Multivariable optimization of carbon nanoparticles synthesized from waste facial tissues by artificial neural networks, new material for downstream quenching of quantum dots
In this study, water-soluble carbon nanoparticles (CNPs) were synthesized by using waste facial tissue as a non-recyclable waste and the efficiency of CNPs in quenching mechanism of cadmium-telluride quantum dots (QDs) was investigated. In addition, CNPs synthesis was modeled by using artificial neu...
Gespeichert in:
| Veröffentlicht in: | Journal of materials science. Materials in electronics 2019-02, Vol.30 (3), p.3156-3165 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 3165 |
|---|---|
| container_issue | 3 |
| container_start_page | 3156 |
| container_title | Journal of materials science. Materials in electronics |
| container_volume | 30 |
| creator | Roodbar Shojaei, Taha Mohd Salleh, Mohamad Amran Mobli, Hossein Aghbashlo, Mortaza Tabatabaei, Meisam |
| description | In this study, water-soluble carbon nanoparticles (CNPs) were synthesized by using waste facial tissue as a non-recyclable waste and the efficiency of CNPs in quenching mechanism of cadmium-telluride quantum dots (QDs) was investigated. In addition, CNPs synthesis was modeled by using artificial neural networks (ANN). To find the optimum model, ANN was trained by using different algorithms. Then, the generated models were statistically assessed and subsequently, the capability of the selected model for predicting the mean diameter size of the nanoparticles was verified. Based on the results, the model GA-4-7-1 had the most optimal statistical characteristics. Furthermore, the most pronounced effect on mean diameter size was associated to HNO
3
concentration while temperature demonstrated the least influence. Moreover, the quenching study confirmed the capability of the synthesized CNPs in quenching QDs. |
| doi_str_mv | 10.1007/s10854-018-00595-0 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2162672983</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2162672983</sourcerecordid><originalsourceid>FETCH-LOGICAL-c363t-2f92c47898a3ed5cf606529a2754a119f92166c9fdc0c9ea4937a810bd74a68a3</originalsourceid><addsrcrecordid>eNp9kc9u1DAQxi0EEkvhBThZ4kpg7MRJfEQV_6RWXEDiZs06duuS2FuPw2r7Trwj3l0kbj3NaL7vNzPSx9hrAe8EwPCeBIyqa0CMDYDSqoEnbCPU0DbdKH8-ZRvQamg6JeVz9oLoDgD6rh037M_1OpfwG3PA7ex42pWwhAcsIUWePLeYt7WLGNMOcwl2dsTpEMuto_DgJu5zWvgeqTju0QaceQlEa3VtD_xI-HCaRrfmUyn7lH_R29rt-YLF5aPqU-ZT2kcq2eHC71cX7W2IN8cX7leMZV2qXugle-ZxJvfqX71gPz59_H75pbn69vnr5YerxrZ9WxrptbTdMOoRWzcp63voldQoB9WhELrKou-t9pMFqx12uh1wFLCdhg77Cl2wN-e9u5zqM1TMXVpzrCdNJWU_SD221SXPLpsTUXbe7HJYMB-MAHOMxZxjMTUWc4rFQIXaM0TVHG9c_r_6EeovrO2VrQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2162672983</pqid></control><display><type>article</type><title>Multivariable optimization of carbon nanoparticles synthesized from waste facial tissues by artificial neural networks, new material for downstream quenching of quantum dots</title><source>SpringerLink Journals - AutoHoldings</source><creator>Roodbar Shojaei, Taha ; Mohd Salleh, Mohamad Amran ; Mobli, Hossein ; Aghbashlo, Mortaza ; Tabatabaei, Meisam</creator><creatorcontrib>Roodbar Shojaei, Taha ; Mohd Salleh, Mohamad Amran ; Mobli, Hossein ; Aghbashlo, Mortaza ; Tabatabaei, Meisam</creatorcontrib><description>In this study, water-soluble carbon nanoparticles (CNPs) were synthesized by using waste facial tissue as a non-recyclable waste and the efficiency of CNPs in quenching mechanism of cadmium-telluride quantum dots (QDs) was investigated. In addition, CNPs synthesis was modeled by using artificial neural networks (ANN). To find the optimum model, ANN was trained by using different algorithms. Then, the generated models were statistically assessed and subsequently, the capability of the selected model for predicting the mean diameter size of the nanoparticles was verified. Based on the results, the model GA-4-7-1 had the most optimal statistical characteristics. Furthermore, the most pronounced effect on mean diameter size was associated to HNO
3
