Surface Parameters Research Articles

Synergistic adsorption and photocatalytic degradation of tetracycline using a Z-scheme kaolin/g-C3N4/MoO3 nanocomposite: A sustainable approach for water treatment

This research focuses on the synthesis and application of a novel kaolin-supported g-C3N4/MoO3 nanocomposite for the degradation of tetracycline, an important antibiotic contaminant in water systems. The nanocomposite was prepared through a facile and environmentally friendly approach, leveraging the adsorption and photocatalytic properties of kaolin, g-C3N4 and MoO3 nanoparticles, respectively. Comprehensive characterization of the nanocomposite was conducted using techniques such as X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR) and optical spectra. The surface parameters were studied using N2 adsorption-desorption isotherm. The elemental composition was studied using X-ray photoelectron spectroscopy. The efficiency of the developed nanocomposite in tetracycline degradation was evaluated and the results revealed an efficient tetracycline degradation exhibiting the synergistic effects of adsorption and photocatalytic degradation in the removal process. The tetracycline degradation was achieved in 60 min. Kinetic studies and thermodynamic analyses provided insights into the degradation mechanism, suggesting potential applications for the nanocomposite in wastewater treatment. Additionally, the recyclability and stability of the nanocomposite were investigated, demonstrating its potential for sustainable and long-term application in water treatment.

Construction and Validation of Surface Soil Moisture Inversion Model Based on Remote Sensing and Neural Network

Surface soil moisture (SSM) reflects the dry and wet states of soil. Microwave remote sensing technology can accurately obtain regional SSM in real time and effectively improve the level of agricultural drought monitoring, and it is of great significance for agricultural precision irrigation and smart agriculture construction. Based on Sentinel-1, Sentinel-2, and Landsat-8 images, the effect of vegetation was removed by the water cloud model (WCM), and SSM was retrieved and validated by a radial basis function (RBF) neural network model in bare soil and vegetated areas, respectively. The normalized difference vegetation index (NDVI) calculated by Landsat-8 (NDVI_Landsat-8) had a better effect on removing the influence the of vegetation layer than that of NDVI_Sentinel-2. The RBF network model, established in a bare area (R = 0.796; RMSE = 0.029 cm3/cm3), and the RBF neural network model, established in vegetated areas (R = 0.855; RMSE = 0.024 cm3/cm3), have better simulation effects on SSM than a linear SSM inversion model with single polarization. The introduction of surface parameters to the RBF neural network model can improve the accuracy of the model and realize the high-accuracy inversion of SSM in the study area.

Evaluation of adjuvant role of topical cyclosporine 1% in acute Stevens-Johnson syndrome: a randomised control trial

PurposeTo evaluate the role of topical cyclosporine A 1% (CsA) as an adjuvant therapy in patients with acute Stevens-Johnson syndrome (SJS).MethodsThis is a randomised controlled trial in which 44 patients...

Impact of canopy environmental variables on the diurnal dynamics of water and carbon dioxide exchange at leaf and canopy level

Abstract. Quantifying water vapor and carbon dioxide (CO2) exchange dynamics between the land and the atmosphere through observations and modeling is necessary in order to reproduce and project near-surface climate in coupled land–atmosphere models. The exchange of water and CO2 occurs at the leaf surface (leaf level) and in a net manner through exchanges at all the leaf surfaces composing the vegetation canopy and at the soil surface (canopy level). These exchanges depend on the meteorological forcings imposed by the overlying atmosphere (atmospheric boundary layer level). In this paper, we investigate the effect of four canopy environmental variables (photosynthetic active radiation (PAR), water vapor pressure deficit (VPD), air temperature (T), and atmospheric CO2 concentration (Ca)) on the local individual leaf exchange and canopy exchange of water and CO2 at hourly timescales. Additionally, we investigate the effect of atmospheric boundary layer (ABL) processes on the local exchange. To that end, we simultaneously investigated the exchanges of water and CO2 at leaf level and canopy level for an alfalfa field in northern Spain over 1 day in summer 2021. We used comprehensive observations ranging from stomatal conductance to ABL measurements collected during the Land Surface Interactions with the Atmosphere in the Iberian Semi-Arid Environment (LIAISE) experiment. To support the observational analysis, we used a coupled land–atmosphere model (CLASS model) that has representations at all levels considered. To relate how temporal changes of the four environmental variables modify the fluxes of water and CO2, we studied tendency equations of the leaf gas exchange. These mathematical expressions quantify the temporal evolution of the leaf gas exchange as a function of the temporal evolution of PAR, VPD, T, and Ca. To investigate the effects of ABL processes on the local exchange, we developed three modeling experiments that impose surface radiative perturbations by a cloud passage (which perturbed PAR, T, and VPD), entrainment of dry air from the free troposphere (which perturbed VPD), and advection of cold air (which perturbed T and VPD). The model results and observations matched the leaf gas exchange (r2 between 0.23 and 0.67) and canopy gas exchange (r2 between 0.90 and 0.95). The tendency equations of the modeled leaf gas exchange during the study day revealed that the temporal dynamics of PAR were the main contributor to the temporal dynamics of the leaf gas exchange, with atmospheric CO2 temporal dynamics being the least important contributor. From the three modeling experiments with ABL perturbations, the surface radiative changes induced by a cloud perturbed the CO2 exchange the most, whereas all of them perturbed the water exchange to a similar extent. Second-order effects on the dynamics of the leaf gas exchange were also identified using the tendency equations. For instance, the decrease in the net CO2 assimilation rate during the cloud passage caused by a decrease in surface radiation was further enhanced due to the decrease in air temperature also associated with the cloud. With this research we showcase that the proposed tendency equations can disentangle the effect of environmental variables on the leaf exchange of water and CO2 with the atmosphere, as represented in land–surface parameterization schemes. As such, this framework can become a useful tool with which to analyze these schemes in weather and climate models.

