Nanofiltration PVP Membrane with Gradient Cross-Linking for Efficient Polycyclic Aromatic Hydrocarbons Removal.

Polycyclic aromatic hydrocarbons (PAHs) are persistent organic pollutants that may contaminate various water sources and pose serious dangers to human health and the environment. Due to their capacity for size-based separation, nanofiltration membranes have become efficient instruments for PAH removal. However, issues such as membrane fouling and ineffective rejection still exist. To improve PAH rejection while reducing fouling problems, this work created a new gradient cross-linking poly(vinylpyrrolidone) (PVP) nanofiltration membrane. The gradient cross-linking technique enhanced the rejection performance and antifouling characteristics of the membrane. The results demonstrated that the highest membrane flow was achieved at a 0.15% SDS-PVP membrane. There is a trade-off between membrane flux and salt rejection since salt rejection increases with SDS owing to the growth of big pores. The membrane flux was reduced for the 0.25% SDS-PVP membrane owing to poor SDS dispersion. The prepared membrane showed enhanced removal efficiencies for the removal of the PAH compounds. The PVP membrane has the potential to be used in several water treatment applications, improving water quality, and preserving the environment.

Ensemble learning approach for advanced metering infrastructure in future smart grids.

Typically, load forecasting models are trained in an offline setting and then used to generate predictions in an online setting. However, this approach, known as batch learning, is limited in its ability to integrate new load information that becomes available in real-time. On the other hand, online learning methods enable load forecasting models to adapt efficiently to new incoming data. Electricity Load and Price Forecasting (ELPF) is critical to maintaining energy grid stability in smart grids. Existing forecasting methods cannot handle the available large amount of data, which are limited by different issues like non-linearity, un-adjusted high variance and high dimensions. A compact and improved algorithm is needed to synchronize with the diverse procedure in ELPF. Our model ELPF framework comprises high/low consumer data separation, handling missing and unstandardized data and preprocessing method, which includes selecting relevant features and removing redundant features. Finally, it implements the ELPF using an improved method Residual Network (ResNet-152) and the machine-improved Support Vector Machine (SVM) based forecasting engine to forecast the ELP accurately. We proposed two main distinct mechanisms, regularization, base learner selection and hyperparameter tuning, to improve the performance of the existing version of ResNet-152 and SVM. Furthermore, it reduces the time complexity and the overfitting model issue to handle more complex consumer data. Furthermore, numerous structures of ResNet-152 and SVM are also explored to improve the regularization function, base learners and compatible selection of the parameter values with respect to fitting capabilities for the final forecasting. Simulated results from the real-world load and price data confirm that the proposed method outperforms 8% of the existing schemes in performance measures and can also be used in industry-based applications.

Characteristics Analysis of Plasticized Polyvinyl Chloride Gel-Based Microlens at Different Temperatures.

Temperature plays a crucial role in the preparation of polyvinyl chloride (PVC) gels for optical applications. Incorrect temperature selection can lead to various issues such as poor surface roughness, inadequate light transmission, and insufficient solution for optical devices. To address this challenge, this study focuses on the preparation of PVC gel samples by combining PVC powder (n = 3000), eco-friendly dibutyl adipate, and tetrahydrofuran at different stirring temperatures ranging from 40 to 70 °C. The PVC gel preparation process is categorized into four groups (T40, T50, T60, and T70) based on the mixing temperatures, employing a controlled test method with specific temperature conditions. The prepared PVC gel samples are then subjected to analysis to evaluate various properties including surface morphology, tensile strength, light transmittance, and electrical response time. Among the samples, the PVC gel prepared at 60 °C (referred to as T60) exhibits excellent optical properties, with a transmittance of 91.2% and a tensile strength of 2.07 MPa. These results indicate that 60 °C is an optimal reaction temperature. Notably, the PVC gel microlenses produced at this temperature achieve their maximum focal length (ranging from -8 to -20 mm) within approximately 60 s, and they recover their initial state within around 80 s after the power is switched off. This focal length achievement is twice as fast as reported in previous studies on microlenses. It is observed that the reaction temperature significantly influences the solubility of the resin-based raw materials and the homogeneity of the gel. Consequently, these findings open up possibilities for utilizing PVC gel microlenses in novel commercial optics applications, thanks to their desirable properties.

Testing of Sr-Doped ZnO/CNT Photocatalysts for Hydrogen Evolution from Water Splitting under Atmospheric Dielectric Barrier Plasma Exposure.

