidia show a growth curve with a reproductive death phase.

In this study, we evaluated the effects of gamma irradiation on the germination of Aspergillus conidia and mycelial growth using microscopy and predictive microbiological modeling methods. A dose of 0.4kGy reduced the germination rate by 20% compared to the untreated control, indicating interphase death due to the high radiation dose. The number of colonies formed (5.5%) was lower than the germination rate (69%), suggesting that most colonies died after germination. Microscopic observations revealed that mycelial elongation ceased completely in the middle of the growth phase, indicating reproductive death. The growth curves of irradiated conidia exhibited a delayed change in the growth pattern, and a decrease in slope during the early stages of germination and growth at low densities. A modified logistic model, which is a general purpose growth model that allows for the evaluation of subpopulations, was used to fit the experimental growth curves. Dose-dependent waveform changes may reflect the dynamics of the subpopulations during germination and growth. These methods revealed the occurrence of two cell death populations resulting from gamma irradiation of fungal conidia and contribute to the understanding of irradiation-induced cell death in fungi.

New Creep Crack Growth Prediction Model for the Life Assessment of Stainless-Steel Material Using Computational Modeling

The limitations of the established and existing creep failure models have inspired the development of a new creep prediction model. Models like Norton–Bailey and Omega are unable to model the tertiary creep curve for engineering materials. Kachanov–Rabotnov, Theta Projection, and Sine hyperbolic models rely on specific material properties for accurate damage predictions. In order to overcome these weaknesses, a new creep model combining the Norton–Bailey and Kachanov–Rabotnov models has been further devised for the creep life prediction of metallic materials. The model combination helps in covering the limitations of one model over another and to benefit from each other’s strengths. A technique of user subroutine scripting was adapted to implement the new creep model in finite element (FE) software of ABAQUS, manufactured by Dassault Systemes, version 2020. The new model was tested on an FE dog bone stainless steel 304 specimen; the analysis showed excellent agreement with the experimental creep deformation data at 600 °C to 700 °C. The creep strain rate curves obtained by the method of user subroutine scripting were found to be 90.69% accurate to the 1000 h experimental creep strain rate curve. Similarly, while comparing with the 336 h experimental creep test, the new model accuracy was found to be 92.66% for the creep strain rate curve. The new model’s precision was 91.56% when compared with the Omega and Norton–Bailey models for creep strain rate for the same conditions. The quantitative accuracy of the new creep model is better as compared to the existing creep models and can be an improved source of alternatives to existing creep models for the deformation predictions.

Reliable and precise determination of energy conversion in fuel cells using an integrable energy model

Fuel cells serve as devices that directly convert chemical energy into electricity via electrochemical reactions. Assessing and characterizing fuel cell performance typically involves the creation of polarization curves. Nevertheless, currently employed polarization curve models have shown limited effectiveness when it comes to evaluating the conversion of chemical energy into electrical energy. To address this knowledge gap, this study has developed a concise and integrable energy model of fuel cells. This model aims to predict output voltage with a high degree of reliability and precision, relying on current density. The validity of this model was through experimentation, involving a dataset of twenty results. Fitting outcomes from these experiments demonstrated the capacity of the proposed model to faithfully replicate experimental curves. Furthermore, this model permits the dependable and precise calculation of fuel cell energy density, contingent upon optimal model parameters. In addition, this study has established quantitative correlations between the inherent characteristics of fuel cells and their energy density. Lastly, the proposed model facilitates the interpolation and extrapolation of polarization curves that may not be attainable through direct experimentation. This capability is achieved through the utilization of a secondary model that quantitatively associates the intrinsic attributes of fuel cells with optimal model coefficients.

