Effects of Chemicals Exposure on the Durability of Geopolymer Concrete Incorporated with Silica Fumes and Nano-Sized Silica at Varying Curing Temperatures.

Durable concrete significantly reduces the spalling caused by chemical damage. The objective of current research is to substitute cement with supplementary such as fly ash (FA), ground granulated blast furnace slag (GGBS), and alccofine (AF). Additionally, the impact of nano-silica (NS) and silica fumes (SF) on the GPC durability when cured at various temperatures has been attempted. In order to perform this, GPC samples were produced by combining NS and SF at proportions of 0.5% NS + 5% SF, 1% NS + 10% SF, and 1.5% NS + 15% SF, and then cured at temperatures of 27 °C, 60 °C, 90 °C, and 120 °C, respectively. In this research, all concrete specimens were continuously immersed for twelve weeks under four different chemicals, i.e., HCl (2%), H2SO4 (2%), NaCl (6%), and Na2SO4 (6%). The influence of chemical attack on the qualities of concrete was examined by evaluating the water absorption, sorptivity, loss of mass, and loss of GPC strength. The durability aspect is also studied by visual appearance and mass loss under harmful chemical attack. The combination of GPC with integrated NS and SF affords great resistance against chemical attacks. The percentages of these two components are 1.5% and 15%. For GPC specimens, when cured at 90 °C, the resultant strength is found at its maximum.

GO/CuO Nanohybrid-Based Carbon Dioxide Gas Sensors with an Arduino Detection Unit.

A gas sensor is a device that detects the presence of gases in a specific area. This research work demonstrates the effectiveness of gas sensors based on graphene oxide (GO) and copper oxide (CuO) semiconductor nanomaterials for the detection of carbon dioxide. GO and CuO were prepared by the modified Hummer's method and precipitation method using CuCl2 as a precursor, respectively. These materials are made into a hybrid using poly(vinyl alcohol) (PVA)/poly(vinylpyrrolidone) (PVP) polymer solutions of low concentrations and are spin coated onto the pattern-etched copper-clad substrate. The sensor is tested using a source measurement unit (SMU) to obtain the change in the resistance of the sensor in open air and in a carbon dioxide environment. The fabricated sensor with an Arduino microcontroller detection unit showed a good sensing response of 60%.

Experimental investigation of H3PO4 activated papaya peels for methylene blue dye removal from aqueous solution: Evaluation on optimization, kinetics, isotherm, thermodynamics, and reusability studies.

This investigation is centered on the effectiveness of methylene blue (MB), a cationic dye, adsorbed from an aqueous media by H3PO4 activated papaya skin/peels (PSPAC), with initial pH (2-10), contact time (30-180 min), MB dye concentration (varying from 10 to 50 mg/L), and MB dose (0.1-0.5 gm). The findings show that the best optimal conditions for MB dye removal occur at a 6 pH, 0.3 gm dose of PSPAC adsorbent for 10 mg/L MB dye concentration, with 90 min of contact time. To optimize and validate the extraction efficiency of MB dye, a response surface methodology (RSM) study was conducted using a central composite design (CCD) with a regression model showing R2 = 0.9940. FT-IR spectroscopy shows, CO, and O-H stretching functional groups while FE-SEM is assessed to supervise morphological features of the PSPAC adsorbent. The peak adsorption capacity with 46.95 mg/g for the Langmuir isotherm model conveniently satisfies the adsorption process with R2 = 0.9984 while with R2 = 0.999, a kinetic model, pseudo-second-order, confirms MB dye adsorption by PSPAC adsorbent. Moreover, thermodynamic parameters including ΔGᵒ, ΔH°, and ΔS° were computed and found to be spontaneous and exothermic. Furthermore, regeneration studies employed with NaOH (0.1 M) and HCl (0.1 M) solution media show an acceptable MB removal efficiency consecutive up to three cycles. The study highlights that H3PO4 papaya skin/peel (PSPAC) is an effectual, sustainable, reasonably available biosorbent to remove industrial cationic dyes disposal.

Sustainable landfill sites selection using geospatial information and AHP-GDM approach: A case study of Abha-Khamis in Saudi Arabia.

