Conceptual design of a novel hybrid fuel cell/desalination system

A novel concept for integrating fuel cells with desalination systems is proposed and investigated in this work. Two unique case studies are discussed — the first involving a hybrid system with a reverse osmosis (RO) unit and the second — integrating with a thermal desalination process such as multi-stage flash (MSF). The underlying motivation for this system integration is that the exhaust gas from a hybrid power plant (fuel cell/turbine system) contains considerable amount of thermal energy, which may be utilized for desalination units. This exhaust heat can be suitably used for preheating the feed in desalination processes such as reverse osmosis which not only increases the potable water production, but also decreases the relative energy consumption by approximately 8% when there is an increase of just 8°C rise in temperature. Additionally, an attractive hybrid system application which combines power generation at 70%+ system efficiency with efficient waste heat utilization is thermal desalination. In this work, it is shown that the system efficiency can be raised appreciably when a high-temperature fuel cell co-generates DC power in-situ with waste heat suitable for MSF. Results indicate that such hybrid system could show a 5.6% increase in global efficiency. Such combined hybrid systems have overall system efficiencies (second-law base) exceeding those of either fuel-cell power plants or traditional desalination plants.     

Cognitive-Mathematical Approaches for Evaluating Architectural Contextual Fit

The main goal of this study is to apply a scientific quantitative approach to the investigation of contextual fit. This is approached mathematically within the framework of cognitive science and research on categorization and prototypes. Two experiments investigated two leading mathematical-cognitive approaches for explaining people’s judgment of contextual fit of a new building with an architectural/urban context: prototype approach and feature frequency approach. The basic concept is that people represent the built environment via architectural prototypes and/or frequencies of encountered architectural features. In the first experiment, a group of twelve participants performed rank order tasks on artificially created architectural patterns, for the purpose of psychological scaling. Perceptual distances among all patterns were mathematically determined. In the second experiment, three groups of architectural patterns were constructed to represent assumed architectural contexts. The prototype of each context was mathematically determined according to prototype cognitive model, and based on the distances calculated in the first experiment. Fifty-six students participated in the main experiment, in which they rank ordered a group of fifteen architectural patterns in terms of contextual fit to each of the three architectural contexts. Participants’ rank order data of the fifteen patterns were regressed on both the perceptual distances from prototypes, and numbers of features shared with each architectural context. Results indicated that both prototype and feature frequency approaches significantly accounted for important portions of participants’ judgments. However, participants tended to prefer one approach to the other according to context composition. Results have implications for both research on utilizing cognitive-mathematical models in architectural research and on urban design guidelines and control.     

Nutratherapeutics approach against cancer: tomato‐mediated synthesised gold nanoparticles

In this study, an eco‐friendly biosynthesis of stable gold nanoparticles (T‐GNPs) was carried out using different concentrations of tomato juice (nutraceuticals) as a reducing agent and tetrachloroauric acid as a metal precursor to explore their potential application in cancer therapeutics. The synthesis of T‐GNPs was monitored by UV‐visible absorption spectroscopy, which unveiled their formation by exhibiting the typical surface plasmon absorption maxima at 522 nm. The size of T‐GNPs was found to be 10.86 ± 0.6 nm. T‐GNPs were characterised by dynamic light scattering, zeta potential, transmission electron microscopy analysis and Fourier transform infrared spectroscopy. T‐GNPs were further investigated for their anti‐cancer activity against human lung carcinoma cell line (A ₅₄₉) and human cervical cancer cell line wherein the IC₅₀ values were found to be 0.286 and 0.200 mM, respectively. T‐GNPs inhibited the growth of cancer cells by generating ROS and inducing apoptosis. T‐GNPs were found highly effective by virtue of their size, metallic property and capping molecules. Thus, this study opens up the prospects of using nutraceutical (tomato juice) as nutratherapeutic agent (T‐GNPs) against critical diseases like lung cancer and cervical cancer.Inspec keywords: gold, nanoparticles, particle size, cancer, ultraviolet spectra, visible spectra, electrokinetic effects, transmission electron microscopy, Fourier transform infrared spectra, cellular biophysics, spectrochemical analysis, nanomedicine, nanofabricationOther keywords: tomato‐mediated synthesised gold nanoparticles, tomato juice, reducing agent, tetrachloroauric acid, cancer therapeutics, UV‐visible absorption spectroscopy, surface plasmon absorption, dynamic light scattering, zeta potential, transmission electron microscopy analysis, Fourier transform infrared spectroscopy, human lung carcinoma cell line, anticancer activity, human cervical cancer cell line, nutratherapeutic agent, lung cancer, Au     

Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

The Internet of Medical Things (IoMT) emerges with the vision of the Wireless Body Sensor Network (WBSN) to improve the health monitoring systems and has an enormous impact on the healthcare system for recognizing the levels of risk/severity factors (premature diagnosis, treatment, and supervision of chronic disease i.e., cancer) via wearable/electronic health sensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy plays a very significant role in the detection of gastric cancer. Convolutional Neural Network (CNN) has been widely used to diagnose gastric cancer based on various feature extraction models, consequently, limiting the identification and categorization performance in terms of cancerous stages and grades associated with each type of gastric cancer. This paper proposed an optimized AI-based approach to diagnose and assess the risk factor of gastric cancer based on its type, stage, and grade in the endoscopic images for smart healthcare applications. The proposed method is categorized into five phases such as image pre-processing, Four-Dimensional (4D) image conversion, image segmentation, K-Nearest Neighbour (K-NN) classification, and multi-grading and staging of image intensities. Moreover, the performance of the proposed method has experimented on two different datasets consisting of color and black and white endoscopic images. The simulation results verified that the proposed approach is capable of perceiving gastric cancer with 88.09% sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.     