Does the ionic distribution in the electrical double layer modify second harmonic scattering?

Surface-specific nonlinear optical techniques are ideally suited to investigate the complex structure of aqueous interfaces. For colloidal particles dispersed in aqueous solutions, interfacial properties can be retrieved with angle-resolved second harmonic scattering (AR-SHS). The mathematical framework of AR-SHS does not require a priori knowledge on the electrostatic distribution in the first few nanometers close to the interface, therefore allowing us to formulate a molecular-level description of the electrical double layer (EDL) based on the experimental data. However, farther away from the interface, an analytical form of the electrostatic potential decay is necessary to account for the distance dependence of the surface electrostatic field propagating into the solution. This requirement is especially important at low ionic strengths, where the electrostatic field is not efficiently screened by counterions. Here, we examine to what extent the analytical form of the electrostatic potential decay impacts the AR-SHS data analysis. We analyze the effect of different functions on the scattering form factors, on the integrated AR-SHS signal intensity, and on the surface parameters extracted from fitting the AR-SHS data. We find that the trends of the surface parameters remain similar regardless of the chosen function, demonstrating the robustness of our approach to establish a molecular-level picture of the EDL. At ionic strengths <10-4M for 100-nm diameter particles, a functional form that physically represents counterions packed more densely in the vicinity of the surface than in the case of the Poisson-Boltzmann distribution has the largest impact, resulting in an overestimation of the obtained surface potential.

Role of surface and sub-surface ocean parameters on cyclonic storms over Arabian Sea in the recent decades

Role of surface and sub-surface ocean parameters on cyclonic storms over Arabian Sea in the recent decades

Alterations in the tear film and ocular surface in pediatric migraine patients.

To evaluate the ocular surface (OS) parameters in the pediatric migraine patients (PMPs). This prospective case-control study consisted of 51 PMPs (PMP group) and 55 healthy pediatric patients (HPP group). In all participants, tear function was evaluated subjectively using the Ocular Surface Disease Index (OSDI) questionnaire, objectively using Schirmer tear test (STT) and tear film disintegration time (TBUT), and with clinical and laboratory examinations (conjunctival impression cytology). The PMP group was subdivided into two groups according to their aura. The mean age and gender distribution of the study groups were almost the same (P > 0.05 for both of them). In the PMP group, both the STT value and the TBUT value were significantly lower than those determined in the HPP group (P = 0.021 and P = 0.018, respectively). In the PMP group, the OSDI scores were higher than those in the HPP group (P = 0.032). In the PMP group, the goblet cell density values were lower than those in the HPP group (P = 0.01). With regard to the aura, the TBUT and STT values were nonsignificantly lower in the PMP aura-positive group than in the PMP aura-negative group (P > 0.05 for both of them). The OSDI assessment was similar in both the groups. With regard to the goblet cell count, it was observed to be less in the PMP aura-positive group than in the PMP aura-negative group (P = 0.01). Influence of OS in children with migraine was also demonstrated using the samples taken from the conjunctiva. These changes were also demonstrated by objective tests such as STT and TBUT. Both clinical objective evaluations and pathologic changes were more prominent in the migraine with aura group.

