Mechanism study of silver nanoparticle production using Neurospora intermedia

Elucidation of the molecular mechanism of silver nanoparticle (AgNP) synthesis is necessary to control nanoparticle size, shape, and monodispersity. In this study, the mechanism of AgNP formation by Neurospora intermedia was investigated. The higher production rate of AgNP formation using a culture supernatant heat‐treated at 100° and 121°C relative to that with an un‐treated culture supernatant indicated that the native form of the molecular species is not essential. The effect of the protein molecular weight (MW) on the nanoparticle size distribution and average size was studied by means of ultraviolet–visible spectroscopy and dynamic light scattering. Using un‐treated and concentrated cell‐free filtrate passed through 10 and 20 kDa cut‐off filters led to the production of AgNPs with average sizes of 25, 30, and 34 nm, respectively. Also, using the permeate fraction of cell‐free filtrate passed through a 100 kDa cut‐off filter led to the formation of the smallest nanoparticles with the narrowest size distribution (average size of 16 nm and polydispersity index of 0.18). Sodium dodecyl sulphate polyacrylamide gel electrophoresis analysis of the fungal extracellular proteins showed two notable bands with the MWs of 15 and 23 kDa that are involved in the reduction and stabilisation of the nanoparticles, respectively.Inspec keywords: silver, nanoparticles, nanofabrication, proteins, molecular weight, ultraviolet spectra, visible spectra, cellular biophysics, electrophoresis, molecular biophysicsOther keywords: Neurospora intermedia, molecular mechanism, silver nanoparticle synthesis, nanoparticle shape, nanoparticle monodispersity, AgNP formation, untreated culture supernatant, molecular species, protein molecular weight, MW, nanoparticle size distribution, ultraviolet‐visible spectroscopy, dynamic light scattering, untreated cell‐free filtrate, concentrated cell‐free filtrate, cut‐off filters, permeate fraction, polydispersity index, Sodium dodecyl sulphate polyacrylamide gel electrophoresis analysis, fungal extracellular proteins, nanoparticle reduction, nanoparticle stabilisation, temperature 100 degC, temperature 121 degC, size 25 nm, size 30 nm, size 34 nm, size 16 nm, Ag     

An ab initio study on properties of cationic chalcogen bonds in XF2Y+⋯NCZ (X═H,CN, F; Y═S,Se; Z═H,Cl, Br) complexes

The geometries, interaction energies, and bonding properties of cationic chalcogen bonds are studied in binary complexes XF₂Y⁺?NCZ (X═H, CN, F; Y═S, Se; Z═H, Cl, Br). The nature of these interactions is studied by a vast number of methods, including molecular electrostatic potential (MEP), Noncovalent Interaction Index (NCI), quantum theory of atoms in molecules (QTAIM), and natural bond orbital (NBO) analyses. The interaction energies of these complexes vary between ?20.94?kcal/mol in HF₂S⁺?NCH and ?33.72?kcal/mol in F₃Se⁺?NCBr. According to the QTAIM analysis, all these cationic chalcogen bonds are classified as a closed-shell interaction with a partial covalent character. Moreover, cooperative effects between cationic chalcogen bond and hydrogen or halogen bond interactions are studied in ternary XF₂Y⁺?NCZ?NH₃ complexes. These cooperative effects are analyzed in terms of the parameters derived from the QTAIM and NBO analyses, and electron density difference plots.     

Simulation and energy optimization of the stabilizer tower of a pyrolysis gasoline hydrogenation unit

Energy optimization of second distillation tower of a pyrolysis gasoline hydrogenation unit has been studied by the thermal cycle of vapor recompression method. The mentioned cycle is connected to the second distillation tower of the stabilizer of pyrolysis gasoline, and the results are found promising. The composite pinch curve for both the current and the optimized methods are shown. Moreover, an increase in the heat transfer rate in heat exchanger E-1014 causes energy recovery in reboiler. According to simulation results, by vapor recompression to 1970 kPa and using this heat source for thermal integration, condenser and reboiler’s energies are decreased by 56.93 and 30.4 percentage, respectively.     

