ABSTRACT
Extensive investigations were made and empirical relations were proposed for the thermal conductivity of mono-nanofluids. The effect of concentration, diameter, and thermal properties of participating nanoparticles is missing in the majority of existing thermal conductivity models. An attempt is made to propose a model that considers the influence of such missing parameters on the thermal conductivity of hybrid nanofluids. Al2O3-TiO2 hybrid nanofluids have a 0.1% particle volume concentration prepared with distinct particle volume ratios (k - 1:6 - k, k = 1 to 6) in DI water. The samples were characterized, and the size and shape of the nanoparticles were verified. Also, the influence of varying particle volume ratios and the fluid temperature (varying from 283 to 308 K) were examined. 2.4 and 2.1% enhancements were observed in the thermal conductivity of alumina (5:0) and titania (0:5) nanofluids (having 0.1% volume concentration), respectively. Due to the low thermal conductivity of titania nanoparticles, the conductivity of the hybrid solution is above that of titania and below that of alumina nanofluids. An empirical relation for the thermal conductivity of hybrid nanofluids is established and validated considering the individual particle size, volume ratio, and thermal conductivity of particles.
ABSTRACT
Intracerebral hemorrhage (ICH) is the most common type of hemorrhagic stroke which occurs due to ruptures of weakened blood vessel in brain tissue. It is a serious medical emergency issues that needs immediate treatment. Large numbers of noncontrast-computed tomography (NCCT) brain images are analyzed manually by radiologists to diagnose the hemorrhagic stroke, which is a difficult and time-consuming process. In this study, we propose an automated transfer deep learning method that combines ResNet-50 and dense layer for accurate prediction of intracranial hemorrhage on NCCT brain images. A total of 1164 NCCT brain images were collected from 62 patients with hemorrhagic stroke from Kalinga Institute of Medical Science, Bhubaneswar and used for evaluating the model. The proposed model takes individual CT images as input and classifies them as hemorrhagic or normal. This deep transfer learning approach reached 99.6% accuracy, 99.7% specificity, and 99.4% sensitivity which are better results than that of ResNet-50 only. It is evident that the deep transfer learning model has advantages for automatic diagnosis of hemorrhagic stroke and has the potential to be used as a clinical decision support tool to assist radiologists in stroke diagnosis.
Subject(s)
Deep Learning , Hemorrhagic Stroke , Stroke , Cerebral Hemorrhage/diagnostic imaging , Humans , Intracranial Hemorrhages/diagnostic imaging , Stroke/diagnostic imaging , Tomography, X-Ray Computed/methodsABSTRACT
Novel hybrid (organic/inorganic) electrospun nanocomposite polymer blend electrolyte fibrous membranes with the composition poly(vinylidene difluoride-co-hexafluoropropylene) [P(VdF-co-HFP)]/poly(methyl methacrylate) [P(MMA)]/magnesium aluminate (MgAl2O4)/LiPF6 were prepared by the electrospinning technique. All of the prepared electrospun P(VdF-co-HFP), PMMA blend [90% P(VdF-co-HFP)/10% PMMA], and nanocomposite polymer blend [90% P(VdF-co-HFP)/10% PMMA/x wt % MgAl2O4 (x = 2, 4, 6, and 8)] fibrous membranes were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, differential scanning calorimetry, and scanning electron microscopy. The fibrous nanocomposite separator-cum-polymer blend electrolyte membranes were obtained by soaking the nanocomposite polymer blend membranes in an electrolyte solution containing 1 M LiPF6 in ethylene carbonate (EC)/diethyl carbonate (DEC) (1:1, v/v). The newly developed fibrous nanocomposite polymer blend electrolyte [90% P(VdF-co-HFP)/10% PMMA/6 wt % MgAl2O4/LiPF6] membrane showed a low crystallinity, low average fiber diameter, high thermal stability, high electrolyte uptake, high conductivity (2.60 × 10(-3) S cm(-1)) at room temperature, and good potential stability above 4.5 V. The best properties of the fibrous nanocomposite polymer blend electrolyte (NCPBE) membrane with a 6 wt % MgAl2O4 filler content was used for the fabrication of a Li/NCPBE/LiCoO2 CR 2032 coin cell. The electrochemical performance of the fabricated CR 2032 cell was evaluated at a current density of 0.1 C-rate. The fabricated CR 2032 cell lithium battery using the newly developed NCPBE membrane delivered an initial discharge capacity of 166 mAh g(-1) and a stable cycle performance.
ABSTRACT
A simple and rapid preparative high-performance liquid chromatography (HPLC) method has been developed to isolate and characterize some minor impurities of astaxanthin using a normal-phase Lichrosorb silica column with n-hexane-acetone-tetrahydrofuran (90:2:8, v/v/v) as mobile-phase and detection at 475 nm. The isolated impurities were characterized as astacene, dehydro astacene and apoastaxanthinal by UV-vis, ESI-MS, 1H and 13C NMR spectroscopy and the molecular structures were assigned. The impurities collected using the developed conditions were over 98% pure.