concentration while temperature demonstrated the least influence. Moreover, the quenching study confirmed the capability of the synthesized CNPs in quenching QDs.</description><identifier>ISSN: 0957-4522</identifier><identifier>EISSN: 1573-482X</identifier><identifier>DOI: 10.1007/s10854-018-00595-0</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Artificial neural networks ; Carbon ; Characterization and Evaluation of Materials ; Chemistry and Materials Science ; Intermetallic compounds ; Materials Science ; Nanoparticles ; Neural networks ; Optical and Electronic Materials ; Optimization ; Quantum dots ; Quenching ; Synthesis ; Tellurides</subject><ispartof>Journal of materials science. Materials in electronics, 2019-02, Vol.30 (3), p.3156-3165</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019</rights><rights>Journal of Materials Science: Materials in Electronics is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c363t-2f92c47898a3ed5cf606529a2754a119f92166c9fdc0c9ea4937a810bd74a68a3</citedby><cites>FETCH-LOGICAL-c363t-2f92c47898a3ed5cf606529a2754a119f92166c9fdc0c9ea4937a810bd74a68a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10854-018-00595-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10854-018-00595-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Roodbar Shojaei, Taha</creatorcontrib><creatorcontrib>Mohd Salleh, Mohamad Amran</creatorcontrib><creatorcontrib>Mobli, Hossein</creatorcontrib><creatorcontrib>Aghbashlo, Mortaza</creatorcontrib><creatorcontrib>Tabatabaei, Meisam</creatorcontrib><title>Multivariable optimization of carbon nanoparticles synthesized from waste facial tissues by artificial neural networks, new material for downstream quenching of quantum dots</title><title>Journal of materials science. Materials in electronics</title><addtitle>J Mater Sci: Mater Electron</addtitle><description>In this study, water-soluble carbon nanoparticles (CNPs) were synthesized by using waste facial tissue as a non-recyclable waste and the efficiency of CNPs in quenching mechanism of cadmium-telluride quantum dots (QDs) was investigated. In addition, CNPs synthesis was modeled by using artificial neural networks (ANN). To find the optimum model, ANN was trained by using different algorithms. Then, the generated models were statistically assessed and subsequently, the capability of the selected model for predicting the mean diameter size of the nanoparticles was verified. Based on the results, the model GA-4-7-1 had the most optimal statistical characteristics. Furthermore, the most pronounced effect on mean diameter size was associated to HNO
3
concentration while temperature demonstrated the least influence. Moreover, the quenching study confirmed the capability of the synthesized CNPs in quenching QDs.</description><subject>Artificial neural networks</subject><subject>Carbon</subject><subject>Characterization and Evaluation of Materials</subject><subject>Chemistry and Materials Science</subject><subject>Intermetallic compounds</subject><subject>Materials Science</subject><subject>Nanoparticles</subject><subject>Neural networks</subject><subject>Optical and Electronic Materials</subject><subject>Optimization</subject><subject>Quantum dots</subject><subject>Quenching</subject><subject>Synthesis</subject><subject>Tellurides</subject><issn>0957-4522</issn><issn>1573-482X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp9kc9u1DAQxi0EEkvhBThZ4kpg7MRJfEQV_6RWXEDiZs06duuS2FuPw2r7Trwj3l0kbj3NaL7vNzPSx9hrAe8EwPCeBIyqa0CMDYDSqoEnbCPU0DbdKH8-ZRvQamg6JeVz9oLoDgD6rh037M_1OpfwG3PA7ex42pWwhAcsIUWePLeYt7WLGNMOcwl2dsTpEMuto_DgJu5zWvgeqTju0QaceQlEa3VtD_xI-HCaRrfmUyn7lH_R29rt-YLF5aPqU-ZT2kcq2eHC71cX7W2IN8cX7leMZV2qXugle-ZxJvfqX71gPz59_H75pbn69vnr5YerxrZ9WxrptbTdMOoRWzcp63voldQoB9WhELrKou-t9pMFqx12uh1wFLCdhg77Cl2wN-e9u5zqM1TMXVpzrCdNJWU_SD221SXPLpsTUXbe7HJYMB-MAHOMxZxjMTUWc4rFQIXaM0TVHG9c_r_6EeovrO2VrQ</recordid><startdate>20190201</startdate><enddate>20190201</enddate><creator>Roodbar Shojaei, Taha</creator><creator>Mohd Salleh, Mohamad Amran</creator><creator>Mobli, Hossein</creator><creator>Aghbashlo, Mortaza</creator><creator>Tabatabaei, Meisam</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>KB.