Poly(ethylene oxide)/clay nanocomposites for solid polymer electrolyte applications

Poly(ethylene oxide) (PEO)/clay nanocomposites were prepared using a solution intercalation method. The organoclay (Nanocore I30E) used for nanocomposite synthesis was basically an octadecylammonium salt of montmorillonite clay prepared using an ion exchange method. Nanocomposite‐based solid polymer electrolytes were prepared using LiBF₄. The nanocomposite structures were characterised using wide‐angle X‐ray diffraction. The crystallisation behaviour and thermal properties were studied using differential scanning calorimetry. It was found that the crystallinity of the composite electrolytes decreases with increasing clay concentration up to 7.5 wt% and then increases with a further increase in clay concentration. The trend is different from that observed in PEO/clay nanocomposites without lithium salt where the crystallinity gradually decreases with increasing clay concentration. The solid polymer electrolyte samples were evaluated using an alternating current impedance analyser. A considerable increase in room temperature conductivity was observed at the optimum clay concentration. The conductivity decreases beyond the optimum clay concentration. Copyright © 2007 Society of Chemical Industry     

Influence of surface‐modified nanoclay on the self‐organized nanostructure of segmented polyurethane composites

The nanoscale morphology of segmented polyurethane (SPU) nanocomposites containing various proportions of organomodified montmorillonite (MMT) and Laponite (dual modified using ionic amine modification followed by silane modification) was studied. These nanocomposites were prepared by solution casting and characterized using small‐angle X‐ray scattering (SAXS), transmission electron microscopy (TEM), variable‐temperature X‐ray diffraction (VT‐XRD) and modulated differential scanning calorimetry (MDSC). TEM micrographs show uniform dispersion of MMT in SPU nanocomposites, and the dispersion is better than in Laponite‐based ones. Nanocrystalline morphology development in annealed samples of the nanocomposites was studied using VT‐XRD (140 to 25 °C at constant cooling rate), which confirms the formation of near‐triclinic unit cell geometry with different planes of reflection depending on temperature, type of clay and modification (aspect ratio, polarity). It is found that clay (MMT) having higher aspect ratios imposes greater restrictions against the formation of crystallographic planes of various inclinations. The overall crystallinity of SPU appears less affected in the presence of Laponite as compared to MMT. This is confirmed by the MDSC results showing variations and multiplicity of the glass transition temperature and entropies. Finally, SAXS studies related to interdomain repeat distances and interfacial roughness give an in‐depth understanding regarding the effect of nanoclay on annealing, crystallinity and reinforcement of polymer microstructures. Such reinforcement effect is maximized in the case of dual‐modified Laponite‐based SPU. Copyright © 2011 Society of Chemical Industry     

ANN Model to Predict Fracture Characteristics of High Strength and Ultra High Strength Concrete Beams

This paper presents fracture mechanics based Artificial Neural Network (ANN) model to predict the fracture characteristics of high strength and ultra high strength concrete beams. Fracture characteristics include fracture energy (Gf), critical stress intensity factor (KIC) and critical crack tip opening displacement (CTODc). Failure load of the beam (Pmax) is also predicated by using ANN model. Characterization of mix and testing of beams of high strength and ultra strength concrete have been described. Methodologies for evaluation of fracture energy, critical stress intensity factor and critical crack tip opening displacement have been outlined. Back-propagation training technique has been employed for updating the weights of each layer based on the error in the network output. Levenberg- Marquardt algorithm has been used for feed-forward back-propagation. Four ANN models have been developed by using MATLAB software for training and prediction of fracture parameters and failure load. ANN has been trained with about 70% of the total 87 data sets and tested with about 30% of the total data sets. It is observed from the studies that the predicted values of Pmax, Gf, failure load, KIc and CTODc are in good agreement with those of the experimental values.     

