Enhancing clayey soil performance with lime and waste rubber tyre powder: Mechanical, microstructural, and statistical analysis.

Nowadays, for soil stabilisation and cleaner production of geo-composites, the possibility of utilizing waste rubber is in vogue. The present paper deals with experimentally investigating the mechanical and micro-structural characteristics of weak Indian clayey soil partially substituted with lime (0-3.5%) and waste rubber tyre powder (0-15%). It was observed that, with increasing lime and rubber powder content, the plasticity index of the soil decreases. The shear strength and compaction testing results reveal that adding lime and rubber tyre powder (RTP) enhances the geotechnical performance of clayey soil up to an optimum dosage value. Also, the tri-axial shear testing was performed to obtain stress-strain curves for all considered soil mixes. For modified clayey soil containing 3% lime and 12.5% rubber powder, the cohesion values and bearing capacities improved phenomenally by 36.1% and 88.6% respectively, when compared to clayey soil. Further for this mix, SEM analysis reveals a compacted microstructure which improves dry-density and California's bearing ratio among all modified mixes. The novel co-relations upon regression analysis are found able to predict plasticity index, dry density, bearing capacity and shear strength with higher confidence levels. Overall, the cost-benefit analysis worked out to obtain the optimum cost of construction of footings and flexible pavement shows cost deductions up to 19% and 39% respectively while utilizing modified clay soil mixes containing 3% lime and 12.5% rubber powder in subgrade, ultimately making production stronger, cheaper and environment friendly.

Evaluating ionic liquids for its potential as eco-friendly solvents for naproxen removal from water sources using COSMO-RS: Computational and experimental validation.

An emerging contaminant of concern in aqueous streams is naproxen. Due to its poor solubility, non-biodegradability, and pharmaceutically active nature, the separation is challenging. Conventional solvents employed for naproxen are toxic and harmful. Ionic liquids (ILs) have attracted great attention as greener solubilizing and separating agent for various pharmaceuticals. ILs have found extensive usage as solvents in nanotechnological processes involving enzymatic reactions and whole cells. The employment of ILs can enhance the effectiveness and productivity of such bioprocesses. To avoid cumbersome experimental screening, in this study, conductor like screening model for real solvents (COSMO-RS) was used to screen ILs. Thirty anions and eight cations from various families were chosen. Activity coefficient at infinite dilution, capacity, selectivity, performance index, molecular interactions using σ-profiles and interaction energies were used to make predictions about solubility. According to the findings, quaternary ammonium cations, highly electronegative, and food-grade anions will form excellent ionic liquid combinations for solubilizing naproxen and hence will be better separating agents. This research will contribute easy designing of ionic liquid-based separation technologies for naproxen. In different separation technologies, ionic liquids can be employed as extractants, carriers, adsorbents, and absorbents.

An IoT based system for magnify air pollution monitoring and prognosis using hybrid artificial intelligence technique.

Air pollution is the existence of atmospheric chemicals damaging the health of human beings and other living organisms or damaging the environment or resources. Rarely any common contaminants are smog, nicotine, mold, yeast, biogas, or carbon dioxide. The paper will primarily observe, visualize and anticipate pollution levels. In particular, three algorithms of Artificial Intelligence were used to create good forecasting models and a predictive AQI model for 4 distinct gases: carbon dioxide, sulphur dioxide, nitrogen dioxide, and atmospheric particulate matter. Thus, in this paper, the Air Qualification Index is developed utilizing Linear Regression, Support Vector Regression, and the Gradient Boosted Decision Tree GBDT Ensembles model over the next 5 h and analyzes air qualities using various sensors. The hypothesized artificial intelligence models are evaluated to the Root Mean Squares Error, Mean Squared Error and Mean absolute error, depending upon the performance measurements and a lower error value model is chosen. Based on the algorithm of the Artificial Intelligent System, the level of 5 air pollutants like CO2, SO2, NO2, PM 2.5 and PM10 can be predicted immediately by integrating the observations with errors. It may be used to detect air quality from distance in large cities and can assist lower the degree of environmental pollution.

Experimental investigation of dehumidification process regulated by the photothermoelectric system.

