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The use of titanium dioxide (also known as titania) in solar cells, biomaterials, and photocatalytic processes has generated an ever-increasing amount of interest. Titania is widely used in a variety of applications because it is nanocrystalline, chemically stable, has a high refractive index, is mechanically hard, and transmits visible light well. In this work, titania nanoparticles were formed via sol–gel synthesis, wherein the effect of varying pH values such as 3, 5, 7, 9, and 11 has been investigated on the synthesized nanoparticles by studying their structural characteristics and analyzing their optical transmittance. Structural analysis of the synthesized TiO2 shows formation of the brookite phase at all pH values under as-synthesized conditions. Moreover, structural parameters including crystallite size, dislocation density, and unit cell volume were also calculated. Cell volume was calculated at various pH levels and measured between 255.7 and 257.2 Å3. Optical analysis shows a transmission increase with a pH increase, and maximum transmission has been observed above 80% for a pH value of 11.

Synthesis of a coconut oil-based biopolyol via sequential glycerolysis and amidation; and its subsequent use as a sole polyol for rigid poly(urethane-urea) hybrid foam production.

Silver nanoparticles were synthesized using locally purchased honey and silver nitrate solution. This method provides a simplistic and straightforward approach to the formation of silver nanoparticles. The silver nanoparticles with varying amounts of silver nitrate solution were characterized using ultraviolet-visible spectroscopy and Fourier transform infrared spectroscopy. In addition, dynamic light scattering characterization was used to determine the average size and size distribution of silver nanoparticles. Experimental results revealed that varying the amount of silver nitrate solution can control the size and absorption spectra of silver nanoparticles. A large amount of silver nitrate solution will exhibit a peak in the higher wavelength. The shifting of the absorption peaks at 401, 406, 407, 408, and 409 nm are believed to be related to the wavelength of the surface plasmon resonance. Moreover, a larger amount of silver nitrate solution also results in an increasing size with 27.2, 57.9, and 63.4 nm as revealed in the size distribution via dynamic light scattering. This green synthesis method of silver nanoparticles will provide a cost-effective production as an alternative to commercial antibacterial agents.

Silver nanoparticles have been well known to possess efficient antibacterial properties. Many studies conducted on silver nanoparticles synthesized in different routes, from physical methods to chemical techniques to biological synthesis. In this study, the biological route was utilized using Psidium guajava leaves extract mixed with silver nitrate to synthesize silver nanoparticles. In addition, syntheses were done at varying temperatures to investigate its effect over antibacterial properties of the silver nanoparticles obtained. Results showed blueshifting of UV-Vis peaks suggesting decrease in particle size as synthesis temperature increases. FTIR also showed that the synthesized nanoparticles may have been capped by phenolic compounds from the biomolecules in guava leaves. Finally, antibacterial tests via Disc Diffusion Test suggested that the silver nanoparticles biologically synthesized at higher temperatures are more effective bactericides than those synthesized at lower temperatures as indicated by the measured zones of inhibition.

Innovative technologies enable businesses to stay competitive in marketplace while also increasing profits in manufacturing of nanocomposites for water treatment. Aforementioned driving factors resulted in adoption of a number of innovative technologies, and no other trend has had a greater impact in recent years than Industry 4.0. Industry 4.0 is a comprehensive term that encompasses data management, manufacturing competitiveness, manufacturing processes, and efficiency. The term “Industry 4.0” refers to a group of key enabler technologies, such as cyber physical models, Internet of Things (IoT), artificial intelligence (AI), and big data analytics, including embedded devices which are all significant components to the mechanized and digitized industrial environments. AI approaches have been used for water treatment processes as well as desalination in recent years for optimizing the process along with providing realistic answers to water scarcity and water pollution-related issues. AI applications have been used to predict and minimize water treatment process operational costs by lowering costs and optimizing chemical utilization. Several AI models are successful and accurate in predicting effectiveness of various adsorbents used in the removal process of a variety of contaminants from water. To identify the current level of research and future development prospects of smart manufacturing, this study uses a comprehensive literature review technique for manufacturing sustainability of nanocomposite fabrication for water treatment applications. The model provided will help to create a baseline for AI and hybrid models in the water treatment and wastewater management sectors, allowing for the increased performance and innovative growth. It will serve to provide the framework and give guidance for researchers interested in creating superior nanocomposites for waste and fresh water treatment and management using Industry 4.0. This study looks at a variety of AI approaches as well as how they may be used in water treatment, with an emphasis on pollutant adsorption. This assessment also identified certain obstacles and research gaps in the field of AI applications in water treatment processes.