Sequential interpenetrating polymer network based on nitrile–phenolic blend and poly(alkyl methacrylate)

Interpenetrating polymer networks (IPNs) based on a nitrile rubber (NBR)–phenolic resin (PH) blend and poly(alkyl methacrylates) were synthesized by a sequential method. The cured blends were swollen in a methacrylate monomer containing a crosslinker and initiator. The swollen rubber sheets were cured at 60°C. From the swelling study of the monomer, it was found that IPN formation in the blend is in between the rubber and poly(alkyl methacrylate) phases only. The IPNs thus formed were characterized for their tensile, dynamic mechanical, and solvent-resistance characteristics. The tensile strength of the IPNs are dependent on the PH content; at a lower content of PH (up to 20 parts), IPNs have a higher strength compared to their corresponding blends, whereas at a higher content of PH (beyond 30 parts), the strength decreases. But for every NBR/PH-fixed composition, the strength of IPNs was found to be increasing in the order of PBuMA < PEMA < PMMA. The dynamic property results showed that NBR/PH blends are incompatible. The storage modulus of IPNs are always higher than their corresponding blends at all temperatures. The tan δ peaks of IPNs are broad, indicating the presence of microphase-separated domains. The IPNs show superior solvent-resistance characteristics compared to the blends. © 1998 John Wiley & Sons, Inc. J Appl Polym Sci 68:255–262, 1998     

Ensemble unit and AI techniques for prediction of rock strain

The behavior of rock masses is influenced by a variety of forces, with measurement of stress and strain playing the most critical roles in assessing deformation. The laboratory test for determining strain at each location within rock samples is expensive and difficult but rock strain data are important for predicting failure of rock material. Many researchers employ AI technology in order to solve these difficulties. AI algorithms such as gradient boosting machine (GBM), support vector regression (SVR), random forest (RF), and group method of data handling (GMDH) are used to efficiently estimate the strain at every point within a rock sample. Additionally, the ensemble unit (EnU) may be utilized to evaluate rock strain. In this study, 3000 experimental data are used for the purpose of prediction. The obtained strain values are then evaluated using various statistical parameters and compared to each other using EnU. Ranking analysis, stress-strain curve, Young’s modulus, Poisson’s ratio, actual vs. predicted curve, error matrix and the Akaike’s information criterion (AIC) values are used for comparing models. The GBM model achieved 98.16% and 99.98% prediction accuracy (in terms of values of R²) in the longitudinal and lateral dimensions, respectively, during the testing phase. The GBM model, based on the experimental data, has the potential to be a new option for engineers to use when assessing rock strain.     

Nanocomposite solid polymer electrolytes based on polyethylene oxide,modified nanoclay,and tetraethylammonium tetrafluoroborate for application in solid‐state supercapacitor

Nanocomposite solid polymer electrolytes (SPEs) have been prepared from polyethylene oxide (PEO), organically modified nanoclay (MNclay), and tetraethylammonium tetrafluoroborate (TEABF₄) salt. The concentration of the salt has been varied in the respective SPE, wherein PEO/MNclay ratio was kept constant. It has been proposed that three types of complex formation could be operative in the SPEs due to the interactions among PEO, MNclay, and the salt. The complex formation mechanism has been postulated on the basis of X‐ray diffraction (XRD) analysis, transmission electron microscopic (TEM) observation, differential scanning calorimetric (DSC) analysis, and polarized optical microscopic (POM) observation. ‘Complex 1’ and ‘complex 3’ formation could be involved in the crystalline phase as indicated by DSC and XRD analyses, whereas ‘complex 2’ formation might be restricted in the amorphous phase as suggested by TEM observation. The ionic conductivity of the SPEs has been correlated with the results obtained from XRD, DSC, and POM analyses. The formation of complex 1 and complex 2 could be responsible for the increase in the ionic conductivity, whereas complex 3 formation might decrease the ionic conductivity. An activated carbon‐based supercapacitor has been fabricated using SPEs and characterized by cyclic voltammetry, galvanostatic ‘charge–discharge’ behavior, and impedance spectroscopic analysis. POLYM. ENG. SCI., 55:1536–1545, 2015. © 2015 Society of Plastics Engineers     