Investigating the impact of tropical deforestation on Indian monsoon hydro-climate: a novel study using a regional climate model

AbstractThis study uses a state-of-the-art regional climate model (RCM) to examine how tropical deforestation affects the meteorology of the Indian Summer Monsoon (ISM). Incorporating insights from existing research on deforestation by climate scientists, alongside evidence of environmental deterioration in semi-arid, hilly and tropical regions of Southeast Asia, this research seeks to elucidate the critical influence of anthropogenic reasons of climate change on the hydroclimate of ISM. Employing “tropical deforestation” design experiments with the ICTP-RegCMv4.4.5.10 RCM the study evaluates the effects on meteorological parameters including precipitation, circulation patterns and surface parameters. This experimental design entails substituting vegetation type in the land use map of RegCMv4.4.5.10 model, such as deciduous and evergreen trees in Southeast Asia with “short grass” to mimic tropical deforestation. Findings reveal that deforestation induces abnormal anti-cyclonic circulation over eastern India curtails moisture advection, diminishing latent heat flux and moisture transport, leads to a decrease in precipitation compared to control experiment scenario. Alterations in albedo and vegetation roughness length attributable to deforestation impact temperature, humidity, precipitation, consequently exacerbating drought and heatwave occurrences. Additionally, the study also explores deforestation-induced feedback on ISM precipitation variability. The study concludes that deforestation substantially alters land-surface characteristics, water and energy cycle, and atmospheric circulation, thereby influencing regional climate dynamics. These findings offer foundational insights into comprehending land-use and land-cover changes and their implications for climate change adaptation strategies.

Deterministic direct design method for a spherical-aberration-free singlet lens with reduced coma

It is still challenging to find a spherical-aberration-free singlet lens with well corrected coma due to an undesired and complicated residual high-order coma. In this paper, we present a spherical-aberration-free singlet lens with reduced coma containing high-order coma contribution. This design algorithm is to deduce the front aspherical surface parameters from the back spherical surface using meridional ray tracing to find the proper values of the back focal length and the back spherical radius to reduce the coma. The exemplary lens demonstrates an excellent well-balanced and diffraction-limited performance at the field angle ranging from 0.0° to 2.5° with a working F# equal to 1.65.

Relation Models of Surface Parameters and Backscattering (or Radiation) Fields as a Tool for Solving Remote Sensing Problems

In this paper, an analysis of existing models for describing surfaces of various types is performed, and the possibilities of their application at the level of mathematical modeling are analyzed. Moreover, due to the large number of models and the complexity of selecting the appropriate model, e.g., when conducting a practical experiment, an algorithm for choosing a specific model depending on the initial data is proposed. According to the algorithm, a software prototype that implements this algorithm (written in Python) is proposed.

ZPD Retrieval Performances of the First Operational Ship-Based Network of GNSS Receivers over the North-West Mediterranean Sea.

This work presents the design and implementation of an operational infrastructure for the monitoring of atmospheric parameters at sea through GNSS meteorology sensors installed on liners operating in the north-west Mediterranean Sea. A measurement system, capable of operationally and continuously providing the values of surface parameters, is implemented together with software procedures based on a float-PPP approach for estimating zenith path delay (ZPD) values. The values continuously registered over a three year period (2020-2022) from this infrastructure are compared with the data from a numerical meteorological reanalysis model (MERRA-2). The results clearly prove the ability of the system to estimate the ZPD from ship-based GNSS-meteo equipment, with the accuracy evaluated in terms of correlation and root mean square error reaching values between 0.94 and 0.65 and between 18.4 and 42.9 mm, these extreme values being from the best and worst performing installations, respectively. This offers a new perspective on the operational exploitation of GNSS signals over sea areas in climate and operational meteorological applications.

Experimental Study on the Hot Surface Ignition Characteristics and a Predictive Model of Marine Diesel in a Ship Engine Room

Experimental Study on the Hot Surface Ignition Characteristics and a Predictive Model of Marine Diesel in a Ship Engine Room

Recent Developments to the SimSphere Land Surface Modelling Tool for the Study of Land–Atmosphere Interactions