In today’s world of electronics, nanomaterial applications pose a challenge. The spin coating approach was used to create nanostructured ZnO with wurtzite structure in a recent study. Antimony doping, aluminum, and antimony codoping with 2.0 percent were used to make these films. The impact of doped and codoped films on structural, optical properties, and morphological has been examined using a variety of characterization approaches. A ZnO nanocrystal with a diameter of 20-30 nm was discovered using XRD (X-ray diffraction). According to SEM (scanning electron microscope) scans, the grain size is in the 80-120 nm region. The use of Fourier transform infrared spectroscopy (FTIR) to detect elemental elements was studied, and the peak at 400-520 cm-1 was identified as ZnO. The optical properties of doped and codoped ZnO were checked, and it was discovered that antimony-doped ZnO has a larger band gap than Al and antimony-codoped ZnO. This proved that ZnO may be used in gas sensors and solar cells. The gas response of a static gas sensor system based on Sb-doped films was measured and compared to Al- and Sb-codoped films in the presence of ethanol vapor.

Water contamination is a significant issue in the modern day, caused by the textile dying business, and it has a detrimental impact on living organisms. We report on the manufacture of gold-doped ZnO nanospheres using a simple heat treatment approach and the use of ZnO nanoparticles as photocatalysts for the degradation of methyl orange dye. To increase this degrading activity, Au was utilised as a modifier, and their temperature quenching effect was noticed. One of the most efficient electron grabbers in the conduction band is Au ion. The novelty of this recent research is that it has found that anatase to rutile phase transformation is promoted, and the highest transformation was achieved by using 1.0% of Au, which proves Au-doped ZnO-based nanoparticles are best for this degradation of dyes. The structural, morphological, optical, electrical, and photocatalytic characteristics of the synthesised nanocatalysts were determined. These nanoparticles have a grain size of 45-75 nm. Photocatalytic activity was investigated using UV-Vis spectra, and a significant absorption peak of about 482 nm was discovered. With increasing frequency, the dielectric constant and frequency of the produced nanoparticles drop. The kinetic analysis yields a rate constant of 0.0165 min-1 for nanosphere-like particles. At a concentration of 1% Au, the produced nanoparticles degrade the dye completely in 150 minutes when exposed to UV light.

In researching social network data and depression, it is often necessary to manually label depressed and non-depressed users, which is time-consuming and labor-intensive. The aim of this study is that it explores the relationship between social network data and depression. It can also contribute to detecting and identifying depression. Through collecting and analyzing college students’ microblog social data, a preliminary screening algorithm for college students’ suspected depression microblogs based on depression keywords, and semantic expansion is researched; a comprehensive lexical grammar was proposed. This research provided has a preliminary screening method based on depression keywords and semantic expansion for college students’ suspected depression microblogs, with a screening accuracy. This method forms a depression keyword table by constructing the basic keyword table and the semantic expansion based on the word embedding learning model Word2Vec. Finally, the word table is used to calculate the semantic similarity of the tested microblogs and then identify whether it is a suspected depression microblog. The experimental results on the microblog dataset of college students show that the comprehensive lexical method is better than the SDS questionnaire segmentation method and the expert lexical method in terms of screening accuracy; the comprehensive lexical approach can quickly and automatically screen out a tiny proportion of suspected doubts from a large number of college students’ microblogs. Depression Weibo can reduce the workload of experts’ annotation, improve annotation efficiency, and provide a suitable data processing basis for the subsequent accurate identification (classification problem) of patients with depression.

