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     

Electroless nano zinc oxide–activate carbon composite supercapacitor electrode

An electroless deposition process was used to synthesize the nanostructured zinc oxide (ZnO)–activated carbon (AC) as supercapacitor. The composite oxide was studied by high resolution transmission electron microscopy (HRTEM), scanning electron microscopy (FESEM), energy dispersive X-ray spectroscopy (EDX) and X-ray diffraction analysis (XRD). The electrochemical performance of the nanocomposite was analyzed through cyclic voltammetry (CV) and AC impedance spectroscopy (EIS) in 0.1 M Na₂SO₄ as electrolyte. A specific capacitance 187 F g^?1 at a scan rate of 5 mV s^?1 was obtained using cyclic voltammetry (CV) and a nearly rectangular shaped CV curve was observed for the composite oxide. The supercapacitor was quite stable during charge–discharge cycling and exhibited constant capacitance during the long-term cycling. It also yielded a specific capacitance 171 F g^?1 at 5 mA cm^?2 with a high energy density of 21.9 Wh kg^?1 and 4.2 kW kg^?1 of power density. Due to unique structure of prepared ZnO–AC nanocomposite, it is a promising candidate for supercapacitor.     

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.     

Effect of hydrogen combustion reaction on the dehydrogenation of ethane in a fixed-bed catalytic membrane reactor

A two-dimensional non-isothermal mathematical model has been developed for the ethane dehydrogenation reaction in a fixed-bed catalytic membrane reactor. Since ethane dehydrogenation is an equilibrium reaction, removal of produced hydrogen by the membrane shifts the thermodynamic equilibrium to ethylene production. For further displacement of the dehydrogenation reaction, oxidative dehydrogenation method has been used. Since ethane dehydrogenation is an endothermic reaction, the energy produced by the oxidative dehydrogena-tion method is consumed by the dehydrogenation reaction. The results show that the oxidative dehydrogenation method generated a substantial improvement in the reactor performance in terms of high conversions and significant energy saving. It was also established that the sweep gas velocity in the shell side of the reactor is one of the most important factors in the effectiveness of the reactor.     

Electroless copper-phosphorus coatings with the addition of silicon carbide （SiC） particles

Cu-P-silicon carbide(SiC)composite coatings were deposited by means of electroless plating.The effects ofpH values,temperature,and different concentrations of sodium hypophosphite(NaH2PO2·H2O),nickel sulfate(NiSO4·6H2O),sodium citrate(C6H5Na3O7·2H2O)and SiC on the deposition rate and coating compositions were evaluated,and the bath formulation for Cu-P-SiC composite coatings was optimised.The coating compositions were determined using energy-dispersive X-ray analysis(EDX).The corresponding optimal operating parameters for depositing Cu-P-SiC are as follows:pH 9; temperature,90℃; NaH2PO2·H2O concentration,125 g/L; NiSO4·6H2O concentration,3.125 g/L; SiC concentration,5 g/L; and C6H5Na3O7·2H2O concentration,50 g/L.The surface morphology of the coatings analysed by scanning electron microscopy(SEM)shows that Cu particles are uniformly distributed.The hardness and wear resistance of Cu-P composite coatings are improved with the addition of SiC particles and increase with the increase of SiC content.     

Two-phase data traffic optimization of wireless sensor networks for prolonging network lifetime

Wireless network sensing and control systems are becoming increasingly important in many application domains due to advent of nanotechnology. The size of a wireless sensor network can easily reach hundreds or even thousands of sensor nodes. Since these types of networks usually have limited battery resources, power consumption optimization for prolonging system lifetime of such networks have received a great attention by the researchers in this field in recent years. In this paper, a centralized approach for clustering and data transmission mechanism is proposed that optimizes the power consumption and hence lifetime of the network. The mechanism is comprised of two phases. In the first phase, a mechanism based on a centralized cluster head selection that utilizes information such as nodes residual energies and their locations in the network is proposed in order to select the most appropriate candidates as cluster heads. In the second phase, the concept of a “window size” is introduced where minimization of the number of cluster head changes of a node and consequently maximization of the network lifetime is considered. Simulation results validate that the proposed mechanism does effectively reduce data traffic and therefore increases network lifetime.     

Flower-like architecture of CoSn4 nano structure as anode in lithium ion batteries

CoSn₄ nano-particles were synthesized on Cu and Ni substrates through pulsed current electrodeposition and used as anode in lithium ion batteries. Nano particles with Flower-like morphology were obtained through applying an average current density of 85 mA/cm² on Ni substrate while the particles formed using constant current electrodeposition are greater in size ca. 500 nm. Optimum discharge capacity of synthesized CoSn₄ was obtained 848 mAh g^?1 which reduced to 500 mAh g^?1 at 120th cycle indicating an enhanced electrochemical performance compared to anode films synthesized through other pulsed current densities and also constant current electrodeposition. This high discharge capacity and cycleability is attributed to finer crystal grains and flower-like morphology of nano particles. Also, the sample synthesized on Ni substrate showed higher cycleability and noticeably lower resistance. High resistance of anode film synthesized on Cu substrate is due to the corrosion and passivation of copper occurred by HF formation in LiPF₆ electrolyte.     

Fault diagnosis of an automobile cylinder head using low frequency vibrational data

This paper proposes a vibration-based fault-diagnosis method for mechanical parts. This method, after algorithm development, only requires a single inexpensive test to inspect the part which could take as short as half a second. The algorithm is developed in three major stages, (i) exciting specimens without or with known faults using a controlled force and recording acceleration of a single point for a short time (ii) finding a signature for each faulty specimen, using Fourier transform and statistical analysis. (iii) Developing a multi-layer perceptron, as a mathematical model, using the results of stage (ii). The elements of a part signature are the inputs to the model. The location (and possibly size and shape factor) of the fault is model output. Stage (i) can be performed experimentally or alternatively with a validated FEM, one experiment or simulation per specimen. The proposed technique was examined to locate (isolate) a fault on an automobile cylinder head. The presented accuracy is considerable, and the data collected at fairly low frequency range (below 1200 Hz) were found to be sufficient for this technique. In the case study of this paper, possible fault locations are on a line; as a result, fault location has one dimension. It is shown that the technique can be extended to higher dimensions.     

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).