Social, environmental, and technical factors must be combined to solve the complex problem of ever-growing municipal solid waste (MSW) and minimize its negative impact on the environment. Saudi Arabia has launched a US$13 billion tourism strategy to transform the Asir region into a year-round tourist destination and has pledged to welcome 10 million local and foreign visitors by 2030. The estimated share of Abha-Khamis will increase to 7.18 million tons of household waste per year. With a gross domestic product (GDP) of USD 820.00 billion by the end of 2022, Saudi Arabia can no longer afford to neglect the issue of waste production and its safe disposal. In this study, to account for all factors and evaluation criteria, a combination of remote sensing, geographic information systems and an analytical hierarchy process (AHP) was used to determine the best locations for municipal solid waste (MSW) disposal in Abha-Khamis. The analysis revealed that 60% of the study area consists of faults (14.28%), drainage networks (12.80%), urban (11.43%), land use (11.41%) and roads (8.35%), while 40% of the suitable area for landfill. Of these, a total of 20 sites ranging in size from 100 to 595 ha are distributed at reasonable distances from the cities of Abha-Khamis, which meet all the critical criteria for suitable landfill sites mentioned in the literature. Current research shows that the use of integrated remote sensing, GIS and the AHP-GDM approach significantly improves the identification of land suitability for MSW management.

Green synthesis of metalloid nanoparticles and its biological applications: A review.

Synthesis of metalloid nanoparticles using biological-based fabrication has become an efficient alternative surpassing the existing physical and chemical approaches because there is a need for developing safer, more reliable, cleaner, and more eco-friendly methods for their preparation. Over the last few years, the biosynthesis of metalloid nanoparticles using biological materials has received increased attention due to its pharmaceutical, biomedical, and environmental applications. Biosynthesis using bacterial, fungal, and plant agents has appeared as a faster developing domain in bio-based nanotechnology globally along with other biological entities, thus posing as an option for conventional physical as well as chemical methods. These agents can efficiently produce environment-friendly nanoparticles with the desired composition, morphology (shape as well as size), and stability, along with homogeneity. Besides this, metalloid nanoparticles possess various applications like antibacterial by damaging bacterial cell membranes, anticancer due to damaging tumour sites, targeted drug delivery, drug testing, and diagnostic roles. This review summarizes the various studies associated with the biosynthesis of metalloid particles, namely, tellurium, arsenic, silicon, boron, and antimony, along with their therapeutic, pharmaceutical and environmental applications.

Imputation of missing monthly rainfall data using machine learning and spatial interpolation approaches in Thale Sap Songkhla River Basin, Thailand.

Missing rainfall data has been a prevalent issue and primarily interested in hydrology and meteorology. This research aimed to examine the capability of machine learning (ML) and spatial interpolation (SI) methods to estimate missing monthly rainfall data. Six ML algorithms (i.e. multiple linear regression (MLR), M5 model tree (M5), random forest (RF), support vector regression (SVR), multilayer perceptron (MLP), genetic programming (GP)) and four SI methods (i.e. arithmetic average (AA), inverse distance weighting (IDW), correlation coefficient weighted (CCW), normal ratio (NR)) were investigated and compared in their performance. The twelve rainfall stations, located in the Thale Sap Songkhla river basin and nearby basins, were considered as a study case. Tuning hyper-parameters for each ML method was conducted to get the most suitable model for the data sets considered. Three performance criteria matrices (i.e. NSE, OI, and r) were chosen, and the sum of those three performance criteria matrices was introduced for methods' performance comparison. The experimental results pointed out that selecting neighbouring stations were essential when applying SI methods, but not for the ML method. The overall performance showed ML better imputed missing monthly rainfall than SI due to overcoming spatial constraints. GP provided the highest performance by giving NSE = 0.825, OI = 0.877, and r = 0.909 for the training stage. Those values for the testing stage were 0.796, 0.852, and 0.902, respectively. It was followed by SVR-rbf, SVR-poly, and RF. NR provided the best performance among four SI methods, followed by CCW, AA, and IDW. When applying SI methods, it should contemplate a correlation between the target and neighbouring stations greater than 0.80.