A straightforward approach to recycle waste expanded polystyrene (EPS) foam to produce polystyrene (PS) microfibers using the improvised centrifugal spinning technique is demonstrated in this work. A typical benchtop centrifuge was improvised and used as a centrifugal spinning device. The obtained PS microfibers were characterized for their potential application for oil adsorption. Fourier transform infrared spectroscopy results revealed similarity on the transmission bands of EPS foam and PS microfibers suggesting the preservation of the EPS foam’s chemical composition after the centrifugal spinning process. Scanning electron microscopy displayed well-defined fibers with an average diameter of 3.14 ± 0.59 μm. At the same time, energy dispersive X-ray spectroscopy revealed the presence of carbon and oxygen as the primary components of the fibers. Contact angle (θCA) measurements showed the more enhanced hydrophobicity of the PS microfiber (θCA = 100.2 ± 1.3°) compared to the untreated EPS foam (θCA = 92.9 ± 3.5°). The PS microfiber also displayed better oleophilicity compared to EPS foam. Finally, the fabricated PS microfibers demonstrated promising potential for oil removal in water with a calculated sorption capacity value of about 15.5 g/g even at a very short contact time. The fabricated PS fiber from the waste EPS foam may provide valuable insights into the valorization of polymeric waste materials for environmental and other related applications.

The utilization of vegetable oil in producing bio-based polyol, as an alternative replacement to petroleum-based polyol in making polyurethane (PU) foam has gained a lot of interest due to its finite supply and low production cost. In this study, bio-based polyol using coconut oil as raw material produced PU foam as thermal insulation material. The vegetable oil-based polyol was prepared using a two-step method, while PU foams were prepared by the free-rise method. In order to enhance the thermal properties of the produce PU foams, phase change material (PCM) was added to the PU foam formulation. FTIR spectra result showed peaks at 2920 cm-1 and 2850-1, which signifies the CH2 asymmetric stretching, indicating that n-octadecane was successfully incorporated into PU foams. Moreover, heat flow meter (HFM) and thermo-gravimetric analysis (TGA) show PU foam with 1% n-octadecane shows better thermal properties than other produced PU foams. Furthermore, the universal testing machine (UTM) result shows an enhancement in the mechanical properties of the produced PU foam. These results demonstrate that the addition of n-octadecane to the PU foam formulation improved the mechanical properties of PU foams while enhancing their thermal properties.

Organic dyes used in the food and textile industries are the primary sources of environmental contamination due to their high toxicity and nonbiodegradability. This paper describes the synthesis of cellulose nanocrystals/zinc oxide (CNC/ZnO) nanocomposite via the sol-gel method. Various characterization techniques such as FTIR spectroscopy, UV-Vis spectroscopy, and FESEM-EDX analysis were done. FTIR and UV-Vis analyses initially confirmed the formation of CNC/ZnO nanocomposites. FESEM-EDX showed a fiber-like structure with agglomerated particles on the CNC-ZnO image, suggesting the functionalization of ZnO nanoparticles onto the CNC. The photocatalytic potential of the CNC/ZnO nanocomposite was then evaluated by degrading 10 ppm thiazine dye (methylene blue) solution. The solution was irradiated with UV and visible light at an ambient temperature. The degradation was monitored at different time intervals using a UV spectrophotometer to measure the absorbance value intermittently. Results on the photocatalytic activity indicated that the synthesized CNC/ZnO nanocomposite showed faster degradation under UV light irradiation than the visible light, with an efficiency of 96.11% and 85.60%, respectively, after 180 mins of light irradiation. Further, the results suggest that the synthesized CNC/ZnO nanocomposite showed great promise as a sustainable material for the degradation of organic contaminants in an aqueous solution.