Impact of carbon black substitution with nanoclay on microstructure and tribological properties of ternary elastomeric composites

Various particulate composites based on epoxidised natural rubber (ENR), carbon black (CB), and nanoclay (NC) were prepared keeping the total filler content constant at 35 phr (parts per 100 g rubber). Tribology and hysteretic (stress–strain) properties of the composites were analyzed. Morphology of these composites were also characterized by small angle X-ray scattering (SAXS), transmission electron microscopy (TEM), scanning electron microscopy (SEM) to establish the structure–property correlations. SAXS results reveal enhancement in overall interfacial roughness (ds) with the increased substitution of CB by NC. Increased CB–NC interface causes enhancement in ds, leading to reduction in wear resistance of ternary composites. Reduction of wear resistance for NC populated samples is attributed to lower dispersion parameter (D_0,1) values of NC in the matrix, realized through image analysis of TEM photomicrographs. Thus, for ternary particulate samples, a definite interrelation among the extent of wear, ds and D_0,1 is realized. Frictional force (F_T) and its adhesive component (F_A) increase when CB is substituted by NC up to 15 phr. When NC fraction exceeds 15 phr, both F_T and F_A decrease substantially. This is attributed to the lubricity offered by the modified NC at higher NC concentration, which is explained using a predictive mechanism.     

All-solid secondary polyaniline-zinc battery

A solid state battery based on polyaniline (PANI), Zinc (Zn) and a gel polymer electrolyte (GPE) is reported for the first time. Poly (ethylene oxide)–zinc sulphate-nanoclay-H₂O based GPE was used as the separator. The GPEs with a varying composition of salt were evaluated for their electrochemical performance. The highest conductivity at ambient temperature for the GPEs was found to be 5.54 × 10⁻⁴ S cm⁻¹. Cyclic voltammetry and impedance studies, with the Zn/GPE/Zn cell, showed reversibility with respect to Zn/Zn²⁺ couple. The battery showed a capacity of 43.9 Ah kg⁻¹ of PANI and a coulombic efficiency higher than 100%. However, a decrease in capacity was observed for the system during the cycling.     

Lake Water-Level fluctuations forecasting using Minimax Probability Machine Regression,Relevance Vector Machine,Gaussian Process Regression,and Extreme Learning Machine

Forecasting freshwater lake levels is vital information for water resource management, including water supply management, shoreline management, hydropower generation optimization, and flood management. This study presents a novel application of four advanced artificial intelligence models namely the Minimax Probability Machine Regression (MPMR), Relevance Vector Machine (RVM), Gaussian Process Regression (GPR) and Extreme Learning Machine (ELM) for forecasting lake level fluctuation in Lake Huron utilizing historical datasets. The MPMR is a probabilistic framework that employed Mercer Kernels to achieve nonlinear regression models. The GPR, which is a probabilistic technique used tractable Bayesian framework for generalization of multivariate distribution of input samples to vast dimensional space. The ELM is a capable algorithm-based model for the implementation of the single-layer feed-forward neural network. The RVM demonstrate depends on the specification of the Bayesian method on a linear model with proper preceding that results in demonstration of sparse. The recommended techniques were tested to evaluate the current lake water-level trend monthly from the historical datasets at four previous time steps. The Lake Huron levels from 1918 to 1993 was managed for the training phase, and the rest of data (from 1994 to 2013) was used for testing. Considering the monthly and annually previous time steps, six models were introduced and found that the best results are achieved for a model with (t-1, t-2, t-3, t-12) as input combinations. The results show that all models can forecast the lake levels precisely. The results of this research study exhibit that the MPMR model (R²?=?0.984; MAE?=?0.035; RMSE?=?0.044; E_NS?=?0.984; D_Refined?=?0.995; E_LM?=?0.874) found to be more precise in lake level forecasting. The MPMR can be utilized as a practical computational tool on current and future planning with sustainable management of water resource of Lake Michigan-Huron.