Experimental study and simulation of workability and compressive performance of SCC with high-volume mineral admixtures

Using industrial waste such as fly ash and silica fume to substitute most of the cement to prepare self-compacting concrete can not only greatly reduce the cement consumption, but also improve the resource utilization of solid waste powder, which is conducive to cost reduction and environmental protection. In this paper, the fly ash (FA) and silica fume (SF) replacement ratio of 40%, 50% and 60% was adopted to produce self-compacting concrete with high-volume mineral admixtures (HVMA-SCC). The slump flow test, L-box test and compression test were conducted, and the effect of different high replacement ratio on the workability, compressive performance and stress–strain relationship of HVMA-SCC were analyzed. The experimental results showed that when the total content of mineral admixtures was the same, an increase in SF content was beneficial for the development of early compressive strength. Under the same SF dosage, the larger the FA dosage, the faster the strength development rate, which was conducive to the sustained growth of HVMA-SCC strength in the later stage. Moreover, a stress–strain calculation model of HVMA-SCC was proposed based on damage theory. Besides, the workability and compression tests were simulated with the discrete element method (DEM). The results showed that the simulated stress–strain curves were in good agreement with experimental curves, and the mesoscopic failure characteristics of HVMA-SCC were analyzed from the development of cracks, stress conditions, and failure modes.

Seismic behavior and mechanism of rectangular steel-concrete composite column with three cavities

This paper proposes a rectangular steel-concrete composite column (RSCC) with three cavities, which has a good architectural function. The composite column consists of steel tubes at both ends, steel plates in the middle, and concrete in the cavity. Five RSCC with three cavities were tested to investigate the seismic performance of the column width and headed studs. It was found that the specimens all exhibited flexural damage in the form of flexural tearing of the steel tube/plate and crushing of the concrete in the steel tube cavity. As the column width increases, the load bearing ratio and the percentage of shear displacement in the middle cavity of the specimen increase. The use of headed studs in RSCC with three cavities delay the stiffness degradation of the specimens, and the ductility of the specimens with column width of 400 mm and 500 mm set with headed studs increased by 10.1% and 4.6%, respectively, compared to that of the specimens without headed studs. In addition, a finite element model of the RSCC with three cavities was established, and the finite element analysis results matched well with the experimental hysteresis curves and damage characteristics. A simplified lateral force calculation method was established, and the average error between the predicted values and the corresponding test results for each specimen was 6.1%, which verified the applicability of the calculation model and provided a reference for the engineering application of the RSCC which has good function and performance.

Real-time artificial intelligence enhanced defect engineering in CeO2 nanostructures

CeO2 nanostructures have been utilized for various biomedical, sensor, and catalysis applications owing to their unique defect structure, enabling them to have regenerative oxidative properties. Defect engineering in CeO2 nanostructures has major importance, enabling them to be utilized for specific applications. Despite various synthesis methods, it is challenging to have precise and reversible control over defect structures. Against this backdrop, in the current work, we have explored machine learning (ML) enhanced defect engineering of CeO2 nanofilms. In our earlier work [J. Vac. Sci. Technol. A 39, 060405 (2021)], we have developed an atomic layer deposition process for CeO2 using in situ ellipsometry measurements. In the current work, data collected through in situ spectroscopic ellipsometry and ex situ XPS have been correlated using two ML algorithms (gradient boost and random forest regressor) to exert better control over the chemical properties. Defect structures are one of the desired properties in CeO2 nanomaterials, characterized by the Ce3+/Ce4+ oxidation state ratio leading to its regenerative properties. We have shown that the defect structure of the CeO2 nanofilms can be predicted using in situ ellipsometry data in real time using a trained ML algorithm using two different methods. The first method involves an indirect approach of thickness prediction using an ML algorithm (k-nearest neighbors) followed by Ce3+/Ce4+ estimation using an experimental calibration curve. The second method with a more direct approach involves Ce3+/Ce4+ prediction using real-time ellipsometry data (amplitude ratio ψ and phase difference Δ) using gradient boost and random forest regressor.