This work reports the influence of precursor concentration on the thermal and optical properties of magnetic iron oxide nanoparticles (MIONPs) synthesized via the green synthesis route. A brown seaweed (Sargassum crassifolium) extract was used to mediate the reduction of iron precursors. Ultraviolet–visible spectra showed a sharp peak between 315 and 375 nm, which corresponds to the absorbance of MIONPs. This peak intensified with an increasing concentration of iron precursors. The Fourier transform infrared spectra revealed the presence of the transmittance bands around 557 cm−1 and 565 cm−1, which corresponds to Fe-O-Fe vibrational modes suggesting the successful formation of iron oxide nanoparticles. The dynamic light scattering technique revealed that the average hydrodynamic size of the MIONPs was 31.96 ± 15.61 nm and 147.40 ± 28.48 nm. Atomic force micrograph and scanning electron micrograph both showed quasi-spherical nanostructures with sizes ranging from 40 to 215 nm. Furthermore, X-ray diffraction and thermogravimetric analyses revealed the crystallinity and thermal stability of the synthesized MIONPs, respectively. This investigation on the effect of precursor concentration provides a valuable insight for the green- mediated synthesis of MIONPs that is vital for various environmental, biomedical, and industrial application.

The influence of cellulose fibers extracted from pineapple (Ananas comosus) leaf fibers on polyurethane foam’s mechanical properties (PUF) is reported. The PUFs were reinforced with different weight ratios (1, 3, and 5 wt%) of cellulose fibers. The surface morphology of the extracted cellulose fibers was determined using a scanning electron microscope (SEM). The functional groups and vibrational modes of the reinforced PUFs were measured using Fourier transform infrared spectroscopy (FTIR). The influence of cellulose fibers to the density and mechanical property of the PUFs were also investigated using universal testing Machine (UTM). Experimental results showed the absence of hemicellulose, lignin, and other soluble materials present in the pineapple leaves after alkaline treatment, suggesting high-quality cellulose fibers. Likewise, FTIR spectra revealed the presence of typical urethane linkages of PUFs suggesting the successful formation of PUFs with a varied amount of cellulose fibers. The mechanical properties of the PUFs with an increasing amount of cellulose fibers exhibit a decrease of the compressive strength of the fabricated PUFs may be due to the lack of cross-linking between the cellulose fibers and PUFs that will influence the reactivity in the system that eventually affects the polyurethane foam density expansion.

Cyanide ( CN - ) is a deleterious chemical and can cause serious health effects upon exposure even at low concentrations. Different sensors have been developed to monitor cyanide in water, which, however, suffers from complex mechanisms and high-cost. Curcuma longa or turmeric is a commercially available natural dye (usually in powder form) used as medicine. The phenolic substance curcumin is responsible for its yellow color and is capable of sensing environmentally significant analytes such as CN - . In this study, a colorimetric sensor for cyanide in aqueous solution was fabricated by the immobilization of crude curcumin on ordinary filter paper. The curcumin was extracted from locally available turmeric powder using a simple extraction method. Its successful integration in the filter paper was validated by FT-IR spectroscopy. UV–Vis analysis showed that the curcumin-based paper sensor was sensitive to CN - (down to 5.0 ppm), producing a visual change from yellow to dark red. The fabricated sensor also demonstrated high selectivity towards CN - in the presence of N a + , K + , and F - . Its response time was found to be inversely proportional to the concentration of the CN - in the solution. Assessment of reusability revealed that the sensor was reusable for up 8 cycles before significant changes in colorimetric response were observed. Importantly, the fabricated sensor exhibited applicability for actual cyanide monitoring in real water samples.

Increasing anthropogenic and industrial activities lead to the contamination of aquatic resources with organic pollutants such as dyes. In this work, a magnetic composite photocatalyst composed of Fe3O4, BiOCl, and Cu2O was fabricated for the degradation of organic dye pollutants. The composite was synthesized via a simple co-precipitation method. FT-IR and SEM-EDX analyses confirmed the formation of Fe3O4/BiOCI/Cu2O (FBC) composite. Photocatalytic degradation was tested under UV, and visible light using model organic dyes, methyl orange (MO), and methylene blue (MB), and was monitored using UV–Vis spectroscopy. The absorbance was found to decrease corresponding to the reduction of dye concentration during photocatalytic degradation. A plot of absorbance versus irradiation time for both model dyes exhibited exponential decay; faster degradation was observed with MO than with MB. Furthermore, the degradation of both dyes using the FBC photocatalyst was more efficient under UV light.