To decrease indoor relative humidity and have relaxing environments, small dehumidifiers are widely used in tropical climatic. Due to the benefits of eco-friendly, small size and silence operation, the thermoelectric dehumidifier has gained interest but has limited practical application due to poor efficiency. Therefore, this study investigates the dehumidification characteristics of the thermoelectric module powered by a photovoltaic system for the production of fresh water under real climatic conditions. The performance of a novel prototype named as the Photo-Thermoelectric Dehumidifier (PVTE-D) was investigated both numerically and experimentally in different combinations of airflow rate and input power. The results obtained from the experiment suggested that the water condensate collection was increased by increasing the input power from a PV panel to the TE-D. In the month of May, the maximum water condensate collection of 1,852.3 mL/hr was attained at the input supply of 6 A and 5 V to the PVTE-D system. In the majority of cases, when the airflow rate is below 0.013 kg/s, maximum collections of water condensate have been achieved. This study provides a detailed understanding of the optimally suitable structural parameters of the PVTE-D under different operating conditions and reveals a novel configuration for higher water condensation capacity.

Data Description Technique-Based Islanding Classification for Single-Phase Grid-Connected Photovoltaic System.

This paper develops an islanding classification mechanism to overcome the problems of non-detection zones in conventional islanding detection mechanisms. This process is achieved by adapting the support vector-based data description technique with Gaussian radial basis function kernels for islanding and non-islanding events in single phase grid-connected photovoltaic (PV) systems. To overcome the non-detection zone, excess and deficit power imbalance conditions are considered for different loading conditions. These imbalances are characterized by the voltage dip scenario and were subjected to feature extraction for training with the machine learning technique. This is experimentally realized by training the machine learning classifier with different events on a 5   kW grid-connected system. Using the concept of detection and false alarm rates, the performance of the trained classifier is tested for multiple faults and power imbalance conditions. The results showed the effective operation of the classifier with a detection rate of 99.2% and a false alarm rate of 0.2%.

Advances in Nanoparticle-Enhanced Thermoelectric Materials from Synthesis to Energy Harvesting: A Review.

This comprehensive review analysis examines the domain of composite thermoelectric materials that integrate nanoparticles, providing a critical assessment of their methods for improving thermoelectric properties and the procedures used for their fabrication. This study examines several approaches to enhance power factor and lattice thermal conductivity, emphasizing the influence of secondary phases and structural alterations. This study investigates the impact of synthesis methods on the electrical characteristics of materials, with a particular focus on novel techniques such as electrodeposition onto carbon nanotubes. The acquired insights provide useful guidance for the creation of new thermoelectric materials. The review also compares and contrasts organic and inorganic thermoelectric materials, with a particular focus on the potential of inorganic materials in the context of waste heat recovery and power production within industries. This analysis highlights the role of inorganic materials in improving energy efficiency and promoting environmental sustainability.

Innovation modeling and simulation of thermal convective on cross nanofluid flow over exponentially stretchable surface.

This work reported to investigate convective flow of non-Newtonian fluid effect on an exponentially stretchable surface. Effect of nanoparticle is considered in heat and mass equation. The transformation technique utilized on dimensionless equations is converted to non-dimensionless equations are solved thought numerical approach Bvp4c. Influence of approatiate analysis of velocities, heat and mass transport are scrutinized through figures. Furthermore, the comparative analysis of drag forces, Nusselt number and Sherwood number are evaluated over and done with tabulated values. It is give details that the temperature field strengthens with intensification in thermophoresis and random diffusions. Similarly, rises in thermophoresis effect parameter both temperature and concentration profile increasing.

Enhancement in the efficiency of heat recovery in a Williamson hybrid nanofluid over a vertically thin needle with entropy generation.

The purpose of the present research is to conduct an examination of entropy generation in a 2D magneto Williamson hybrid nanofluid flow that contains cobalt ferrite and titanium oxide nanoparticles and undergoes surface-catalyzed reactions through a thin vertical needle. The consequences of joule heating and viscous dissipation are considered to elaborate the features of heat transport. Further, the influence of thermal stratification, thermal radiation, and homogeneous-heterogeneous reaction is also taken into account. Through the application of appropriate similarity variables, the dimensionless system of coupled ordinary differential equations is achieved. The coupled system of equations is numerically solved by the usage of the bvp4c technique in the MATLAB algorithm. The current investigation also compared the existing outcomes with the available literature, which shows great harmony between the two. The consequences of the physical parameters are discussed graphically and with numerical data. It is worth noting that larger values of homogeneous reaction strength and the surface-catalyzed parameter diminish the concentration field. Further, the velocity distribution and their related momentum boundary layer thickness, diminishes with the enlargement of the Weissenberg parameter.

Artificial ecosystem optimization with Deep Learning Enabled Water Quality Prediction and Classification model.