A new theoretical model for the nonlinear flow in a rough rock fracture under shear

Shear deformations significantly affect the nonlinearity of fluid flow in rock fractures. To estimate this nonlinear flow using the Izbash equation, it is necessary to determine the Izbash coefficients. However, their parametric expressions are rarely mentioned, particularly when the shear-flow process is involved. This study proposed an improved Izbash equation-based model by incorporating the Darcy–Weisbach equation, along with a friction factor. The proposed model considers two asymptotic cases. At low Re regime, the fluid flow regime is dominated by the viscous effect, and the proposed model can be approximated to the cubic law. When Re reaches a certain threshold (Res), the fluid flow transitions into a fully turbulent flow regime where the inertia force dominates the fluid behaviours. Consequently, the proposed model is equivalent to the quadratic term of the Forchheimer equation. The proposed model is verified with the experimental results conducted on 5 rough fracture surfaces with normal stress from 0.5 to 3 MPa. Good correlations between the experimental and analytical curves illustrated that the proposed model of strong theoretical foundation yielded good prediction signified by the Normalised Objective Function (NOF) and an average error (σ¯ave) within 0.18 and 15 %, respectively.

Coarse-Grained Artificial Intelligence for Design of Brush Networks.

The ability to synthesize elastomeric materials with programmable mechanical properties is vital for advanced soft matter applications. Due to the inherent complexity of hierarchical structure-property correlations in brush-like polymer networks, the application of conventional theory-based, so-called Human Intelligence (HI) approaches becomes increasingly difficult. Herein we developed a design strategy based on synergistic combination of HI and AI tools which allows precise encoding of mechanical properties with three architectural parameters: degrees of polymerization (DP) of network strands, nx, side chains, nsc, backbone spacers between side chains, ng. Implementing a multilayer feedforward artificial neural network (ANN), we took advantage of model-predicted structure-property cross-correlations between coarse-grained system code including chemistry specific characteristics S = [l, v, b] defined by monomer projection length l and excluded volume v, Kuhn length b of bare backbone and side chains, and architecture A = [nsc, ng, nx] of polymer networks and their equilibrium mechanical properties P = [G, β] including the structural shear modulus G and firmness parameter β. The ANN was trained by minimizing the mean-square error with Bayesian regularization to avoid overfitting using a data set of experimental stress-deformation curves of networks with brush-like strands of poly(n-butyl acrylate), poly(isobutylene), and poly(dimethylsiloxane) having structural modulus G < 50 kPa and 0.01 ≤ β ≤ 0.3. The trained ANN predicts network mechanical properties with 95% confidence. The developed ANN was implemented for synthesis of model networks with identical mechanical properties but different chemistries of network strands.

Mechanical performance study of basalt-polyethylene fiber reinforced concrete under dynamic compressive loading

In order to enhance concrete's impact resistance, this study investigates the effects of single basalt fiber and basalt-polyethylene hybrid fiber on concrete through split Hopkinson pressure bar (SHPB) tests, emphasizing their significance in engineering applications. A comparative analysis of the failure modes, dynamic compressive strength, dynamic compressive toughness, and dynamic growth factor of specimens was conducted under different strain rates and polyethylene fiber volume fractions. The study considered concrete damage, strain rate effect, and the toughening effect of fibers, introducing the damage factor, and establishing a dynamic damage constitutive model for fiber-reinforced concrete. The results showed that basalt-polyethylene fiber reinforced concrete (BPFRC) exhibited better impact resistance and deformation capability. There were improvements observed in dynamic compressive strength, dynamic compressive toughness, and dynamic growth factor. The addition of basalt fiber (BF) and polyethylene fiber (PE) played a buffering role in inhibiting crack propagation and slowing down the damage process. The optimum PE volume fraction for dynamic compressive strength was 0.1% at a strain rate of 60 s−1, 0.15% at a strain rate of 95 s−1, and 0.05% at a strain rate of 120 s−1, all with a BF volume fraction of 0.25%. The optimum fiber volume fractions for dynamic compressive toughness and dynamic peak toughness were consistent, both at 0.25% BF and 0.1% PE. The established dynamic damage constitutive model for fiber-reinforced concrete can effectively describe the impact resistance performance of BPFRC, and the experimental curves fit well with the constitutive curves. Furthermore, it was observed that BF acted as macrofibers, bridging to restrain the post-hardening cracking of concrete, while PE acted as microfibers, inhibiting the propagation of plastic cracks, reducing internal defects in concrete, and enhancing the ductility and toughness of concrete. Consequently, BPFRC holds significant potential value in engineering applications.