Protein is the material foundation of living things, and it directly takes part in and runs the process of living things itself. Predicting protein complexes helps us understand the structure and function of complexes, and it is an important foundation for studying how cells work. Genome-wide protein interaction (PPI) data is growing as high-throughput experiments become more common. The aim of this research is that it provides a dual-tree complex wavelet transform which is used to find out about the structure of proteins. It also identifies the secondary structure of protein network. Many computer-based methods for predicting protein complexes have also been developed in the field. Identifying the secondary structure of a protein is very important when you are studying protein characteristics and properties. This is how the protein sequence is added to the distance matrix. The scope of this research is that it can confidently predict certain protein complexes rapidly, which compensates for shortcomings in biological research. The three-dimensional coordinates of C atom are used to do this. According to the texture information in the distance matrix, the matrix is broken down into four levels by the double-tree complex wavelet transform because it has four levels. The subband energy and standard deviation in different directions are taken, and then, the two-dimensional feature vector is used to show the secondary structure features of the protein in a way that is easy to understand. Then, the KNN and SVM classifiers are used to classify the features that were found. Experiments show that a new feature called a dual-tree complex wavelet can improve the texture granularity and directionality of the traditional feature extraction method, which is called secondary structure.

Aiming at the inadequacy of the group decision-making method with the current attribute value as interval language information, an interval binary semantic decision-making method is proposed, which considers the decision maker’s psychological behavior. The scope of this research is that this paper is based on localized amplification method. The localized amplification method used in this research may amplify physiological movement after removing unwanted noise, allowing the movement trend to be seen with the naked eye, improving the CNN network’s mental identification accuracy. These two algorithms analyze the input picture from various perspectives, allowing the CNN network to extract more information and enhance identification accuracy. A new distance formula with interval binary semantics closer to decision-makers thinking habits is defined; time degree is introduced. An optimization model is established to solve the time series weights by considering the comprehensive consistency of expert evaluation. Based on prospect theory, a prospect deviation value is constructed and minimized weight optimization model, using the interactive multiple attribute decision community making (TODIM) method based on the new distance measure to calculate the total overall dominance of the schemes to rank the schemes. Taking the selection and evaluation of supply chain collaboration partners as an example, the effectiveness and rationality of the proposed method are verified.

The mechanical behavior of the rockfill materials (RFMs) used in a dam’s shell must be evaluated for the safe and cost-effective design of embankment dams. However, the characterization of RFMs with specific reference to shear strength is challenging and costly, as the materials may contain particles larger than 500 mm in diameter. This study explores the potential of various kernel function-based Gaussian process regression (GPR) models to predict the shear strength of RFMs. A total of 165 datasets compiled from the literature were selected to train and test the proposed models. Comparing the developed models based on the GPR method shows that the superlative model was the Pearson universal kernel (PUK) model with an R-squared (R2) of 0.9806, a correlation coefficient (r) of 0.9903, a mean absolute error (MAE) of 0.0646 MPa, a root mean square error (RMSE) of 0.0965 MPa, a relative absolute error (RAE) of 13.0776%, and a root relative squared error (RRSE) of 14.6311% in the training phase, while it performed equally well in the testing phase, with R2 = 0.9455, r = 0.9724, MAE = 0.1048 MPa, RMSE = 0.1443 MPa, RAE = 21.8554%, and RRSE = 23.6865%. The prediction results of the GPR-PUK model are found to be more accurate and are in good agreement with the actual shear strength of RFMs, thus verifying the feasibility and effectiveness of the model.