Soil–Vegetation–Atmosphere Transfer (SVAT) models are a promising avenue towards gaining a better insight into land surface interactions and Earth’s system dynamics. One such model developed for the academic and research community is the SimSphere SVAT model, a popular software toolkit employed for simulating interactions among the layers of vegetation, soil, and atmosphere on the land surface. The aim of the present review is two-fold: (1) to deliver a critical assessment of the model’s usage by the scientific and wider community over the last 15 years, and (2) to provide information on current software developments implemented in the model. From the review conducted herein, it is clearly evident that from the models’ inception to current day, SimSphere has received notable interest worldwide, and the dissemination of the model has continuously grown over the years. SimSphere has been used so far in several applications to study land surface interactions. The validation of the model performed worldwide has shown that it is able to produce realistic estimates of land surface parameters that have been validated, whereas detailed sensitivity analysis experiments conducted with the model have further confirmed its structure and architectural coherence. Furthermore, the recent inclusion of novel functionalities in the model, as outlined in the present review, has clearly resulted in improving its capabilities and in opening up new opportunities for its use by the wider community. SimSphere developments are also ongoing in different aspects, and its use as a toolkit towards advancing our understanding of land surface interactions from both educational and research points of view is anticipated to grow in the coming years.

Transferability of Machine Learning Models for Geogenic Contaminated Groundwaters.

Machine learning models show promise in identifying geogenic contaminated groundwaters. Modeling in regions with no or limited samples is challenging due to the need for large training sets. One potential solution is transferring existing models to such regions. This study explores the transferability of high fluoride groundwater models between basins in the Shanxi Rift System, considering six factors, including modeling methods, predictor types, data size, sample/predictor ratio (SPR), predictor range, and data informing. Results show that transferability is achieved only when model predictors are based on hydrochemical parameters rather than surface parameters. Data informing, i.e., adding samples from challenging regions to the training set, further enhances the transferability. Stepwise regression shows that hydrochemical predictors and data informing significantly improve transferability, while data size, SPR, and predictor range have no significant effects. Additionally, despite their stronger nonlinear capabilities, random forests and artificial neural networks do not necessarily surpass logistic regression in transferability. Lastly, we utilize the t-SNE algorithm to generate low-dimensional representations of data from different basins and compare these representations to elucidate the critical role of predictor types in transferability.

Optimization of hardness values via Taguchi method for chestnut wood etched or impregnated with sodium bicarbonate after artificial aging

In today’s wood industry, research is being conducted to increase material strength, ensure long-term use, and increase its hardness against many harmful external factors. With the studies on the protection of wood, new protection materials and methods are introduced. In this study, wood was etched with solid sodium bicarbonate (NaHCO3) and 1, 2, and 3 bars of air pressure after treatment with NaHCO3 solution and drying. The change in hardness values of impregnated and surface-treated (paint, varnish) chestnut wood after artificial aging was examined. Etching, impregnation, and surface treatment factors were optimized using the Taguchi design of experiments (DoE) after artificial aging for 3, 6, and 9 months. L16 orthogonal array was used to determine the optimum conditions for determining hardness values and their percentage changes. The results showed that the abrasive factor prevails over the effect of surface treatments. It has been understood that the most effective factor in the hardness value changes during the artificial aging period (3-6-9 months) is abrasion, and the factor with the least effect is the surface parameter. The percentage accuracy of the model used in estimating the wear factor average R2 across all dependent variables was 95%.

Broadleaf afforestation impacts on terrestrial hydrology insignificant compared to climate change in Great Britain

Abstract. Widespread afforestation has been proposed internationally to reduce atmospheric carbon dioxide; however, the specific hydrological consequences and benefits of such large-scale afforestation (e.g. natural flood management) are poorly understood. We use a high-resolution land surface model, the Joint UK Land Environment Simulator (JULES), with realistic potential afforestation scenarios to quantify possible hydrological change across Great Britain in both present and projected climate. We assess whether proposed afforestation produces significantly different regional responses across regions; whether hydrological fluxes, stores and events are significantly altered by afforestation relative to climate; and how future hydrological processes may be altered up to 2050. Additionally, this enables determination of the relative sensitivity of land surface process representation in JULES compared to climate changes. For these three aims we run simulations using (i) past climate with proposed land cover changes and known floods and drought events; (ii) past climate with independent changes in precipitation, temperature, and CO2; and (iii) a potential future climate (2020–2050). We find the proposed scale of afforestation is unlikely to significantly alter regional hydrology; however, it can noticeably decrease low flows whilst not reducing high flows. The afforestation levels minimally impact hydrological processes compared to changes in precipitation, temperature, and CO2. Warming average temperatures (+3 °C) decreases streamflow, while rising precipitation (130 %) and CO2 (600 ppm) increase streamflow. Changes in high flow are generated because of evaporative parameterizations, whereas low flows are controlled by runoff model parameterizations. In this study, land surface parameters within a land surface model do not substantially alter hydrological processes when compared to climate.

