Chemical Papers最新文献

The main problem with food authenticity is that labels and information that are not clear about where the ingredient in food comes from. One of ingredient that difficult to detect where the sources which used as raw materials is gelatine. Therefore, the detection method that is easy to use, quick, accurate, low-cost, and more efficient is necessary to develop. The objective of the research is to examine the use of 1H NMR spectroscopy, visual analysis to identify the gelatine product raw material sources. The results show that based on 1 H NMR analysis, fish and calf gelatine characterize with amino acid methionine and valine, meanwhile porcine gelatine characterize with amino acid proline. Furthermore, based on colorimetry analysis fish and calf gelatine was characterized by high a* and b* values. Meanwhile, the sample of porcine gelatine was characterized by a high L* value.

Photocatalysis and biocatalysis represent powerful efficient tools in synthetic chemistry. While, both have individually shown promising results, their integration remains challenging, particularly in continuous flow processes. This work presents a semicontinuous setup, combining photo- and biocatalysis in a multistep synthesis for the production of optically pure (S)-3,3,3-trifluoro-1-phenylpropan-1-ol. Initially, a photocatalytic trifluoromethylation of a methyl ketone in α-position in a self-made photoreactor was tested in flow, followed by enzymatic ketone reduction catalyzed by an alcohol dehydrogenase (variant of an ADH from Lactobacillus kefir). The study addresses the challenge of enzyme stability in aggressive solvents, developing a robust immobilization approach for the selected ADH with a PVA/PEG cryogel matrix. This strategy has been investigated in this work to ensure enzyme stability in THF, marking a notable advance in compatibility for continuous cascades. The separate process steps were finally combined in a semicontinuous flow system, achieving a space–time yield for the photocatalytic step of 39.8 g L⁻¹ h⁻¹ and of 1.12 g L⁻¹ h⁻¹ for the enzymatic step. The study signifies one of the first instances of combining photo- and biocatalysis in continuous cascades, offering an innovative approach to synthesizing chiral 3,3,3-trifluoro-1-phenylpropan-1-ol.

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In this study, we synthesized lead aluminosilicate glasses doped with Gd³⁺ ions and investigated the effects of varying Al₂O₃ concentrations on their properties. Specifically, we explored the photoluminescence behavior as well as the optical, infrared, and other spectroscopic characteristics of these glasses. Our infrared (IR) spectroscopic analysis revealed that the depolymerization of the glass network progressively increased with Al₂O₃ concentrations up to 5.0 mol%. Under excitation at 273 nm, corresponding to the ⁶P_7/2→⁸S_7/2 transition of Gd³⁺ ions, we observed significant UVB emissions at 311 nm. Notably, the emission intensity of glass containing 5.0 mol% Al₂O₃, when mixed with alumina-free glass, was found to be four times greater than that of glass with 1 mol% alumina. This enhancement in emission can be attributed to the declustering of Gd³⁺ ions and the ability of Al³⁺ ions to reduce cross-relaxation losses, thus enriching the photoluminescence properties. These findings provide valuable insights into the design and optimization of lead aluminosilicate glasses for applications requiring enhanced UVB emissions.

Propene polymerization kinetic profiles with a diether-based Ziegler–Natta MgCl₂-supported catalyst were investigated in a stainless-steel batch reactor. The initial 10 min period characterizes various temperature levels with a constant volume of liquid propene. The lowest temperature level corresponds to the usual prepolymerization temperature (10 °C), and the highest level corresponds to the usual main polymerization temperature (70 °C). The effects of the starting temperature levels were evaluated through polymerization kinetic patterns computed namely from the second polymerization period carried out at 70 °C for the next 90 min. Based on the heat transfer data, the kinetic profiles were fitted to suitable semi-empirical equations derived from fundamental kinetic approaches using the first and second orders of the catalyst active sites decay. Both approaches adequately describe the dependence of the initial activities and deceleration constants on the temperature during the initial period.

The imidazoheterocycles (fused imidazoles), widely distributed in nature and pharmaceuticals, find many applications in drug discovery, metal sensing and C–H activations, OLEDs, etc. Thus, developing new novel methods for their synthesis has been an active area of research and hence received a great deal attention from synthetic chemists. Likewise, the multicomponent reactions (MCRs) are a hot topic in synthetic research. Herein, we present this review article that covers the multicomponent synthesis of C-3 functionalized imidazoheterocycles from the one-pot reaction of aryl glyoxal, α-aminoazaarene and cyclic 1,3-dicarbonyls and the notable achievements in the area since 2014. Also, this review highlights the substrate-dependent unusual reactions and the potential future perspective in the area for further advancement.

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The aryl glyoxal-based MCR provides diverse array of highly functionalized imidazoheterocycles from the one-pot MCR of α-aminoazaarene, aryl glyoxal and cyclic 1,3-dicarbonyl through Knoevenagel condensation, Michael addition, intramolecular cyclization and dehydration sequences.