</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PDBOC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>S0W</scope></search><sort><creationdate>20190201</creationdate><title>Multivariable optimization of carbon nanoparticles synthesized from waste facial tissues by artificial neural networks, new material for downstream quenching of quantum dots</title><author>Roodbar Shojaei, Taha ; Mohd Salleh, Mohamad Amran ; Mobli, Hossein ; Aghbashlo, Mortaza ; Tabatabaei, Meisam</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c363t-2f92c47898a3ed5cf606529a2754a119f92166c9fdc0c9ea4937a810bd74a68a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Artificial neural networks</topic><topic>Carbon</topic><topic>Characterization and Evaluation of Materials</topic><topic>Chemistry and Materials Science</topic><topic>Intermetallic compounds</topic><topic>Materials Science</topic><topic>Nanoparticles</topic><topic>Neural networks</topic><topic>Optical and Electronic Materials</topic><topic>Optimization</topic><topic>Quantum dots</topic><topic>Quenching</topic><topic>Synthesis</topic><topic>Tellurides</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Roodbar Shojaei, Taha</creatorcontrib><creatorcontrib>Mohd Salleh, Mohamad Amran</creatorcontrib><creatorcontrib>Mobli, Hossein</creatorcontrib><creatorcontrib>Aghbashlo, Mortaza</creatorcontrib><creatorcontrib>Tabatabaei, Meisam</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection (ProQuest)</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>Materials Science Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Materials Science Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>DELNET Engineering & Technology Collection</collection><jtitle>Journal of materials science. Materials in electronics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Roodbar Shojaei, Taha</au><au>Mohd Salleh, Mohamad Amran</au><au>Mobli, Hossein</au><au>Aghbashlo, Mortaza</au><au>Tabatabaei, Meisam</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multivariable optimization of carbon nanoparticles synthesized from waste facial tissues by artificial neural networks, new material for downstream quenching of quantum dots</atitle><jtitle>Journal of materials science. Materials in electronics</jtitle><stitle>J Mater Sci: Mater Electron</stitle><date>2019-02-01</date><risdate>2019</risdate><volume>30</volume><issue>3</issue><spage>3156</spage><epage>3165</epage><pages>3156-3165</pages><issn>0957-4522</issn><eissn>1573-482X</eissn><abstract>In this study, water-soluble carbon nanoparticles (CNPs) were synthesized by using waste facial tissue as a non-recyclable waste and the efficiency of CNPs in quenching mechanism of cadmium-telluride quantum dots (QDs) was investigated. In addition, CNPs synthesis was modeled by using artificial neural networks (ANN). To find the optimum model, ANN was trained by using different algorithms. Then, the generated models were statistically assessed and subsequently, the capability of the selected model for predicting the mean diameter size of the nanoparticles was verified. Based on the results, the model GA-4-7-1 had the most optimal statistical characteristics. Furthermore, the most pronounced effect on mean diameter size was associated to HNO
3
concentration while temperature demonstrated the least influence. Moreover, the quenching study confirmed the capability of the synthesized CNPs in quenching QDs.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10854-018-00595-0</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0957-4522 |
| ispartof | Journal of materials science. Materials in electronics, 2019-02, Vol.30 (3), p.3156-3165 |
| issn | 0957-4522 1573-482X |
| language | eng |
| recordid | cdi_proquest_journals_2162672983 |
| source | SpringerLink Journals - AutoHoldings |
| subjects | Artificial neural networks Carbon Characterization and Evaluation of Materials Chemistry and Materials Science Intermetallic compounds Materials Science Nanoparticles Neural networks Optical and Electronic Materials Optimization Quantum dots Quenching Synthesis Tellurides |
| title | Multivariable optimization of carbon nanoparticles synthesized from waste facial tissues by artificial neural networks, new material for downstream quenching of quantum dots |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T13%3A55%3A17IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Multivariable%20optimization%20of%20carbon%20nanoparticles%20synthesized%20from%20waste%20facial%20tissues%20by%20artificial%20neural%20networks,%20new%20material%20for%20downstream%20quenching%20of%20quantum%20dots&rft.jtitle=Journal%20of%20materials%20science.%20Materials%20in%20electronics&rft.au=Roodbar%20Shojaei,%20Taha&rft.date=2019-02-01&rft.volume=30&rft.issue=3&rft.spage=3156&rft.epage=3165&rft.pages=3156-3165&rft.issn=0957-4522&rft.eissn=1573-482X&rft_id=info:doi/10.1007/s10854-018-00595-0&rft_dat=%3Cproquest_cross%3E2162672983%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2162672983&rft_id=info:pmid/&rfr_iscdi=true |