Dopamine-Functionalized Gold Nanoparticles for Colorimetric Detection of Histamine.

Histamine, a primary biogenic amine (BA) generated through the decarboxylation of amino acids, concentration increases in protein-rich foods during deterioration. Thus, its detection plays a crucial role in ensuring food safety and quality. This study introduces an innovative approach involving the direct integration of dopamine onto gold nanoparticles (DCt-AuNP), aiming at rapid histamine colorimetric detection. Transmission electron microscopy revealed the aggregation of uniformly distributed spherical DCt-AuNPs with 12.02 ± 2.53 nm sizes upon the addition of histamine to DCt-AuNP solution. The Fourier-transform infrared (FTIR) spectra demonstrated the disappearance of the dicarboxy acetone peak at 1710 cm-1 along with the formation of well-defined peaks at 1585 cm-1, and 1396 cm-1 associated with the N-H bending modes and the aromatic C=C bond stretching vibration in histamine molecule, respectively, confirming the ligand exchange and interactions of histamine on the surface of DCt-AuNPs. The UV-vis spectra of the DCt-AuNP solution exhibited a red shift and a reduction in surface plasmon resonance (SPR) peak intensity at 518 nm along with the emergence of the 650 nm peak, signifying aggregation DCt-AuNPs with increasing histamine concentration. Notably, color transitions from wine-red to deep blue were observed in the DCt-AuNP solution in response to histamine, providing a reliable colorimetric signal. Dynamic Light Scattering (DLS) characterization showed a significant increase in the hydrodynamic diameter, from ∼15 to ∼1690 nm, confirming the interparticle cross-linking of DCt-AuNPs in the presence of histamine. This newly developed DCt-AuNP sensor provides colorimetric results in less than a minute that exhibits a remarkable naked-eye histamine detection threshold of 1.57 µM and a calculated detection limit of 0.426 µM, making it a promising tool for the rapid and sensitive detection of histamine.

Development of High-Performance Coconut Oil-Based Rigid Polyurethane-Urea Foam: A Novel Sequential Amidation and Prepolymerization Process.

The utilization of coconut diethanolamide (p-CDEA) as a substitute polyol for petroleum-based polyol in fully biobased rigid polyurethane-urea foam (RPUAF) faces challenges due to its short chain and limited cross-linking capability. This leads to compromised cell wall resistance during foam expansion, resulting in significant ruptured cells and adverse effects on mechanical and thermal properties. To address this, a novel sequential amidation-prepolymerization route was employed on coconut oil, yielding a hydroxyl-terminated poly(urethane-urea) prepolymer polyol (COPUAP). Compared to p-CDEA, COPUAP exhibited a decreased hydroxyl value (496.3-473.2 mg KOH/g), an increase in amine value (13.464-24.561 mg KOH/g), and an increase in viscosity (472.4-755.8 mPa·s), indicating enhanced functionality of 34.3 mgKOH/g and chain lengthening. Further, COPUAP was utilized as the sole B-side polyol in the production of RPUAF (PU-COPUAP). The improved functionality of COPUAP and its improved cross-linking capability during foaming have significantly improved cell morphology, resulting in a remarkable 4.7-fold increase in compressive strength (132-628 kPa), a 3.5-fold increase in flexural strength (232-828 kPa), and improved insulation properties with a notable decrease in thermal conductivity (48.02-34.52 mW/m·K) compared to PU-CDEA in the literature. Additionally, PU-COPUAP exhibited a 16.5% increase in the water contact angle (114.93° to 133.87°), attributing to the formation of hydrophobic biuret segments and a tightly packed, highly cross-linked structure inhibiting water penetration. This innovative approach sets a new benchmark for fully biobased rigid foam production, delivering high load-bearing capacity, exceptional insulation, and significantly improved hydrophobicity.

Flexible Polyurethane Foams Modified with Novel Coconut Monoglycerides-Based Polyester Polyols.

