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A Unique Interactive Nanostructure Knitting based Passive Sampler Adsorbent for Monitoring of Hg(2+) in Water
This work reports the development of ultralight interwoven ultrathin graphitic carbon nitride (g-CN) nanosheets for use as a potential adsorbent in a passive sampler (PAS) designed to bind Hg(2+) ions. The g-CN nanosheets were prepared from bulk g-CN synthesised via a modified high-temperature short...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696128/ https://www.ncbi.nlm.nih.gov/pubmed/31387298 http://dx.doi.org/10.3390/s19153432 |
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author | Chouhan, Raghuraj S. Žitko, Gregor Fajon, Vesna Živković, Igor Pavlin, Majda Berisha, Sabina Jerman, Ivan Vesel, Alenka Horvat, Milena |
author_facet | Chouhan, Raghuraj S. Žitko, Gregor Fajon, Vesna Živković, Igor Pavlin, Majda Berisha, Sabina Jerman, Ivan Vesel, Alenka Horvat, Milena |
author_sort | Chouhan, Raghuraj S. |
collection | PubMed |
description | This work reports the development of ultralight interwoven ultrathin graphitic carbon nitride (g-CN) nanosheets for use as a potential adsorbent in a passive sampler (PAS) designed to bind Hg(2+) ions. The g-CN nanosheets were prepared from bulk g-CN synthesised via a modified high-temperature short-time (HTST) polycondensation process. The crystal structure, surface functional groups, and morphology of the g-CN nanosheets were characterised using a battery of instruments. The results confirmed that the as-synthesized product is composed of few-layered nanosheets. The adsorption efficiency of g-CN for binding Hg(2+) (100 ng mL(−1)) in sea, river, rain, and Milli-Q quality water was 89%, 93%, 97%, and 100%, respectively, at natural pH. Interference studies found that the cations tested (Co(2+), Ca(2+), Zn(2+), Fe(2+), Mn(2+), Ni(2+), Bi(3+), Na(+), and K(+)) had no significant effect on the adsorption efficiency of Hg(2+). Different parameters were optimised to improve the performance of g-CN such as pH, contact time, and amount of adsorbent. Optimum conditions were pH 7, 120 min incubation time and 10 mg of nanosheets. The yield of nanosheets was 72.5%, which is higher compared to other polycondensation processes using different monomers. The g-CN sheets could also be regenerated up to eight times with only a 20% loss in binding efficiency. Overall, nano-knitted g-CN is a promising low-cost green adsorbent for use in passive samplers or as a transducing material in sensor applications. |
format | Online Article Text |
id | pubmed-6696128 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-66961282019-09-05 A Unique Interactive Nanostructure Knitting based Passive Sampler Adsorbent for Monitoring of Hg(2+) in Water Chouhan, Raghuraj S. Žitko, Gregor Fajon, Vesna Živković, Igor Pavlin, Majda Berisha, Sabina Jerman, Ivan Vesel, Alenka Horvat, Milena Sensors (Basel) Article This work reports the development of ultralight interwoven ultrathin graphitic carbon nitride (g-CN) nanosheets for use as a potential adsorbent in a passive sampler (PAS) designed to bind Hg(2+) ions. The g-CN nanosheets were prepared from bulk g-CN synthesised via a modified high-temperature short-time (HTST) polycondensation process. The crystal structure, surface functional groups, and morphology of the g-CN nanosheets were characterised using a battery of instruments. The results confirmed that the as-synthesized product is composed of few-layered nanosheets. The adsorption efficiency of g-CN for binding Hg(2+) (100 ng mL(−1)) in sea, river, rain, and Milli-Q quality water was 89%, 93%, 97%, and 100%, respectively, at natural pH. Interference studies found that the cations tested (Co(2+), Ca(2+), Zn(2+), Fe(2+), Mn(2+), Ni(2+), Bi(3+), Na(+), and K(+)) had no significant effect on the adsorption efficiency of Hg(2+). Different parameters were optimised to improve the performance of g-CN such as pH, contact time, and amount of adsorbent. Optimum conditions were pH 7, 120 min incubation time and 10 mg of nanosheets. The yield of nanosheets was 72.5%, which is higher compared to other polycondensation processes using different monomers. The g-CN sheets could also be regenerated up to eight times with only a 20% loss in binding efficiency. Overall, nano-knitted g-CN is a promising low-cost green adsorbent for use in passive samplers or as a transducing material in sensor applications. MDPI 2019-08-05 /pmc/articles/PMC6696128/ /pubmed/31387298 http://dx.doi.org/10.3390/s19153432 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chouhan, Raghuraj S. Žitko, Gregor Fajon, Vesna Živković, Igor Pavlin, Majda Berisha, Sabina Jerman, Ivan Vesel, Alenka Horvat, Milena A Unique Interactive Nanostructure Knitting based Passive Sampler Adsorbent for Monitoring of Hg(2+) in Water |
title | A Unique Interactive Nanostructure Knitting based Passive Sampler Adsorbent for Monitoring of Hg(2+) in Water |
title_full | A Unique Interactive Nanostructure Knitting based Passive Sampler Adsorbent for Monitoring of Hg(2+) in Water |
title_fullStr | A Unique Interactive Nanostructure Knitting based Passive Sampler Adsorbent for Monitoring of Hg(2+) in Water |
title_full_unstemmed | A Unique Interactive Nanostructure Knitting based Passive Sampler Adsorbent for Monitoring of Hg(2+) in Water |
title_short | A Unique Interactive Nanostructure Knitting based Passive Sampler Adsorbent for Monitoring of Hg(2+) in Water |
title_sort | unique interactive nanostructure knitting based passive sampler adsorbent for monitoring of hg(2+) in water |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696128/ https://www.ncbi.nlm.nih.gov/pubmed/31387298 http://dx.doi.org/10.3390/s19153432 |
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