Equilibrium and Kinetic Insights into the Comprehensive Investigation of CO2, CH4, and N2 Adsorption on Cation-Exchanged X and Y Faujasite Zeolites.

The adsorption equilibria and kinetic performance of CO2, CH4, and N2 on pelletized cation-exchanged faujasite zeolites (with alkali, alkaline earth, and transition metal ions) have been investigated by an innovative volumetric apparatus simultaneously. The standard instrumental analytical techniques, including X-ray diffraction, field emission scanning electron microscopy, energy dispersive X-ray spectroscopy (EDX), and atomic absorption spectroscopy (AAS), were utilized to characterize binder-free modified zeolites. EDX and AAS analyses revealed that the ion exchange was successfully achieved. The results indicate that the type of cation present in the zeolite framework and the Si/Al ratio can have a significant impact on the adsorption capacity and kinetic performance. The obtained isotherms were determined by three isotherm models, and the Langmuir-Freundlich (Sips) model was found to show the best agreement with the experimental isotherm data for all gases. The CO2 uptakes of KX, MgX, and CaX reached 4.13, 4.79, and 5.48 mmol/g, respectively. The effective binary and kinetic selectivities of CO2/CH4 and CO2/N2 were also calculated. Among all samples, KX showed the highest CO2/CH4 and CO2/N2 selectivities of 54.46 and 91.62, respectively. Pseudo-first-, pseudo-second-order, and Avrami kinetic models were fitted to the experimental kinetic data to analyze the adsorption kinetics. Finally, the macropore diffusion coefficient (Dp) and microporous diffusional time constant (Dc/rc2) were estimated by correlating the micropore-macropore kinetic model with the experimental fractional uptake curves. Among the ion-exchanged zeolite samples, the K+ form exhibits a suitable performance in terms of kinetic behavior and adsorption capacity.

Three-zone simulated moving bed for the separation of chlorogenic acid and caffeine fractions in the liquid extract of spent coffee grounds

Spent Coffee Grounds (SCG) is an agricultural residue obtained in a large quantity from local cafes in Thailand. In order to handle this waste effectively, the valorization of SCG is essential. SCG consists of beneficial phenolic compounds with antioxidative properties and caffeine, which can be recovered through extraction followed by separation and purification processes. In this work, water extraction of SCG was carried out. The volumetric composition of the liquid extract of SCG was then adjusted with an organic solvent, and the obtained mixture was used as the feed for subsequent separation. For the separation method of the SCG extract, a single chromatographic column was employed to separate a group of phenolic compounds (represented by chlorogenic acid) and a group of contaminants (represented by caffeine). The volumetric composition of the mobile phase was varied to determine the condition suitable for the separation of chlorogenic acid and caffeine in a C18 column. Adsorption parameters were determined and used to formulate the mathematical models describing the adsorption dynamics of those two bioactive compounds in the experimental breakthrough curves of standard solutions and the liquid extract of SCG. Furthermore, the three-zone simulated moving bed system (TZ-SMB) was designed to continuously separate fractions of chlorogenic acid and caffeine in the liquid extract of SCG. The adsorption parameters were employed in the optimization of TZ-SMB operating conditions using triangle theory, conducted via computer simulation. The experimental result of water extraction revealed that the yields of chlorogenic acid and caffeine were 0.292 and 0.583 mg/g dried SCG, respectively, using solid-to-liquid ratio of 1 g: 30 mL and temperature of 75 °C. The separation result in a single chromatographic column showed that the mobile phase consisting of acetonitrile, water, and formic acid (10: 90: 1.5 vol%) provided the linear adsorption isotherms for both chlorogenic acid and caffeine, and the chromatographic peaks of all compounds in the liquid extract of SCG were well separated. The simulated results of TZ-SMB at the optimal point revealed that the flow rates of desorbent, feed, extract product, and raffinate product were 0.626, 0.115, 0.081, and 0.593 mL/min, respectively, with the switching time of 20 min. At this point, the relative purities of caffeine in the extract product and chlorogenic acid in the raffinate product were 99.45 % and 98.88 %, respectively, with the maximum productivity of 0.045 mg/mL⋅h. In addition, for demonstration purposes, the lab-scale TZ-SMB experiment was conducted to show the separation of chlorogenic acid and caffeine in the liquid extract of SCG. The operating point from the triangle separation region was chosen based on the sensitivity of flow rate that ensured the criteria of purity. The experimental results showed that the relative purities of caffeine in extract product and chlorogenic acid in raffinate product were both 100 %, verifying the successful separation.