Coconut oil, a low-molecular-weight vegetable oil, is virtually unutilized as a polyol material for flexible polyurethane foam (FPUF) production due to the high-molecular-weight polyol requirement of FPUFs. The saturated chemistry of coconut oil also limits its compatibility with widely used polyol-forming processes, which mostly rely on the unsaturation of vegetable oil for functionalization. Existing studies have only exploited this resource in producing low-molecular-weight polyols for rigid foam synthesis. In this present work, high-molecular-weight polyester polyols were synthesized from coconut monoglycerides (CMG), a coproduct of fatty acid production from coconut oil, via polycondensation at different mass ratios of CMG with 1:5 glycerol:phthalic anhydride. Characterization of the CMG-based polyol (CMGPOL) products showed number-average molecular weights between 1997 and 4275 g/mol, OH numbers between 77 and 142 mg KOH/g, average functionality between 4.8 and 5.8, acid numbers between 4.49 and 23.56 mg KOH/g, and viscosities between 1.27 and 89.57 Pa·s. The polyols were used to synthesize the CMGPOL-modified PU foams (CPFs) at 20 wt % loading. The modification of the foam formulation increased the monodentate and bidentate urea groups, shown using Fourier transform infrared (FTIR) spectroscopy, that promoted microphase separation in the foam matrix, confirmed using atomic force microscopy (AFM) and differential scanning calorimetry (DSC). The implications of the structural change to foam morphology and open cell content were investigated using a scanning electron microscope (SEM) and gas pycnometer. The density of the CPFs decreased, while a significant improvement in their tensile and compressive properties was observed. Also, the CPFs exhibited different resiliency with a correlation to microphase separation. These findings offer a new sustainable polyol raw material that can be used to modify petroleum-based foam and produce flexible foams with varying properties that can be tailored to meet specific requirements.

Producing polyglycerol polyester polyol for thermoplastic polyurethane application: A novel valorization of glycerol, a by-product of biodiesel production.

The production of biodiesel generates glycerol as a by-product that needs valorization. Glycerol, when converted to polyglycerol, is a potential polyol for bio-based thermoplastic polyurethane (TPU) production. In this study, a novel polyglycerol polyester polyol (PPP) was developed from refined glycerol and coconut oil-based polyester polyol. Glycerol was first converted to glycerol acetate and then polymerized with coconut oil-based polyester polyol (CPP) as secondary polyol and phthalic anhydride. The resulting PPP polymerized at 220 °C and OH:COOH molar ratio of 2.5 exhibited an OH number of <100 mg KOH·g sample-1, an acid number of <10 mg KOH·g sample-1, and a molecular weight (MW) of 3697 g mol-1 meeting the polyol requirement properties for TPU (Handlin et al., 2001; Parcheta et al., 2020) [1-2]. Fourier-transform infrared (FTIR) spectroscopic characterization determined that higher reaction temperatures increase the polymerization rate and decrease the OH and acid numbers. Further, higher OH:COOH molar ratios decrease the polymerization rate and acid number, and increase the OH number. Gel permeation chromatography determined the molecular weight of PPP and suggested two distinct molecular structures which differ only in the number of moles of CPP in the structure. A differential scanning calorimetric (DSC) experiment on a sample of PPP-based polyurethane revealed that it was able to melt and remelt after 3 heating cycles which demonstrates its thermoplastic ability. The novel PPP derived from the glycerol by-product of biodiesel industries can potentially replace petroleum-derived polyols for TPU production.

Production of Bio-Based Polyol from Coconut Fatty Acid Distillate (CFAD) and Crude Glycerol for Rigid Polyurethane Foam Applications.

This study propounds a sustainable alternative to petroleum-based polyurethane (PU) foams, aiming to curtail this nonrenewable resource's continued and uncontrolled use. Coconut fatty acid distillate (CFAD) and crude glycerol (CG), both wastes generated from vegetable oil processes, were utilized for bio-based polyol production for rigid PU foam application. The raw materials were subjected to catalyzed glycerolysis with alkaline-alcohol neutralization and bleaching. The resulting polyol possessed properties suitable for rigid foam application, with an average OH number of 215 mg KOH/g, an acid number of 7.2983 mg KOH/g, and a Gardner color value of 18. The polyol was used to prepare rigid PU foam, and its properties were determined using Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis/derivative thermogravimetric (TGA/DTA), and universal testing machine (UTM). Additionally, the cell foam morphology was investigated by scanning electron microscope (SEM), in which most of its structure revealed an open-celled network and quantified at 92.71% open-cell content using pycnometric testing. The PU foam thermal and mechanical analyses results showed an average compressive strength of 210.43 kPa, a thermal conductivity of 32.10 mW·m-1K-1, and a density of 44.65 kg·m-3. These properties showed its applicability as a type I structural sandwich panel core material, thus demonstrating the potential use of CFAD and CG in commercial polyol and PU foam production.

Coconut power: a sustainable approach for the removal of Cr6+ ions using a new coconut-based polyurethane foam/activated carbon composite in a fixed-bed column.