Graphene nanoplatelets dispersion in poly(<Emphasis Type="SmallCaps">l</Emphasis>-lactic acid): preparation method and its influence on electrical,crystallinity and thermomechanical properties

Poly(l-lactic acid) (PLLA)/graphene nanoplatelets (GnP) nanocomposites were prepared through solvent casting and coagulation methods. The better dispersion of graphene was achieved by ultrasounds and its effect on crystallinity, thermomechanical and electrical properties of PLLA were studied and compared in both methods. Differential scanning calorimetry (DSC) was used to investigate the crystallinity of PLLA and its composites. Field emission gun scanning electron microscope (FEG-SEM) and wide-angle X-ray scattering (WAXS) were employed to characterize the microstructure of PLLA crystallites. Dynamic mechanical thermal analysis (DMTA) was performed to study the thermomechanical properties of the nanocomposites. FEG-SEM images illustrated finer dispersion of GnP in samples obtained by coagulation method with respect to solvent casting method. Graphene imparted higher electrical conductivity to nanocomposites obtained by solvent casting under ultrasound due to better formation of graphene network. DSC thermograms and their resulting data showed positive effects of GnP on crystallization kinetics of PLLA in both methods enhanced by the nucleating effect of graphene particles. Meanwhile, the effect of GnP, as nucleating agent, was more prominent in samples produced by coagulation method without utilization of ultrasounds. WAXS patterns represented the same characteristic peaks of PLLA in nanocomposite specimens suggesting similar crystalline structure of PLLA in presence of graphene, and the intensified peaks of nanocomposites compared to neat PLLA confirmed the DSC results regarding its improved crystallinity. Graphene increased storage modulus in rubbery region and glass transition temperature of nanocomposites in the coagulation method due to restricted mobility of PLLA chains.     

Photocatalytic degradation of Basic Blue 41 dye under visible light over SrTiO3/Ag3PO4 hetero-nanostructures

In an attempt to develop nanostructured photocatalysts with high performance, SrTiO₃/Ag₃PO₄ hetero-nanostructures were successfully fabricated. The formed binary heterojunctions were composed of SrTiO₃ nanotubes prepared using liquid-phase deposition, and Ag₃PO₄ nanoparticles prepared using a sol–gel method. Synthesis details, including morphology, structure, and optical properties of the prepared photocatalysts, were characterized and comparatively discussed. The results showed that at an optimal ratio of SrTiO₃ to Ag₃PO₄ (20–80), the photocatalytic degradation of Basic Blue 41 under 80-min visible light irradiation is the maximum amount of 99%, which is about 4.4 and 1.5 times higher than that of pristine SrTiO₃ nanorods and Ag₃PO₄ nanoparticles, respectively. It can be due to the synergistic effect of two materials that provide high light absorption and charge carriers’ separation. Finally, a detailed possible mechanism for enhancing the photocatalytic activity of the SrTiO₃/Ag₃PO₄ hetero-nanostructures was proposed.     

Enzymatic treatment of peanut kernels to reduce allergen levels

This study investigated the use of enzymatic treatment to reduce peanut allergens in peanut kernels as affected by processing conditions. Two major peanut allergens, Ara h 1 and Ara h 2, were used as indicators of process effectiveness. Enzymatic treatment effectively reduced Ara h 1 and Ara h 2 in roasted peanut kernels by up to 100% under optimal conditions. For instance, treatment of roasted peanut kernels with α-chymotrypsin and trypsin for 1–3 h significantly increased the solubility of peanut protein while reducing Ara h 1 and Ara h 2 in peanut kernel extracts by 100% and 98%, respectively, based on ELISA readings. Ara h 1 and Ara h 2 levels in peanut protein extracts were inversely correlated with protein solubility in roasted peanut. Blanching of kernels enhanced the effectiveness of enzyme treatment in roasted peanuts but not in raw peanuts. The optimal concentration of enzyme was determined by response surface to be in the range of 0.1–0.2%. No consistent results were obtained for raw peanut kernels since Ara h 1 and Ara h 2 increased in peanut protein extracts under some treatment conditions and decreased in others.     