The majority of what is needed to maintain life is found in the approximately 70 percent of the earth's surface that is composed of water. Water quality has been deteriorating at an alarming rate as a direct result of rapid industrialization and urbanisation, which has led to a rise in the prevalence of serious diseases. In the past, determining the quality of water was typically accomplished by employing labor-intensive, time-consuming, and statistically pricey laboratory investigations, which renders the prevalent concept of real-time monitoring meaningless. The worrisome effect of poor water quality mandates the necessity of an alternative model that is both rapid and economical to implement. There has been a lot of talk about using artificial intelligence to forecast and model water quality as a means of preventing and reducing water pollution. An artificial ecosystem optimization with Deep Learning Enabled Water Quality Prediction and Classification (AEODL-WQPC) model is presented in this paper. The primary objectives of the AEODL-WQPC model that is being given are the prediction and categorization of different levels of water quality. As a first processing step, the data normalization technique is used to the provided AEODL-WQPC model so that this goal can be achieved. In addition to this, an optimal stacked bidirectional gated recurrent unit (OSBiGRU) model is used to forecast the water quality index (WQI), and the Adam optimizer is utilised in order to fine-tune the model's parameters. AEO with enhanced Elman Neural Network (AEO-IENN) model is utilised for the categorization of water quality. This model is characterized by the fact that the AEO algorithm effectively tunes the parameters associated to the ENN model. For the purposes of the experimental validation of the AEODL-WQPC model, a benchmark water quality dataset obtained from the Kaggle repository is utilised. The research that compared several models found that the AEODL-WQPC model had superior results to more recent state of the art methods.

Synergy of RHA and silica sand on physico-mechanical and tribological properties of waste plastic-reinforced thermoplastic composites as floor tiles.

The usage of waste for the development of sustainable building materials has received an increasing attention in socio-eco-environment spheres. The rice husk ash (RHA) produced during burning of rice husk and the ever-increasing plastic wastes are useless causing detrimental effects on the environment. This research supports the idea of sustainability and circular economy via utilization of waste to produce value-added products. This research explores the potential of waste plastics, RHA, and silica sand as thermoplastic composite materials. The different composite samples were prepared through waste plastics which includes low- and high-density polyethylene and polypropylene with incorporation of RHA and silica sand in proportions. The study investigates the effect of filler/polymer in 30/70, 20/80, and 10/90 (wt. %) on the workability of the developed composite materials. The workability of the composites was found to improve with filler reinforcement. The experimental results showed the maximum density of 1.676 g/cm3 and mechanical strength of 26.39, 4.89, and 3.25 MPa as compressive, flexural, and tensile strengths, respectively. The minimum percentage of water absorption was 0.052%. The wear tests resulted in a minimum abrasive and sliding wear rate of 0.03759 (cm3) and 0.00692 × 10-6 kg/m. The correlations between wear mechanisms and responses were morphologically analyzed. The developed composites verify the feasibility of RHA and plastics waste as a cost effective and environmentally competent product. The results and discussions provided a direction for the future research on sustainable polymeric composite materials.

Thermo-Optical Characterization of Therminol55 Based MXene-Al2O3 Hybridized Nanofluid and New Correlations for Thermal Properties.

The current research focuses on formulating a new class of Therminol55-based nanofluids that incorporates an MXene/Al2O3 nanocomposite as the new class of dispersant at three different concentrations of 0.05, 0.10, and 0.20 wt%. The optical and thermophysical properties of the formulated nanofluid are assessed experimentally. Zeta potential and FTIR analyses are employed to evaluate the composite particles' surface charge and chemical stability, respectively. Thermal conductivity is observed to increase with nanoparticle loading and maximally augmented by 61.8% for 0.20 wt%, whereas dynamic viscosity increased with adding nanoparticles but remarkably dropped with increasing temperature. In addition, the prepared TH55/MXene + Al2O3 samples are thermally stable up to 200 °C according to TGA analyses. Moreover, the proposed correlations for the thermal conductivity and viscosity showed good agreement with the experimental data. The study's findings suggest that the formulated nanofluid could be a viable contender to be used as a heat transfer fluid in the thermal sector.

Mechanical performance of sustainable high strength ductile fiber reinforced concrete (HSDFRC) with wooden ash.

The knowledge of sustainable development believes that natural resources should be treated limited, and waste must be managed rationally. This idea and the constant striving to reduce production costs make the use of waste materials potential substitutes for traditionally used raw materials. In cement concrete technology, there are many possibilities to use waste materials either as cement replacement or aggregate in concrete production. The basic aim of this research work is to study the impact of wooden ash (WA) as binding material in proportions 10%, 20%, and 30% by weight of cement on high strength ductile cementitious composite concrete. The fresh property was evaluated through the slump cone test, while the mechanical property was evaluated through compressive and split tensile strength test. Load deflection curve, ductility index, and maximum and minimum crack were also studied to find flexure cracking behaviors of reinforced cement concrete (RCC) beam. The durability of high-strength ductile concrete was studied through water absorption and acid attacks test. Pozzolanic activity of wooden ash was studied through XRD analysis.