A dynamic sliding model of hard spherical surfaces on a flat soft plane

This study proposes a dynamic sliding model of hard spherical surfaces on a flat soft plane. For the sphere sliding at velocity v, the dynamic frictional force was described as Fd = λAv using a surface parameter λ and the contact area A. The compressive force was expressed as Pd = λ'A'v using a surface parameter λ' and the sectional area A'. An analytical formula for the sliding motion was derived considering Fd and the drag force Df associated with Pd. The model was validated by comparing the experimental velocities of stainless steel spherical surfaces on a flat polypropylene plate. The model showed all the dominant aspects of the measured velocities during the sliding process.

Impact of Randomized Soil Properties and Rock Motion Intensities on Ground Motion

Seismic site response is inevitably influenced by natural variability of soil properties and anticipated earthquake intensity. This study presents the influence of variability in shear wave velocity (Vs) and amplitude of input rock motion on seismic site response analysis. Monte Carlo simulations were employed to randomize the Vs profile for different scenarios. A series of 1-D equivalent linear (EQL) seismic site response analyses were conducted by combining the randomized Vs profile with different levels of rock motion intensities. The results of the analyses are presented in terms of surface spectral acceleration, amplification factors (AFs), and peak ground acceleration (PGA). The mean and standard deviation of these parameters are thoroughly discussed for a wide range of randomized Vs profile, number of Vs randomizations, and intensities of input rock motions. The results demonstrate that both the median PGA and its standard deviations across different number of Vs profile realization exhibit a slight variation. As few as twenty Vs profile realizations are sufficient to compute reliable response parameters. Both rock motion intensity and standard deviation of Vs variability cause significant variation in computed surface parameters. However, the variability in the number of records used to conduct site response has no significant impact on ground response if the records closely match the target spectrum. Incorporating the multiple sources of variabilities can reduce uncertainty when conducting ground response simulations.

Strain energy density and entire fracture surface parameters relationship for LCF life prediction of additively manufactured 18Ni300 steel

In this study, the connection between total strain energy density and fracture surface topography is investigated in additively manufactured maraging steel exposed to low-cycle fatigue loading. The specimens were fabricated using laser beam powder bed fusion (LB-PBF) and examined under fully-reversed strain-controlled setup at strain amplitudes scale from 0.3% to 1.0%. The post-mortem fracture surfaces were explored using a non-contact 3D surface topography measuring system and the entire fracture surface method. The focus is on the relationship between fatigue characteristics, expressed by the total strain energy density, and the fracture surface topography features, represented by areal, volume, and fractal dimension factors. A fatigue life prediction model based on total strain energy density and fracture surface topography parameters is proposed. The presented model shows good accordance with fatigue test results and outperforms other existing models based on the strain energy density. This model can be useful for post-failure analysis of engineering elements under low-cycle fatigue, especially for materials produced by additive manufacturing (AM).

Application of the TOPSIS decision-making method for selecting a manufacturing technique for children’s furniture elements with therapeutic functions

AbstractMaking furniture or furniture elements that account for the needs of children at various stages of development or with psychomotor dysfunctions is very difficult. From the point of view of exploitation and production technology, it is difficult to select a specific material and manufacturing technique. In this article, the results of using the APEKS method, which is a type of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, are presented to select the best solution for the production of children’s furniture elements with surface structures similar to those of natural materials. Wood bark was selected as a material that, due to the sensory tactile sensations of dysfunctional children, could contribute to therapy and education. Comparative analysis was performed on the basis of the subtractive and additive methods used for manufacturing furniture products. Precise multiaxis milling of ash wood and 3D printing with fused filament fabrication technology using wood PLA filaments were carried out. The method used to select the best option considered quantitative and qualitative criteria in the assessment. Various parameters characterizing the surfaces were analyzed, such as geometric dimensions, hill heights, valley depths, and 3D surface parameters. The quality and surface roughness (Sa, Sz, Ssk, Sku, Sp, and Sv) parameters obtained based on 3D microscope measurements were determined. A scale of 1 to 10 was used to assess qualitative factors (i.e., usability and aesthetics). Based on the critical values obtained from the coefficient Kcri = 79.36, it was assumed that multiaxis wood milling was the best method for producing furniture elements with the required surface characteristics for use as therapeutic and educational tools for children with dysfunctions. The applied method allowed an effective evaluation of the compared variants of the production of furniture elements for customized applications.