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Optimization of Carbon Nanotube Dispersions in Water Using Response Surface Methodology

[Image: see text] The aim of this work was to demonstrate an optimization methodology to reliably obtain stable macrodispersions (i.e., for ≥24 h) of carbon nanotubes in water using sonication. Response surface methodology (RSM) was utilized to assess and optimize the sonication parameters for the p...

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Autores principales: Zaib, Qammer, Ahmad, Farrukh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2019
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6648579/
https://www.ncbi.nlm.nih.gov/pubmed/31459363
http://dx.doi.org/10.1021/acsomega.8b02965
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author Zaib, Qammer
Ahmad, Farrukh
author_facet Zaib, Qammer
Ahmad, Farrukh
author_sort Zaib, Qammer
collection PubMed
description [Image: see text] The aim of this work was to demonstrate an optimization methodology to reliably obtain stable macrodispersions (i.e., for ≥24 h) of carbon nanotubes in water using sonication. Response surface methodology (RSM) was utilized to assess and optimize the sonication parameters for the process. The studied input parameters were (i) sonication time (duration), (ii) amplitude (of vibration), and (iii) pulse-on/off (duration) of the sonicator. The analyzed responses were mean diameter and size distribution of multiwalled carbon nanotube (MWNT) aggregates in water, which were measured by the dynamic light scattering technique. A semiempirical model was developed and statistically tested to estimate the magnitude of sonicator parameters required to obtain specified MWNT macrodispersions (i.e., aggregates’ mean diameter and distribution) in water. The results showed that MWNT aggregates of 2 ± 0.5 μm can be obtained by optimizing sonicator parameters to a sonication time of 89 s, amplitude of 144 μm, and pulse-on/off cycle of 44/30 s. These process settings for 100 mg/L MWNTs in a 30 mL aliquot of deionized water would consume 863 J/mL of sonication energy. Contrary to the popular belief, “sonication time” and/or “sonication energy input” were not found to be proportional to the degree of dispersion of MWNTs in water. This might be the reason for the frequent disparity and nonreproducibility of sonication results reported in scientific literature, especially for dispersing nanomaterials in a number of different systems. The amplitude of vibration was noted to be the most sensitive parameter affecting MWNT aggregates’ diameter and distribution in water. The characterization of MWNTs was performed using electron microscopy, surface area analyzer, thermogravimetric analyzer, and zeta potential analyzer. This study can be helpful in evaluating sonication dispersion of particulate matter in other incompressible fluids such as graphene dispersion in organic solvents.
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spelling pubmed-66485792019-08-27 Optimization of Carbon Nanotube Dispersions in Water Using Response Surface Methodology Zaib, Qammer Ahmad, Farrukh ACS Omega [Image: see text] The aim of this work was to demonstrate an optimization methodology to reliably obtain stable macrodispersions (i.e., for ≥24 h) of carbon nanotubes in water using sonication. Response surface methodology (RSM) was utilized to assess and optimize the sonication parameters for the process. The studied input parameters were (i) sonication time (duration), (ii) amplitude (of vibration), and (iii) pulse-on/off (duration) of the sonicator. The analyzed responses were mean diameter and size distribution of multiwalled carbon nanotube (MWNT) aggregates in water, which were measured by the dynamic light scattering technique. A semiempirical model was developed and statistically tested to estimate the magnitude of sonicator parameters required to obtain specified MWNT macrodispersions (i.e., aggregates’ mean diameter and distribution) in water. The results showed that MWNT aggregates of 2 ± 0.5 μm can be obtained by optimizing sonicator parameters to a sonication time of 89 s, amplitude of 144 μm, and pulse-on/off cycle of 44/30 s. These process settings for 100 mg/L MWNTs in a 30 mL aliquot of deionized water would consume 863 J/mL of sonication energy. Contrary to the popular belief, “sonication time” and/or “sonication energy input” were not found to be proportional to the degree of dispersion of MWNTs in water. This might be the reason for the frequent disparity and nonreproducibility of sonication results reported in scientific literature, especially for dispersing nanomaterials in a number of different systems. The amplitude of vibration was noted to be the most sensitive parameter affecting MWNT aggregates’ diameter and distribution in water. The characterization of MWNTs was performed using electron microscopy, surface area analyzer, thermogravimetric analyzer, and zeta potential analyzer. This study can be helpful in evaluating sonication dispersion of particulate matter in other incompressible fluids such as graphene dispersion in organic solvents. American Chemical Society 2019-01-10 /pmc/articles/PMC6648579/ /pubmed/31459363 http://dx.doi.org/10.1021/acsomega.8b02965 Text en Copyright © 2019 American Chemical Society This is an open access article published under an ACS AuthorChoice License (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
spellingShingle Zaib, Qammer
Ahmad, Farrukh
Optimization of Carbon Nanotube Dispersions in Water Using Response Surface Methodology
title Optimization of Carbon Nanotube Dispersions in Water Using Response Surface Methodology
title_full Optimization of Carbon Nanotube Dispersions in Water Using Response Surface Methodology
title_fullStr Optimization of Carbon Nanotube Dispersions in Water Using Response Surface Methodology
title_full_unstemmed Optimization of Carbon Nanotube Dispersions in Water Using Response Surface Methodology
title_short Optimization of Carbon Nanotube Dispersions in Water Using Response Surface Methodology
title_sort optimization of carbon nanotube dispersions in water using response surface methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6648579/
https://www.ncbi.nlm.nih.gov/pubmed/31459363
http://dx.doi.org/10.1021/acsomega.8b02965
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