This is the unspecified version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractIn this work, the design and characteristics of a wavelength-dependent pH optical sensor have been studied. To create the sensor itself, brilliant yellow (BY) as a pH indicator and poly (allylamine hydrochloride) [PAH] as a cross-linker have been deposited on the end of a bare silica core of an optical fibre by use of a 'layer-by-layer' technique. In the experiments carried out to characterize the sensor, it was observed that the value of pK a (the dissociation constant) of the thin film is dependent both on the outer layer and the number of bilayers. A heat treatment process was applied to the sensor to reduce the effect on the deposited layers during the testing of the probe. As a result of these series of experiments, it could be concluded that the probe design on which were deposited structured layers comprising six double layers of (PAH/BY) showed the best sensitivity for a pH range from 6.80 to 9.00 (with an accuracy of ±0.20) and showing an average wavelength shift of 4.65 nm per 0.2 pH units, while the concentration of the BY and the PAH solutions was maintained as 0.25mM and 2.5mM respectively.
This is the accepted version of the paper.This version of the publication may differ from the final published version. The results obtained from a series of evaluations show that the sensitivity was enhanced by reducing the concentration of the indicator solution used and by designing a U-bend configuration sensor probe with a sharply bent fibre. However, when making an overall comparison, the straight (unbent) fibre probe resulted in a more sensitive probe when compared to the use of a high radius bend. Further, utilizing a small core diameter of the fibre allows a wide pH range to be measured and with high sensitivity. Additionally, the performance was shown to be improved for measurements over a narrower range of pH, by using a fibre with a larger core diameter. Considering the effect of the number of layers, work carried out has shown that probes with 5-6 bilayers presented the best performance. The sensitivity has been shown to diminish when more than 6 layers were used and the sensing range shifts towards higher pH values. When monitored, the value of pKa (the dissociation constant) of the thin film showed the smallest change of any of the design factors considered. In summary, using a larger core diameter, employing a larger curve radius, a higher number of bilayers, a higher concentration of the indicator solution and applying PAH as an outer layer, all cause a higher pKa value and consequently the probe sensitivity moves towards alkaline region. Permanent repository link
A mechanistic model is developed to simulate ethanol purification using membrane technology. In the considered process, a feed solution containing 10 wt% water + 90 wt% ethanol is contacted with a polymeric dense membrane in a pervaporation system. The membrane selectively separates water from solution in order to purify the ethanol. In the development of the model, it is assumed that the water is the main penetrant through the membrane due to the hydrophilicity of membrane material. The mass fraction of water molecules in the feed solution, as well as membrane, is estimated using Maxwell–Stefan approach. The governing equations are then solved using finite element method in order to predict mass fraction, mass transfer flux, and velocity of the solution in the membrane module. The results indicate that the model can predict the formation of concentration and velocity boundary layer in the feed solution near the membrane/feed interface. Moreover, the developed model is robust and reliable in the understanding of membrane separation processes applicable for dehydration of alcohols.
A comprehensive computational fluid dynamics simulation was developed for the rational design of a bioethanol purification system using a pervaporation process by tailoring the hydrodynamics of the process. The process involves the removal of water from a water/ethanol liquid mixture using a dense polymeric membrane. The model domain was divided into two compartments comprising the feed and the membrane. To describe water transport in the feed solution, the Maxwell-Stefan approach was used, whereas for mass transfer inside the membrane the molecular diffusion mechanism was adopted. The governing equations were solved numerically by using a finite element method. The model was capable of predicting mass transfer along with momentum transfer in the feed and membrane compartments.
The present study introduces a QSPR model to predict the flash point of pure organic compounds from diverse chemical families. We used the Maximum-Relevance Minimum-Redundancy (MRMR) as an efficient descriptor selection algorithm to select 20 the most effective out of 1926 calculated descriptors. The selected descriptors and their combination with the normal boiling point data were used as model inputs and their correlation with FP was mapped using feedforward artificial neural networks. Study-ing various models, the best result was obtained by a neural network with 2 neurons in the hidden layer for which a combination of the selected descriptors and normal boiling point data were used as model inputs. Evaluating the performance of this model for a dataset of 727 compounds resulted in average absolute relative errors of of 1.36 %, 1.34 %, 1.44 % and 1.42 % and average absolute deviations of 4.48, 4.41, 4.75 and 4.66 K for the overall, training, validation, and test datasets, respectively.
This paper experimentally investigates the conductive heat transfer of samples with different materials and coatings. A range of graphene oxide nanoparticle concentration has been employed. Results demonstrate that utilizing nanoparticles leads to enhancement of conductive heat transfer by 10.07% and 8.01% for EK2 and ST14 samples, respectively. The aforementioned nanoparticles also reduce coating thickness and yield an enhanced quality of the surface, in terms of mechanical properties. The convective and radiative methods of heat transfer have been ignored in this study. K E Y W O R D SCoating, Conduction, Enamel, Graphene oxide, Nanoparticles 1 | INTRODUCTION Nanotechnology and its fascinating capabilities have attracted abundant research funds and endeavors during the past six decades. The term "nanotechnology" was introduced for the first time by a most-prized physicist, Richard Feynman, in 1959, to focus more at the bottoms looking for more space, instead of current space explorations. However, Drexler 1 published the first book about nanotechnology in 1986. Nowadays, countless numbers of nanotechnology applications are known, and include, but are not limited to, health and medicine, 2,3 energy storage, 4-11 automotive, 12-16 oil and gas industries, 17-22 renewable energies, 23-25 lubrication, 26-28 manufacturing, 29-37 and heat and mass transfer applications. 13,21,[38][39][40][41][42][43][44][45][46][47][48][49][50][51][52] Graphene is a modern nanomaterial, which was discovered, isolated, and characterized by Novoselov et al 53 for the first time and now has numerous applications due to its promising mechanical, electrical, and thermal properties. 54,55 Graphene has a two-dimensional structure
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.