Study on evolution process and electrochemical behavior of porous cathode in discharge process of the solid-state lithium-oxygen battery

Solid-state lithium‑oxygen battery is one of the lithium-metal batteries with high theoretical specific capacity and strong safety. In order to explore the evolution process and electrochemical behavior of the battery, a study of combination of simulation and experiment is conducted in this paper. Firstly, solid-state lithium‑oxygen batteries with quartz tubes as the battery shell are designed for the experiment. FTIR and Raman results show that the lithium peroxide is produced as discharge products and SEM images show the product is accumulated on the surface of carbon particles and bound the carbon particles into blocks during the cathode evolution process. Then, a 1D multi-physics coupling field model referred to the experiment is established to investigate the electrochemical behavior of the battery. The calculated discharge curves are compared with the experimental discharge curves to verify the accuracy of the model. Using the model, the electrochemical behaviors, such as discharge product volume fraction, active three-phase interface, LATP phase potential and carbon phase potential, are analyzed. At last, the reasons for discharge termination are attributed to the accumulation of lithium peroxide and the decline of active three-phase interface.

Estimation of Two Component Activities of Binary Liquid Alloys by the Pair Potential Energy Containing a Polynomial of the Partial Radial Distribution Function

An investigation of partial radial distribution functions and atomic pair potentials within a system has established that the existing potential functions are rooted in the assumption of a static arrangement of atoms, overlooking their distribution and vibration. In this study, Hill’s proposed radial distribution function polynomials are applied for the pure gaseous state to a binary liquid alloy to derive the pair potential energy. The partial radial distribution functions of 36 binary liquid alloy from literatures were used to obtain the binary model parameters of four thermodynamic models for validation. Results show that the regular solution model (RSM) and molecular interaction volume model (MIVM) outperform other models when the asymmetric method calculates the partial radial distribution function. RSM demonstrates an average SD of 0.078 and an ARD of 32.2%. Similarly, MIVM exhibits an average SD of 0.095 and an average ARD of 32.2%. Wilson model yields an average SD of 0.124 and an average ARD of 226%. Nonrandom two-liquid (NRTL) model exhibits an average SD of 0.225 and an average ARD of 911%. On applying the partial radial distribution function symmetry method, MIVM and RSM outperform the other models, with an average SD of 0.143 and an average ARD of 165.9% for MIVM. RSM yields an average SD of 0.117 and an average ARD of 208.3%. Wilson model exhibits average values of 0.133 and 305.6% for SD and ARD, respectively. NRTL model shows an average SD of 0.200 and an average ARD of 771.8%. Based on this result, the influence of the symmetry degree on the thermodynamic model is explored by examining the symmetry degree as defined by the experimental activity curves of the two components.

