Abstract The effect of dichlorocarbene modified styrene butadiene rubber (DCSBR) as a compatibilizer in blends of acrylonitrile butadiene rubber and styrene butadiene rubber (SBR/NBR) has been studied. The cure characteristics, physical properties, crosslink density, FTIR studies and low temperature transitions of the blends were determined as a function of chlorine content of DCSBR. Compatibilizing efficiency depended on the chlorine content of DCSBR and composition of blend constituents. Effective compatibilization was achieved when chlorine content of compatibilizer was 25% and SBR content of blend was either 50% or lower. FTIR studies, glass transition behavior measured by DSC and DMA showed that an appreciable extent of molecular level miscibility has been achieved in SBR/NBR blends by using DCSBR as a compatibilizer. Improvement in mechanical properties such as tensile strength, tear strength, resilience, hardness and compression set were achieved both when DCSBR was added and chlorine content of DCSBR increased up to 25%. The resistance of the vulcanizate towards air and oil aging improved with compatibilization. The change in technological properties correlated with crosslink density of the blends assessed from swelling studies and stress-strain data.
Natural fibers available plenty can be used as reinforcements in development of eco friendly polymer composites. The less utilized palm leaf stalk fibers sandwiched with artificial glass fibers was researched in this work to have a better reinforcement in preparing a green composite. The commercially available polyester resin blend with coconut shell filler in nano form was used as matrix to sandwich these composites. Naturally available Fibers of palm leaf stalk, coconut leaf stalk, raffia and oil palm were extracted and treated with potassium permanganate solution which enhances the properties. For experimentation four different plates were fabricated using these fibers adopting hand lay-up method. These sandwiched composite plates are further machined to obtain ASTM standards Specimens which are mechanically tested as per standards. Experimental results reveal that the alkali treated palm leaf stalk fiber based polymer composite shows appreciable results than the others. Hence the developed composite can be recommended for fabrication of automobile parts.
This article highlights the development of an eco friendly polymer composite with natural fibers as reinforcement. The natural fibers are taken from palm tree namely; palm leaf stalk portion. It was sandwiched with artificially available glass fibers. The commercially available polyester resin blend with coconut shell filler in nano form was used as matrix to sandwich these composites. Portions of the extracted palm leaf stalk fibers are treated with potassium permanganate solution to enhance their properties while the remaining fibers are kept raw. For experimentation two set of plates one as untreated and other as alkali treated were fabricated using hand lay-up method. Specimens cut from the sandwiched composite plates were mechanically tested according to the ASTM standards. Experimental results reveal that the alkali treated palm leaf stalk fiber based polymer composite shows appreciable results than the untreated one. SEM analysis explores fewer voids for treated fibers. Hence the developed composite can be recommended for fabrication of automobile parts.
Parkinson's disease is a neural degenerative disease, in which the patients' faces various critical neurological disorders.Thus, the earlier prediction of PD helps to enhance the patients' life.The prediction of PD in the earlier stage is extremely complex and it consumes huge time.Therefore, effectual and appropriate prediction of PD is measured to a challenging factor for the health care experts and practitioners.To deal with this issue and to accurately predict the PD in earlier stage, this work concentrates on machine learning approaches for designing a predictor system.For developing the anticipated model, L1-norm based Genetic algorithm (L1-GA) is applied for predicting PD in the earlier stage.This L1-GA is utilized for selecting the highly related features for accurate classification of Parkinson disease.This L1-GA produces newer feature subset from PD dataset based on its feature weight value.For validation purpose, this work considers 10-fold cross validation (CV) is used.Also, metrics like sensitivity, accuracy, precision, specificity, F1 score and execution time are evaluated.The inputs are taken from The PD dataset which is available online for preceding the feature selection process.The optimal accuracy attained with these newly selected sub-sets are considered for further computation.The experimental findings determine that this study recommends that L1-GA provides better contribution towards PD feature selection and to predict PD in earlier stage.In recent times, Clinical Decision Support System (CDSS) plays an essential role for assisting PD recognition.As well, the anticipated model lays a bridge to fill the gap encountered in feature selection using the available data.The anticipated model gives better trade-off in contrast to prevailing approaches.
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