Simultaneously energy production and dairy wastewater treatment using bioelectrochemical cells: In different environmental and hydrodynamic modes

A successful design, previously adapted for treatment of complex wastewaters in a microbial fuel cell (MFC), was used to fabricate two MFCs, with a few changes for cost reduction and ease of construction. Performance and electrochemical characteristics of MFCs were evaluated in different environmental conditions (in complete darkness and presence of light), and different flow patterns of batch and continuous in four hydraulic retention times from 8 to 30 h. Changes in chemical oxygen demand, and nitrate and phosphate concentrations were evaluated. In contrast to the microbial fuel cell operated in darkness (D-MFC) with a stable open circuit voltage of 700 mV, presence of light led to growth of other species, and consecutively low and unsteady open circuit voltage. Although the performance of theMFC subjected to light (L-MFC)was quite lowand unsteady in dynamic state (internal resistance = 100 Ω, power density = 5.15 W·m^-3), it reached power density of 9.2 W·m^-3 which was close to performance of D-MFC (internal resistance = 50 Ω, power density = 10.3 W·m^-3). Evaluated only for D-MFC, the coulombic efficiency observed in batch mode (30%) was quite higher than the maximum acquired in continuous mode (9.6%) even at the highest hydraulic retention time. In this study, changes in phosphate and different types of nitrogen existing in dairy wastewater were investigated for the first time. At hydraulic retention time of 8 h, the orthophosphate concentration in effluent was 84% higher compared to influent. Total nitrogen and total Kjeldahl nitrogen were reduced 70% and 99% respectively at hydraulic retention time of 30 h, while nitrate and nitrite concentrations increased. The microbial electrolysis cell (MEC), revamped from D-MFC, showed the maximum gas production of 0.2 m³ H₂·m^-3·d^-1 at 700 mV applied voltage.     

In vitro studies on calf thymus DNA interaction and 2-tert-butyl-4-methylphenol food additive

The interaction of native calf thymus DNA (CT-DNA) with 2-tert-butyl-4-methylphenol (TBMP) at physiological pH has been investigated by spectrofluorometric and viscosimetric techniques. TBMP molecules were found to intercalate between base pairs of DNA, demonstrated by an increase in the specific viscosity of DNA and decrease in the fluorescence of TBMP solutions in the presence of increasing amounts of DNA and the calculated binding constants (K _f) at different temperatures. Furthermore, the enthalpy and entropy of the reaction between TBMP and CT-DNA showed that the reaction is exothermic and enthalpy favored (ΔH = −19.18 kJ mol⁻¹; ΔS = −26.98 J mol⁻¹ K⁻¹) which are other evidences to indicate that TBMP is able to be intercalated in the DNA base pairs.     

Structural performance assessment of GFRP elastic gridshells by machine learning interpretability methods

The prediction of structural performance plays a significant role in damage assessment of glass fiber reinforcement polymer (GFRP) elastic gridshell structures. Machine learning (ML) approaches are implemented in this study, to predict maximum stress and displacement of GFRP elastic gridshell structures. Several ML algorithms, including linear regression (LR), ridge regression (RR), support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), category boosting (CatBoost), and light gradient boosting machine (LightGBM), are implemented in this study. Output features of structural performance considered in this study are the maximum stress as f₁(x) and the maximum displacement to self-weight ratio as f₂(x). A comparative study is conducted and the Catboost model presents the highest prediction accuracy. Finally, interpretable ML approaches, including shapely additive explanations (SHAP), partial dependence plot (PDP), and accumulated local effects (ALE), are applied to explain the predictions. SHAP is employed to describe the importance of each variable to structural performance both locally and globally. The results of sensitivity analysis (SA), feature importance of the CatBoost model and SHAP approach indicate the same parameters as the most significant variables for f₁(x) and f₂(x).