Shear-wave velocity determination by combining data from passive and active source field investigations in Kumamoto city, Japan

We present the 1D subsoil structure and local site effects at KUMA strong ground motion station in Kumamoto City, Japan. We analyze data from a field campaign conducted in the framework of the Blind Prediction BP1 test of the 6th IASPEI/IAEE International Symposium: Effects of Surface Geology on Seismic Motion. In parallel with other participants of the BP1 test, we process data from passive and active source measurements aiming to determine the shear-wave velocity, Vs, structure and the site response at KUMA station. Passive measurements are associated to five microtremor arrays. In each array, seven seismometers have been deployed in a common-center triangle shape, recording microtremors simultaneously for 1 to 2 h. The vertical component of microtremors was analyzed using the spatial autocorrelation (SPAC) method. Cross-correlation coefficients were computed for all station pairs available for each array. By fitting the average SPAC’s coefficients to the first-kind zero-order Bessel function, J0, and assuming that microtremors primarily comprise fundamental mode Rayleigh waves, phase velocity dispersion curves were determined. Phase velocity values for frequencies > 15 Hz were obtained from data of a close-by active source geophone profile. We integrated the data with those of the passive measurements and obtained an experimental phase velocity dispersion curve. The resulting curve shows low velocity variation, from 150 to 200 m/s, in the surface layers, whereas significant dispersion appears in frequencies below 2.5 Hz. By inverting this curve, we achieved to determine the 1D shear-wave velocity structure at KUMA station. Site response characteristics were determined by applying the Horizontal-to-Vertical-Spectral-Ratios method. Significantly amplified peaks in the frequency range between 0.3 to 1.5 Hz dominate HVSR spectral ratios. These peaks correspond to resonant frequencies of soils and originate from different impedance contrasts within the substratum of the site.Graphical

Infiltration Measurements during Dry Conditions in an Urban Park in Ljubljana, Slovenia

A thorough understanding of the hydrologic mechanisms that control the movement of water through the soil is essential for developing effective stormwater management strategies. Infiltration is critical for determining the amount of water entering the soil and controlling surface runoff. Spatial and temporal variations in soil properties strongly affect infiltration rates, which underscores the importance of evaluating field-specific values for hydraulic conductivity, which are also highly dependent on the chosen measurement and evaluation methods. The objective of this study is to determine and compare soil hydraulic conductivity under dry conditions using two field measurement techniques, namely the double-ring infiltrometer (DRI) and the mini-disk infiltrometer (MDI). The results demonstrate the importance of performing multiple replicates of infiltration tests, especially during the dry season, as the initial dry surface caused deviations in hydraulic conductivity estimates for both methods used (DRI and MDI). Significant spatial variability was observed within the radius of the test replicates over short distances (&lt;1 m). In addition, experimental infiltration curves for a selected site were used to evaluate and compare soil hydraulic parameters through infiltration modeling. In general, the Philip, Green-Ampt, and Smith-Parlange theoretical models showed a better fit to the experimental DRI data than the semi-empirical Horton model.

Megastigmane glycoside and phenol glycosides from the bark of Mallotus barbatus

A new megastigmane glycoside, barbatcoside A (1), and two new phenol glycosides, barbatcosides B (2) and C (3), together with eight known compounds (4-11) were isolated from the bark of Mallotus barbatus Müll. Arg. Their structures were elucidated using extensive 1D and 2D NMR as well as HRESIMS spectroscopic data. The stereochemistry of compounds 1 and 2 were established based on the experimental CD curves. The anti-inflammatory activities of compounds 1-11 from M. barbatus were evaluated using LPS-stimulated RAW 264.7 cell models. Compounds 2 and 3 substantially inhibited the release of NO with IC50 values of 34.78 μM and 20.73 μM, respectively.

Analyses of I-V curve hysteresis in perovskite solar cells using circuit simulator

We have analyzed current–voltage (I-V) hysteresis curves of perovskite solar cells by an equivalent circuit using a circuit simulator in order to quickly certificate cell performances. A circuit model that added a sub-diode with a large saturation current and a reverse diode to the basic equivalent circuit of a solar cell showed a typical I-V hysteresis curve by setting the junction capacitance of the reverse diode. Furthermore, the simulated I-V curves are in good agreement with the peculiar experimental curves where the maximum power point current density (Jpmax) is higher than the short circuit current density (Jsc) due to the setting of the sub-diode diffusion capacitance. Based on the equivalent circuit model, we have proposed a hysteresis-inducing cell structure in which the grains and grain boundaries of the perovskite film form the main diode of the solar cell and the sub-diode with a large saturation current, respectively.