Arithmetic optimization algorithm with deep learning enabled airborne particle-bound metals size prediction model.

Recently, heavy metal air pollution has received significant interest in computing the total concentration of every toxic metal. Chemical fractionation of possibly toxic substances in airborne particles becomes a vital element. Among the primary and secondary air pollutants, airborne particulate matter (APM) received considerable internet among research communities owing to the adversative impact on human health. Hence, size distribution details of airborne heavy metals are important in assessing the adverse health effects over the globe. Recently, deep learning models have gained significant interest over the mathematical and statistical prediction models. In this view, this paper presents a novel arithmetic optimization algorithm (AOA) with multi-head attention based bidirectional long short-term memory (MABLSTM) model for predicting the size fractionated airborne particle bound metals. The proposed AOA-MABLSTM technique focuses on the forecasting of the size-fractionated airborne particle bound matter. The presented model intends to examine the concentration of PM and distinct sized-fractionated APM. The proposed model establishes MABLSTM based accurate predictive approaches for atmospheric heavy 83 metals is used for determining temporal trend of heavy metal. The proposed model employs AOA based hyperparameter tuning process to optimally tune the hyperparameters included in the MABLSTM method. To demonstrate the improved outcomes of the AOA-MABLSTM approach, a comparison study is performed with recent models. The stimulation results emphasized the betterment of the presented model over the other methods. Aluminum metal had an RMSE of 73.200 for AOA-MABLSTM. On Cu metal, the AOA-MABLSTM approach had an RMSE of 6.747. On Zn metal, the AOA-MABLSTM system lowered the RMSE by 45.250.

Optimization of Single α-Phase for Promoting Ferromagnetic Properties of 44Fe-28Cr-22Co-3Mo-1Ti-2V Permanent Magnet with Varying Co Concentration for Energy Storage.

The thermal stability and structural, microstructural and magnetic properties of (40 + x) Fe-28Cr-(26 - x) Co-3Mo-1Ti-2V magnets with x = 0, 2, 4 addition in cobalt content were investigated and presented. The magnetic alloys were synthesized by vacuum arc melting and casting technique in a controlled argon atmosphere. Magnetic properties in the alloys were convinced by single-step isothermal field treatment and subsequent aging. The alloys were investigated for thermal stability, structural, microstructural and magnetic properties via differential thermal analysis (DTA), X-ray diffractometery (XRD), optical microscopy (OM), field emission scanning electron microscope (FESEM) and DC magnetometer. Metallurgical grains of size 300 ± 10 µm were produced in the specimens by casting and refined by subsequent thermal treatments. The magnetic properties of the alloys were achieved by refining the microstructure, the optimization of conventional thermomagnetic treatment to modified single-step isothermal field treatment and subsequent aging. The best magnetic properties achieved for the alloy 44Fe-28Cr-22Co-3Mo-0.9Ti-2V was coercivity Hc = 890 Oe (71 kA/m), Br = 8.43 kG (843 mT) and maximum energy product (BH)max = 3 MGOe (24 kJ/m3). The enhancement of remanence and coercivity enabled by the isothermal field treatment was due to the elongation of the ferromagnetic phase and step aging treatment due to the increase in the volume fraction. This work is interesting for spin-based electronics to be used for energy storage devices.

Energy Conversion Efficiency Enhancement of Polyethylene Glycol and a SiO2 Composite Doped with Ni, Co, Zn, and Sc Oxides.

Doping the SiO2 support with Co, Ni, Zn, and Sc improves the thermal conductivity of a hybrid PEG/SiO2 form-stable phase change material (PCM). Doping also improves the energy utilization efficiency and speeds up the charging and discharging rates. The thermal, chemical, and hydrothermal stability of the PEG/Zn-SiO2 and PEG/Sc-SiO2 hybrid materials is better than that of the other doped materials. The phase change enthalpy of PEG/Zn-SiO2 is 147.6 J/g lower than that of PEG/Sc-SiO2, while the thermal conductivity is 40% higher. The phase change enthalpy of 155.8 J/g of PEG/Sc-SiO2 PCM is very close to that of the parent PEG. PEG/Sc-SiO2 also demonstrates excellent thermal stability when subjected to 200 consecutive heating-cooling cycles and outstanding hydrothermal stability when examined under a stream at 120 °C for 2 h. The supercooling of the PEG/Sc-SiO2 system is the lowest among the tested materials. In addition, the developed PCM composite has a high energy storage capacity and high thermal energy storage/release rates.