PCM (phase-change memory) is a memory innovation that has gained prominence as a capacity-class memory for computer systems. It is made up of a tiny functional amount of phase-change material that is located in the middle of two electrodes. In PCM, data is kept by utilizing the difference in electrical resistance between a crystalline phase, which has a high resistance, and its amorphous phase, which has a low resistance. Using electrical pulses, the phase-change material would be shifted from a high to the low conductive region and conversely. However, the device’s material science concerned with PCM has been generally studied, and questions remain regarding their electrical, warm, and fundamental aspects since its publication in the 1960s. One major downside of PCM is its low heat conductivity, which causes delays in the energy charging/discharging procedure and hardware efficiency. As a result of this, one of the primary focuses of PCM studies has been the improvement of PCM’s heat conductivity through the utilization of nanotechnology and nanomaterials. Nanotechnology has been developing ultrasmall nanoparticles to improve traditional PCMs’ thermophysical characteristics. These nanomaterials, such as metal, metal oxide, and carbon, will significantly boost PCM’s thermal properties, including supercooling, viscosity, and heat capacity. An overview of PCM devices is presented in this article, which underlies perusing and composition activities. Consequently, we offered novel PCM devices and materials. Therefore, the total study is qualitative, and no machine learning approach is used. Because of this, we cannot say that the data is quantitative. Our work includes both test representations of the specific features observed in nanoscale PCM devices and material science demonstrations. In the end, we provide a point of view on some remaining open inquiries and possible future exploration directions.

This paper reports on the synthesis and application of Fe3O4/TiO2 nanocomposite. In situ attachment of TiO2 coating to the surface of the magnetic nanoparticles (Fe3O4) was attained by direct condensation of titanium precursors. Characterization result suggests that the average particle size of the synthesized nanocomposite is 10-15 nm. Also, FT-IR result confirms the presence of TiO2 layer in the surface of the magnetic nanoparticles. Furthermore, the prepared Fe3O4/TiO2 nanocomposite was utilized as an active magnetic nanophotocatalyst for the degradation of cyanide. Results show that even at 5.0 mg of Fe3O4/TiO2 photocatalyst, higher cyanide removal efficiency (91%) was obtained when 60 ppm CN- was incubated with the photocatalyst for 30 minutes. Likewise, it has been demonstrated that the synthesized magnetic nanophotocatalyst can be used to degrade cyanide using sunlight as the natural light source. A 94% cyanide removal efficiency was obtained when the sample was incubated with the synthesized magnetic nanophotocatalyst for 120 minutes under sunlight irradiation. Importantly, the prepared magnetic photocatalyst can be re-used several times (up to 5 cycles) without significant changes in the cyanide removal efficiency.

We report on the growth of zinc oxide/polyaniline (ZnO/PANI) nanocomposites deposited on platinum (Pt)–coated glass substrate via chemical bath deposition and dip–casting technique is reported. Scanning electron micrographs of the nanocomposites revealed that etching of ZnO nanorods takes place during growth which turn into plate–like and distorted nanostructures. We found out that increasing the concentration of NH4OH triggered increase in nanostructure diameter. The surface morphology of nanocomposites significantly changed as the molar concentration of NH4OH precursor varies. Fast fourier transform infrared spectroscopy results showed the interaction of ZnO and PANI by observing the shift of peaks to the higher wavenumbers. The measured optical band gap of the nanocomposites are in good aggreement with the reported values. This result indicates that the grown ZnO/PANI nanocomposites is a good material for solar cell device.

We report on antibacterial activities of Zinc oxide (ZnO) with different structures. Fast furrier transform infrared spectroscopy ZnO nanostructures showed peaks in the range between 450–600 cm-1 indicating the successful growth through the presence of Zn-O stretching. On the other hand, impurities such as zinc complexes might be present due to the appearance of peaks at 1110 cm-1, 1390 cm-1 and 1506 cm-1. Furthermore, SEM images revealed that nanorods and sea-urchin like nanostructures are present in the produced ZnO nanostructures. Nanorods exhibit a better antibacterial response than the sea-urchin like structure. The change in structural morphology along with its purity has greatly influenced the area of bacterial inhibition zone during antibacterial testing.