Development and Validation of a Simple Reversed‐Phase HPLC‐UV Method for Determination of Malondialdehyde in Olive Oil

A simple, precise, accurate and selective method was developed and validated for determination of malondialdehyde (MDA) in olive oil. Separation was achieved on a reversed‐phase C₈ column using a mobile phase consisting of methanol/0.8 % phosphoric acid (10:90, v/v), at a flow rate of 1.0 ml/min and UV detection at 220 nm. This method was validated according to the requirements for new methods, which include accuracy, precision, selectivity, robustness, a limit of detection, limit of quantitation (LOQ), linearity and range. The current method demonstrated good linearity over the range of 0.5–1000 ppm of MDA with r² greater than 0.999. The recovery of MDA in olive oil ranged from 97.1 to 99.1 %. The method was selective where MDA was distinctly separated from other compounds of the oil with good resolution. The method was also precise where the RSD of the peak areas of replicate injections of MDA standard solution were less than 1 %. The degree of reproducibility of the results obtained as a result of small deliberate variations in the method parameters and by changing the analytical operators proved that the method is robust and rugged. The low LOQ of MDA (0.5 ppm) using this method enables quantitation of MDA at low concentration.     

Development and test of a new catalytic converter for natural gas fuelled engine

This paper presents characteristics of a new catalytic converter (catco) to be used for natural gas fuelled engine. The catco were developed based on catalyst materials consisting of metal oxides such as titanium dioxide (TiO₂) and cobalt oxide (CoO) with wire mesh substrate. Both of the catalyst materials (such as TiO₂ and CoO) are inexpensive in comparison with conventional catalysts (noble metals) such as palladium or platinum. In addition, the noble metals such as platinum group metals are now identified as human health risk due to their rapid emissions in the environment from various resources like conventional catalytic converter, jewelers and other medical usages. It can be mentioned that the TiO₂/CoO based catalytic converter and a new natural gas engine such as compressed natural gas (CNG) direct injection (DI) engine were developed under a research collaboration program. The original engine manufacture catalytic conveter (OEM catco) was tested for comparison purposes. The OEM catco was based on noble metal catalyst with honeycomb ceramic substrate. It is experimentally found that the conversion efficiencies of TiO₂/CoO based catalytic converter are 93%, 89% and 82% for NO_x, CO and HC emissions respectively. It is calculated that the TiO₂/CoO based catalytic converter reduces 24%, 41% and 40% higher NO_x, CO and HC emissions in comparison to OEM catco respectively. The objective of this paper is to develop a low-cost three way catalytic converter to be used with the newly developed CNG-DI engine. Detailed review on catalytic converter, low-cost catalytic converter development characteristics and CNGDI engine test results have been presented with discussions.     

Comparative study of the effects of chlorinated polyethylene and acrylic impact modifier on the thermal degradation of poly(vinyl chloride) compounds and poly(vinyl chloride)/(oil palm empty fruit bunch) composites

The effects of chlorinated polyethylene (CPE) and acrylic impact modifier (AIM) on the thermal degradation of poly(vinyl chloride) (PVC) compounds and composites were investigated. The amounts of AIM and CPE used were fixed at 9 parts per hundred parts of resin (phr), while oil palm empty fruit bunch (OPEFB) fiber content was increased from 0 to 40 phr. To produce composites, the PVC formulations were dry‐blended by using a laboratory blender before being milled into sheets on a two‐roll mill at 165°C. The milled sheets were then hot‐pressed at 180°C. The thermal degradation of the specimens was evaluated by using thermogravimetry in a nitrogen environment. Thermal stability of the PVC/CPE compounds and PVC/CPE/OPEFB composites was improved by the addition of CPE. The CPE retarded the dehydrochlorination of PVC. However, the stabilization effect was reduced by the incorporation of OPEFB at levels of 30 and 40 phr. The presence of AIM accelerated the dehydrochlorination of PVC/AIM compounds and PVC/AIM/OPEFB composites. J. VINYL ADDIT. TECHNOL., 2010. © 2010 Society of Plastics Engineers     

Formulation Optimization of a Palm-Based Nanoemulsion System Containing Levodopa

Response surface methodology (RSM) was utilized to investigate the influence of the main emulsion composition; mixture of palm and medium-chain triglyceride (MCT) oil (6%–12% w/w), lecithin (1%–3% w/w), and Cremophor EL (0.5%–1.5% w/w) as well as the preparation method; addition rate (2–20 mL/min), on the physicochemical properties of palm-based nanoemulsions. The response variables were the three main emulsion properties; particle size, zeta potential and polydispersity index. Optimization of the four independent variables was carried out to obtain an optimum level palm-based nanoemulsion with desirable characteristics. The response surface analysis showed that the variation in the three responses could be depicted as a quadratic function of the main composition of the emulsion and the preparation method. The experimental data could be fitted sufficiently well into a second-order polynomial model. The optimized formulation was stable for six months at 4 °C.