This study presents an exploration into the relationship between the molecular structure of oil-soluble fluorinated surfactants and their surface properties across a variety of organic solvents. A series of oil-soluble surfactants with fluorocarbon/hydrocarbon hybrid chains, N-methyl-N-alkyl-4-perfluoroalkylsulfoxybenzylamines (FmHn), were designed and synthesized, and their surface activity was evaluated in 12 organic solvents with different polarities. The surface parameters, including CMC, ({gamma }_{CMC}), ({Gamma }_{max}) and ({A}_{min}), were measured in n-hexadecane, m-xylene, and DMSO, allowing for an in-depth analysis of the influence of molecular structure on these surface properties. Results indicate that an increase in the length of the fluorocarbon chain generally enhances surface activity, leading to a reduction in the CMC value and an increase in the effectiveness of surface tension reduction. However, the impact of the hydrocarbon chain length on surface activity is more complex and dependent on the polarity of the organic solvents. In low-polarity solvents, surface activity is improved with a longer hydrocarbon chain, whereas in high-polarity solvents, a shorter hydrocarbon chain is more beneficial. Consequently, a “polarity–directionality” strategy was proposed to tailor the molecular structure of surfactants to optimize performance in solvents with varying polarities, resulting in a significant reduction in surface tension. Specifically, F8H12 was identified as particularly effective in low-polarity n-alkanes and cycloalkanes, F6H8 was most effective in medium polarity aromatics, and F8H4 or F6H4 was ideal for larger polar solvents. These findings enrich the understanding of the structure–activity relationship in oil-soluble fluorinated surfactants and offer new perspectives for the development of high-performance surfactants with reduced environmental impact.

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In the current work, a covalent organic framework-reinforced hollow fiber-solid phase microextraction method has been introduced for the extraction of patulin and ochratoxin A from commercial apple juices before their determination with high-performance liquid chromatography-tandem mass spectrometry. This method benefited from the advantages of traditional hollow fiber-liquid phase microextraction and solid phase microextraction methods. Briefly, in this method, after preparation of the covalent organic framework-reinforced hollow fiber through a simple method, it was placed into the sample solution and the mixture was stirred to increase the contact surface. After stopping the stirring process, the hollow fiber was taken and placed into a vial containing choline chloride: dichloroacetic acid deep eutectic solvent as an elution solvent. In the following, the mixture was sonicated to accelerate the desorption process. Finally, the eluate containing the extracted analytes was taken and injected into separation system. Under optimized conditions, validation of the developed method was done and the outcomes indicated great precision (relative standard deviations ≤ 5.2%), good linearity (coefficient of determination ≥ 0.9974) in a wide range (77.3–1 × 10⁶ ng L⁻¹), and low limits of detection (23.2 and 15.6 ng L⁻¹ for patulin and ochratoxin A, respectively) and quantification (77.3 and 52.1 ng L⁻¹ for patulin and ochratoxin A, respectively). At the end, the suggested approach was employed for the analysis of patulin and ochratoxin A in thirty commercial apple juice samples. Based on the results, patulin and ochratoxin A were detected in 17% and 23% of the investigated apple juice samples, respectively.

The revolutionized field of nanotechnology emerged in the oil and gas sector to enhance drilling fluid performance. This technology increases the success rate of drilling operations in different environments. However, their performance in presence of various salts has not been entirely discovered up till now. In this research, three different salt mud systems that include KCl, NaCl and MgCl₂ were prepared and coated with silica iron oxide nanoparticles. The rheological and filtration characteristics of all the samples with the joint interaction of salt and nanoparticles were analyzed. Additionally, the stability of the mud samples after the interaction between the two entities was investigated using a visualization tool. This unique application will be useful in observing the changes in the mud system during nonproductive time events (NPT). The result of the study reveals that all the fluids in the presence of these entities showed shear thinning behavior with flow index (< 1). Reduction in shear stresses in the salt systems was observed mainly because of the neutralization of the negative charges associated with the base mud system. However, this behavior was weaker in monovalent cations as compared with divalent. KCL/(Si/Fe₃O₄) demonstrates the most stable mud system when investigated through intensity profile plots. Minimum segregation and maximum dispersion were the key parameters that demonstrated its stability. Moreover, all three cations in the presence of Si/Fe₃O₄ nanoparticles reduce the filtrate loss volume and provide a thin and smooth mud cake. The Si/Fe₃O₄ because of its spherical bead shape plugs the pore spacing of the filter paper while blocking of microsize spacing was done with the help of cations.

The petroleum refining business consumes approximately 0.2 MMBTU/BBL of energy annually. This consumption is mitigated using heat integration techniques. However, a significant challenge in this process is fouling in the preheat train network of heat exchangers. Fouling necessitates regular cleaning, leading to substantial operational inefficiencies and costs, with annual losses estimated at nearly $16.5 billion. To address this issue, implementing a predictive maintenance model is crucial for performing maintenance at optimal periods, thereby reducing these losses. The study proposes an artificial neural network (ANN) developed using MATLAB’s nntool, trained on industrial heat exchanger samples that were preprocessed in Microsoft Excel. This ANN model is designed to forecast fouling patterns in shell and tube heat exchangers. The model’s accuracy and effectiveness were validated using R² (coefficient of determination) and RMSE (root mean square error) measures. The results indicated that the EA-307 Feed-Forward Back-Propagation Neural Network (FFBPNN) model delivered satisfactory performance, with an R² value of 0.9961. This high level of accuracy underscores the significant impact of the number of neurons on the model’s predictive output. Furthermore, the model’s testing on a new dataset yielded impressive results, achieving an R² value of 0.966. This demonstrates the model’s robustness and reliability in predicting fouling patterns, facilitating improved maintenance schedules, and minimizing the financial losses associated with fouling. The study highlights the potential of advanced neural network models to significantly enhance the operational efficiency of petroleum refineries by enabling more precise and timely maintenance interventions.

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