35 research outputs found
Magnesium based alloys for reinforcing biopolymer composites and coatings: A critical overview on biomedical materials
No full textMagnesium (Mg) & its alloys are favourable for orthopaedic & cardiovascular medical device fabrication applications, but holds a natural ability to degrade biologically when put with aqueous solution of the substances and/or water-saturated tissue in the context of a living organism. Mg alloys nature to corrode inside the living organism body is mainly attributed to the excessive rates of corrosion of Mg. Poor corrosion resistance possessed by Mg decreases the mechanical properties of the implants, and adds toxic effects on the bone metabolism. A potential method for increasing Mg alloy resistance to corrosion without changing its properties is by the protective polymeric deposit coatings. Moreover, to impart better mechanical and biocompatible aspects to Mg based materials biopolymers have been used as a composite constituent. This review is based on such composite materials constituting Mg and biopolymers. Their resulting favourable mechanical and osteopromotive properties in conjunction with biocompatibility may help the clinicians to fix the existing orthopaedic related issues
The effect of STW defects on the mechanical properties and fracture toughness of pristine and hydrogenated graphene
No full textGraphene is emerging as a versatile material with a diverse field of applications.</p
Molecular dynamics based simulations to study failure morphology of hydroxyl and epoxide functionalised graphene
No full textMolecular dynamics based simulations to study the fracture strength of monolayer graphene oxide
No full textMechanical, Microstructural and Thermal Characterization of Epoxy-Based Human Hair–Reinforced Composites
No full textDetection of Atmospheric Gravity Waves: Two classification approach - Image classification and meteorological feature classification
No full textWith the advent of offshore wind farms, the research into the various phenomenon that affects their performance is vast and detailed. But the effect of a particular phenomenon, atmospheric gravity waves (AGWs), on wind farm performance is limited. AGWs are oscillations of the airflow due to an imbalance in the buoyancy and gravity forces, generated by topographical or meteorological obstacles in neutral or stable surface atmospheric conditions. AGWs are frequent over offshore regions and affect offshore wind farms as the event occurs over a large area. Detecting them through satellite images is easy by an eye test, but not so much when viewed digitally through meteorological data. Weather data can be obtained from reanalysis data which combines past weather forecasts with observational data assimilation. This project aims to develop machine learning models that detect AGWs in satellite images and detect AGWs from atmospheric conditions, such as temperature and wind speed profile with height. The models learn using the reanalysis data and satellite images. The same satellite images are used to label the reanalysis data so that the model is taught to pick out gravity waves in the case of having no satellite image. Thus the final objective of the project is to train a model to detect an AGW event, based solely on reanalysis data. The trained model is then used to predict the percentage of time an AGW occurs or will occur over a chosen wind farm site.Electrical Engineering | Sustainable Energy Technolog
Cloud correction of Sentinel-2 NDVI using S2cloudless package
No full textOptical satellite-derived Normalized Difference Vegetation Index (NDVI) is by far the most commonly used vegetation index value for crop monitoring. However, it is quite sensitive to the cloud, and cloud shadows and significantly decreases its usability, especially in agricultural applications. Therefore, an accurate and reliable cloud correction method is mandatory for its effective application. To address this issue, we have developed an approach to correct the NDVI values of each and every pixel of the image captured by the Sentinel-2A satellite of the European Space Agency's Copernicus Program. The Chhattisgarh region of India was selected to analyze the variation in NDVI value. The NDVI value of each pixel shows a slight decrease in the value because of clouds. Cloud probability was calculated using the S2cloudless package provided by Sentinel Hub. The cloud probability of a pixel implies how densely the cloud is present over that pixel, thus inversely affecting the NDVI values. To understand the relationship between cloud probability and NDVI, we did a pixel-to-pixel (>25 million pixels) comparison between clouded and non-clouded NDVI Images on two consecutive dates in June 2021. Our analysis shows that an increase in 0.1 Units of cloud probability corresponds to a decrease in 0.2668 units of NDVI value. We further validated this finding on images captured during the months of August and September 2021. The validation was done on more than 35k square meters of actual farm fields and the results indicate that the proposed method shows more than 60% improvement in the cloud-affected NDVI images
Experimental Analysis on Carbon Residuum Transformed Epoxy Resin: Chicken Feather Fiber Hybrid Composite
No full textEffect of grain boundaries on the interfacial behaviour of graphene-polyethylene nanocomposite
Get PDFAim of this article was to investigate the effect of grain boundaries on the interfacial properties of bi-crystalline graphene/polyethylene based nanocomposites. Molecular dynamics based atomistic simulations were performed in conjunction with the reactive force field parameters to capture atomic interactions within graphene and polyethylene atoms, whereas non-bonded interactions were considered for the interfacial properties. Atoms at the higher energy state in bi-crystalline graphene helps in improving the interaction at the nanocomposite interphase. Geometrical imperfections such as wrinkles and ripples helps the bi-crystalline graphene in increasing the number of adhesion points between the nanofiller and matrix, which eventually improves the strength and toughness of nanocomposite. These outcomes will help in opening new opportunities for defective nanofillers in the development of nanocomposites for future applications