Nonthermal plasma is a well-recognized environmentally advantageous method for producing green fuels. This work used different photocatalysts, including PZO, SxZO, and SxZCx for hydrogen production using an atmospheric argon coaxial dielectric barrier discharge (DBD)-based light source. The photocatalysts were produced using a sol-gel route. The DBD discharge column was filled with water, methanol, and the catalyst to run the reaction under argon plasma. The DBD reactor was operated with a 10 kV AC source to sustain plasma for water splitting. The light absorption study of the tested catalysts revealed a decrease in the band gap with an increase in the concentration of Sr and carbon nanotubes (CNTs) in the Sr/ZnO/CNTs series. The photocatalyst S25ZC2 demonstrated the lowest photoluminescence (PL) intensity, implying the most quenched recombination of charge carriers. The highest H2 evolution rate of 2760 µmol h-1 g-1 was possible with the S25ZC2 catalyst, and the lowest evolution rate of 56 µmol h-1 g-1 was observed with the PZO catalyst. The photocatalytic activity of S25ZC2 was initially high, which decreased slightly over time due to the deactivation of the photocatalyst. The photocatalytic activity decreased from 2760 to 1670 µmol h-1 g-1 at the end of the process.

Response Surface Methodology for the Synthesis and Characterization of Bio-Oil Extracted from Biomass Waste and Upgradation Using the Rice Husk Ash Catalyst.

Rice husk ash (RHA), a low-cost biomaterial, was utilized to form bio-oil from pyrolysis in a batch-stirred reactor, followed by its upgradation using the RHA catalyst. In the present study, the effect of temperature (ranging from 400 to 480 °C) on bio-oil production produced from RHA was studied to obtain the maximum bio-oil yield. Response surface methodology (RSM) was applied to investigate the effect of operational parameters (temperature, heating rate, and particle size) on the bio-oil yield. The results showed that a maximum bio-oil output of 20.33% was obtained at 480 °C temperature, 80 °C/min heating rate, and 200 µm particle size. Temperature and heating rate positively impact the bio-oil yield, while particle size has little effect. The overall R2 value of 0.9614 for the proposed model proved in good agreement with the experimental data. The physical properties of raw bio-oil were determined, and 1030 kg/m3 density, 12 MJ/kg calorific value, 1.40 cSt viscosity, 3 pH, and 72 mg KOH/g acid value were obtained, respectively. To enhance the characteristics of the bio-oil, upgradation was performed using the RHA catalyst through the esterification process. The upgraded bio-oil stemmed from a density of 0.98 g/cm3, an acid value of 58 mg of KOH/g, a calorific value of 16 MJ/kg, and a viscosity 10.5 cSt, respectively. The physical properties, GC-MS and FTIR, showed an improvement in the bio-oil characterization. The findings of this study indicate that RHA can be used as an alternative source for bio-oil production to create a more sustainable and cleaner environment.

Analysis of the Performance of a Gel Actuator Made of Plasticized Polyvinyl Chloride/Carboxylated Cellulose Nanocrystals.

Recently, polyvinyl chloride (PVC) gel materials appeared promising for developing actuators, artificial muscles, and sensors. However, their energized response time and recovery limitations restrict their broader applications. Herein, a novel soft composite gel was prepared by mixing functionalized carboxylated cellulose nanocrystals (CCNs) and plasticized PVC. The surface morphology of the plasticized PVC/CCNs composite gel was characterized by scanning electronic microscopy (SEM). The prepared PVC/CCNs gel composites have increased polarity and electrical actuation with a fast response time. Experimental results demonstrated good response characteristics within the actuator model with a multilayer electrode structure when stimulated with a specified DC voltage (1000 V), with deformation of approximately 36.7%. Moreover, this PVC/CCNs gel has excellent tensile elongation, and the elongation at break of the PVC/CCNs gel is greater than the elongation at break of the pure PVC gel under the same thickness conditions. However, these PVC/CCNs composite gels showed excellent properties and development potential and are directed for broad applications in actuators, soft-robotics, and biomedical applications.

Analyzing Distributed Vibrating Sensing Technologies in Optical Meshes.

Hundreds of kilometers of optical fibers are installed for optical meshes (OMs) to transmit data over long distances. The visualization of these deployed optical fibers is a highlighted issue because the conventional procedure can only measure the optical losses. Thus, this paper presents distributed vibration sensing (DVS) estimation mechanisms to visualize the optical fiber behavior installed for OMs which is not possible by conventional measurements. The proposed technique will detect the power of light inside the optical fiber, as well as different physical parameters such as the phase of transmitted light inside the thread, the frequency of vibration, and optical losses. The applicability of optical frequency domain reflectometry (OFDR) and optical time-domain reflectometry (OTDR) DVS techniques are validated theoretically for various state detection procedures in optical fibers. The simulation model is investigated in terms of elapsed time, the spectrum of a light signal, frequency, and the impact of many external physical accidents with optical fibers.