To attain efficient removal of hexavalent chromium (Cr6+) from aqueous solutions, a novel polyurethane foam-activated carbon (PUAC) adsorbent composite was developed. The composite material was synthesized by the binding of coconut shell-based activated carbon (AC) onto a coconut oil-based polyurethane matrix. To thoroughly characterize the physicochemical properties of the newly developed material, various analytical techniques including FTIR spectroscopy, SEM, XRD, BET, and TGA analyses were conducted. The removal efficiency of the PUAC composite in removing Cr6+ ions from aqueous solutions was evaluated through column experiments with the highest adsorption capacity of 28.41 mg g-1 while taking into account variables such as bed height, flow rate, initial Cr6+ ion concentration, and pH. Experimental data were fitted using Thomas, Yoon-Nelson, and Adams-Bohart models to predict the column profiles and the results demonstrate high breakthrough and exhaustion time dependence on these variables. Among the obtained R2 values of the models, a better fit was observed using the Thomas and Yoon-Nelson models, indicating their ability to effectively predict the adsorption of Cr6+ ions in a fixed bed column. Significantly, the exhausted adsorbent can be conveniently regenerated without any noteworthy loss of adsorption capability. Based on these findings, it can be concluded that this new PUAC composite material holds significant promise as a potent sorbent for wastewater treatment backed by its excellent performance, cost-effectiveness, biodegradability, and outstanding reusability.

Synthesis and Characterization of Ferric Vanadate Nanorods for Efficient Electrochemical Detection of Ascorbic Acid.

This study reports the synthesis of ferric vanadate (FeVO4) via a facile hydrothermal method, focusing on demonstrating its exceptional electrochemical (EC) properties on detecting low-density ascorbic acid (AA). The phase purity, crystallinity, structure, morphology, and chemical compositional properties were characterized by employing X-ray diffraction, energy-dispersive X-ray spectroscopy, scanning electron microscopy, Raman spectroscopy, and X-ray photoelectron spectroscopy techniques. EC impedance spectroscopy and cyclic voltammetry techniques were also adopted in order to assess the EC response of a FeVO4-modified glassy carbon electrode for sensing AA at room temperature. The AA concentration range adopted in this experiment is 0.1-0.3 mM at a working electric potential of -0.13 V. The result showed functional excellence of this material for the EC determination of AA with good stability and reproducibility, promising its potentiality in connection with relevant sensing applications.

Biosynthesizing Cassia fistula Extract-Mediated Silver Nanoparticles for MCF-7 Cell Lines Anti-Cancer Assay.

The unique consequence of green synthesis is that the mediator plant is able to release chemicals that are efficacious as reducing as well as stabilizing agents. In this work, the fruit pulp and leaf essences of Cassia fistula have been used to manufacture silver nanoparticles through the green synthesis technique. The sculpturing of nanoparticles was accomplished by utilizing the reduction phenomenon that ensued due to the reaction between plant essences and the precursor solution. These biosynthesized silver nanoparticles were examined, where we used scanning electron microscopy, UV-vis spectroscopy, and X-ray diffraction techniques as means to analyze the structure, optical properties, and crystalline behavior, respectively. The absorption spectra for fruit and leaf extracts obtained from the UV-vis analyses peaked at 401 and 397 nm, and these peaks imply the appearance of optical energy gaps of 2.12 and 2.58 eV, accompanying spherical shapes of particles with diameters in the ranges of 12-20 and 50-80 nm, respectively. These silver nanoparticles together with the adopted green technique have a vast array of applications, specifically in the biomedical realm. In particular, they are being used to treat several diseases and are manifested as strong anti-tumor agents to medicate MCF-7 breast cancer cell lines in order to minimize the cell growth rate depending on their concentrations.

Improving the efficiency of dye-sensitized solar cells based on rare-earth metal modified bismuth ferrites.

This study reports light energy harvesting characteristics of bismuth ferrite (BiFeO3) and BiFO3 doped with rare-earth metals such as neodymium (Nd), praseodymium (Pr), and gadolinium (Gd) dye solutions that were prepared by using the co-precipitation method. The structural, morphological, and optical properties of synthesized materials were studied, confirming that 5-50 nm sized synthesized particles have a well-developed and non-uniform grain size due to their amorphous nature. Moreover, the peaks of photoelectron emission for bare and doped BiFeO3 were observed in the visible region at around 490 nm, while the emission intensity of bare BiFeO3 was noticed to be lower than that of doped materials. Photoanodes were prepared with the paste of the synthesized sample and then assembled to make a solar cell. The natural and synthetic dye solutions of Mentha, Actinidia deliciosa, and green malachite, respectively, were prepared in which the photoanodes were immersed to analyze the photoconversion efficiency of the assembled dye-synthesized solar cells. The power conversion efficiency of fabricated DSSCs, which was confirmed from the I-V curve, is in the range from 0.84 to 2.15%. This study confirms that mint (Mentha) dye and Nd-doped BiFeO3 materials were found to be the most efficient sensitizer and photoanode materials among all the sensitizers and photoanodes tested.