Concrete with Partial Substitution of Waste Glass and Recycled Concrete Aggregate.

The current practice of concrete is thought to be unsuitable because it consumes large amounts of cement, sand, and aggregate, which causes depletion of natural resources. In this study, a step towards sustainable concrete was made by utilizing recycled concrete aggregate (RCA) as a coarse aggregate. However, researchers show that RCA causes a decrease in the performance of concrete due to porous nature. In this study, waste glass (WG) was used as a filler material that filled the voids between RCA to offset its negative impact on concrete performance. The substitution ratio of WG was 10, 20, or 30% by weight of cement, and RCA was 20, 40, and 60% by weight of coarse aggregate. The slump cone test was used to assess the fresh property, while compressive, split tensile, and punching strength were used to assess the mechanical performance. Test results indicated that the workability of concrete decreased with substitution of WG and RCA while mechanical performance improved up to a certain limit and then decreased due to lack of workability. Furthermore, a statical tool response surface methodology was used to predict various strength properties and optimization of RCA and WG.

Efficient Capture of Heavy Metal Ions and Arsenic with a CaY-Carbonate Layered Double-Hydroxide Nanosheet.

Nanosheets consisting of two-dimensional (2-D) nanomaterials made up of Ca2+ (Ca), and Y3+ (Y) cations and carbonate [CO3 2-] anions in the interlayer with a uniform thickness and lengths of around 10 µm have been successfully synthesized in a hydrotalcite layer structure, otherwise known as a layered double hydroxide, using a facile hydrothermal method. The resulting CaY-CO3 2- layered double-hydroxide (LDH) materials demonstrate outstanding affinity and selectivity for toxic transition metal ions such as Cr3+, Ni2+, Cu2+, Zn2+, Pb2+, Cd2+, and Hg2+ as well as metalloid As3+. The adsorption of all of the highly toxic metal ions from the aqueous solution was found to be exceptionally rapid and highly selective, with more than 95% removal achieved within 30 min. For AsO3, a strong adsorption potential of 452 mg/g was observed at pH 7.0, which is better than most values previously reported. The distribution coefficient K d values can exceed ∼106 mL/g for Cr3+, Pb2+, and As3+, which are highly toxic. The fabricated materials have excellent chemical stability: they retain their well-defined lamellar shapes even under mildly acidic conditions.

Numerical Study of Joule Heating Effects on Microfluidics Device Reliability in Electrode Based Devices.

In electrode-based microfluidic devices, micro channels having narrow cross sections generate undesirable temperature inside the microfluidic device causing strong thermal distribution (joule heating) that eventually leads to device damage or cell loss. In this work, we investigate the effects of joule heating due to different electrode configuration and found that, electrodes with triangular arrangements produce less heating effect even at applied potential of 30 V, without compromising the performance of the device and separation efficiency. However, certain electrode materials have low thermal gradients but erode the channel quickly thereby affecting the reliability of the device. Our simulation also predicts optimal medium conductivity (10 mS/m with 10 V) for cells to survive inside the channel until they are selectively isolated into the collection outlet. Our investigations will aid the researchers in the designing of efficient and reliable microfluidic devices to overcome joule heating inside the microchannels.

Electrical and Optical Properties of Indium and Lead Co-Doped Cd0.9Zn0.1Te.

We have investigated the electrical and optical properties of Cd0.9Zn0.1Te:(In,Pb) wafers obtained from the tip, middle, and tail of the same ingot grown by modified vertical Bridgman method using I-V measurement, Hall measurement, IR Transmittance, IR Microscopy and Photoluminescence (PL) spectroscopy. I-V results show that the resistivity of the tip, middle, and tail wafers are 1.8 × 1010, 1.21 × 109, and 1.2 × 1010 Ω·cm, respectively, reflecting native deep level defects dominating in tip and tail wafers for high resistivity compared to the middle part. Hall measurement shows the conductivity type changes from n at the tip to p at the tail in the growth direction. IR Transmittance for tail, middle, and tip is about 58.3%, 55.5%, and 54.1%, respectively. IR microscopy shows the density of Te/inclusions at tip, middle, and tail are 1 × 103, 6 × 102 and 15 × 103/cm2 respectively. Photoluminescence (PL) spectra reflect that neutral acceptor exciton (A0,X) and neutral donor exciton (D0,X) of tip and tail wafers have high intensity corresponding to their high resistivity compared to the middle wafer, which has resistivity a little lower. These types of materials have a large number of applications in radiation detection.