An emergent addition for the optimal systemization of wastewater utilization plants using artificial intelligence.

The treatment of wastewater is an essential factor in preventing pollutants and promoting the quality of the water. The inherent complexity, influential impact and the solid waste infrastructure all lead to confusion and variance in the primary clarifier for wastewater. These inconsistencies lead to variations in the purity and capacity constraints of wastewater and the existential impact of water receipt. Water treatment is a complicated task that has chemical, technical and biochemical aspects. A credible artificial neural network (ANN) method is necessary for another wastewater treatment plant to prevent the breakdown of the processes. Virtual reality seems to have become a strong solution for preventing waste management uncertainties and problems. This is not only due to extreme changes but also to significant external disturbances that water systems are subjected to when controlling challenges. Climate is among the most significant of such disturbances. Various environmental conditions actually include different influx frequencies and levels of substances. Water contamination has become one of the extremely serious growing concerns; sewage treatment plant identification is a key major issue here and the agencies enforce tighter requirements when operating wastewater software systems. This article plans to create models of achievement and prospects when possible future guidance of recent research borders for the use of artificial intelligence in wastewater treatment plants that concurrently deal with pollutants. This study has shown us that the composite ANN provides a greater level of competence in plant prediction and systemization.

Characteristics and Photovoltaic Applications of Au-Doped ZnO-Sm Nanoparticle Films.

Au-doped ZnO-samarium nitrate (Sm) nanoparticles with fixed concentrations of Sm (1 wt %) and various concentrations of Au (0.0, 0.5, 1.0 and 1.5 wt %) were prepared and used as photoelectrodes to enhance the photovoltaic efficiency of dye-sensitized solar cells (DSSCs). The cell fabricated with 1.5 wt % of Au-doped ZnO-Sm nanoparticles film achieved an optimal efficiency of 4.35%, which is about 76% higher than that of 0.0 wt % of Au-doped ZnO-Sm-based cell (2.47%). This increase might be due to the formation of a blocking layer at the ZnO-Sm/Au interface, which inhibits the recombination of electrons. This increase may also be attributed to the addition of rare-earth ions in ZnO to enhance the non-absorbable wavelength region of light via up/down-conversion of near-infrared and ultraviolet radiations to visible emission and reduce the recombination loss of electron in the cell. The efficiency of cells may be increased by the blocking layer and up/down-conversion process and thus promote the overall performance of the cells. This work indicates that Au-doped ZnO-Sm nanoparticle films have the potential to increase the performance of DSSCs.

Comparative Overview of the Performance of Cementitious and Non-Cementitious Nanomaterials in Mortar at Normal and Elevated Temperatures.

Nanotechnology has emerged as a field with promising applications in building materials. Nanotechnology-based mortars are examples of such building materials that have widespread applications in the construction industry. The main nanomaterials used in mortars include nano-silica, nano-magnesium oxide, nano-alumina, nano-titanium oxide, nano-zinc oxide, nano-clay, and nano-carbon. This review paper presents a summary of the properties and effects of these nanomaterials on cement mortar in terms of its fresh-state and hard-state properties. The fresh-state properties include the setting time, consistency, and workability, while the hard-state properties include mechanical properties such as compressive, flexural, tensile strengths, as well as the elasticity modulus, in addition to durability properties such as water absorption, shrinkage strain, strength loss due to freeze-thaw cycles, and chloride penetration, among others. Different nanomaterials cause different physical and chemical alterations within the microstructures of cement mortar. Therefore, the microstructural characterization and densification of mortar are discussed in detail at varying temperatures. In general, the involvement of nanomaterials in cement mortar influences the fresh-state properties, enhances the mechanical properties, and impacts the durability properties, while reducing the porosity present in the mortar matrix. Cementitious nanomaterials can create a pathway for the easy injection of binding materials into the internal microstructures of a hydration gel to impact the hydration process at different rates, whereas their non-cementitious counterparts can act as fillers. Furthermore, the research gaps and future outlook regarding the application of nanomaterials in mortar are discussed.