A novel reaction mechanism for the synthesis of coconut oil-derived biopolyol for rigid poly(urethane-urea) hybrid foam application.

Coconut oil (CO) has become one of the most important renewable raw materials for polyol synthesis due to its abundance and low price. However, the saturated chemical structure of CO limits its capability for functionalization. In this study, a novel reaction mechanism via the sequential glycerolysis and amidation of CO triglycerides produced an amine-based polyol (p-CDEA). The synthesized biopolyol has a relatively higher hydroxyl value of 361 mg KOH per g relative to previously reported CO-based polyols with values ranging from 270-333 mg KOH per g. This primary hydroxyl-rich p-CDEA was used directly as a sole B-side polyol component in a polyurethane-forming reaction, without further purification. Results showed that a high-performance poly(urethane-urea) (PUA) hybrid foam was successfully produced. It has a compressive strength of 226 kPa and thermal conductivity of 23.2 mW (m-1 K-1), classified as type 1 for a rigid structural sandwich panel core and type 2 for rigid thermal insulation foam applications according to ASTM standards. Fourier-transform infrared (FTIR) spectroscopy was performed to characterize the chemical features of the polyols and foams. Scanning electron microscopy (SEM) analysis was also performed to evaluate the morphological structures of the synthesized foams. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were conducted to investigate the foam's thermal characteristics. Thus far, this work is the first to report a novel and effective reaction mechanism for the synthesis of a highly functional CO-derived polyol and the first CO-based polyol with no petroleum-based replacement that may serve as raw material for rigid PUA foam production. PUA hybrid foams are potential insulation and structural materials. This study further provided a compelling case for enhanced sustainability of p-CDEA PUA hybrid foam against petroleum-based polyurethane.

Ab-initio study of pressure influenced elastic, mechanical and optoelectronic properties of Cd0.25Zn0.75Se alloy for space photovoltaics.

The optoelectronic properties of the ternary Cd0.25Zn0.75Se alloy are reported under the influence of a high pressure ranging from 0 to 25 GPa, within a modified Becke-Jhonson potential using density functional theory. This alloy has a cubic symmetry, is mechanically stable, and its bulk modulus rises with pressure. It is observed to be a direct bandgap material with a bandgap energy that increases from 2.37 to 3.11 eV with rise in pressure. Pressure changes the optical and electronic properties, causing the absorption coefficient to rise and absorb visible green-to-violet light. The static dielectric constant, along with the static index of refraction, both increase under the influence of pressure. Optical constants, including dielectric constant, optical conductivity, refractive index, extinction coefficient, and reflection, are also investigated and discussed. This DFT forecast explores important research directions for the usage of the CdZnSe semiconductor alloys in the manufacturing of space photovoltaic and optoelectronic devices operating at different pressures.

Light-Weighted Deep Learning Model to Detect Fault in IoT-Based Industrial Equipment.

Industry 4.0, with the widespread use of IoT, is a significant opportunity to improve the reliability of industrial equipment through problem detection. It is difficult to utilize a unified model to depict the working condition of devices in real-world industrial scenarios because of the complex and dynamic relationship between devices. The scope of this research is that it can detect equipment defects and deploys them in a natural production environment. The proposed research is describing an online detection method for system failures based on long short-term memory neural networks. In recent years, deep learning technology has taken over as the primary method for detecting faults. A neural network with a long short-term memory is used to develop an online defect detection model. Feature extraction from sensor data is done using the curve alignment method. Based on long-term memory neural networks, the fault detection model is built (LSTM). In the end, sliding window technology is used to identify and fix the problem: the model's online detection and update. The method's efficacy is demonstrated by experiments based on real data from power plant sensors.

Recognition of Protein Network for Bioinformatics Knowledge Analysis Using Support Vector Machine.

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.

Identification and Classification of Depressed Mental State for End-User over Social Media.

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.

Prediction of Rockfill Materials' Shear Strength Using Various Kernel Function-Based Regression Models-A Comparative Perspective.

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.

Homogeneous Decision Community Extraction Based on End-User Mental Behavior on Social Media.

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.