Films based on crosslinked TEMPO-oxidized cellulose and predictive analysis via machine learning

Merve Özkan; Maryam Borghei; Alp Karakoç; Orlando J. Rojas; Jouni Paltakari

doi:10.1038/s41598-018-23114-x

Films based on crosslinked TEMPO-oxidized cellulose and predictive analysis via machine learning

Merve Özkan^*, Maryam Borghei, Alp Karakoç, Orlando J. Rojas, Jouni Paltakari

^*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Lehtiartikkeli › Article › Scientific › vertaisarvioitu

27 Sitaatiot (Scopus)

229 Lataukset (Pure)

Abstrakti

We systematically investigated the effect of film-forming polyvinyl alcohol and crosslinkers, glyoxal and ammonium zirconium carbonate, on the optical and surface properties of films produced from TEMPO-oxidized cellulose nanofibers (TOCNFs). In this regard, UV-light transmittance, surface roughness and wetting behavior of the films were assessed. Optimization was carried out as a function of film composition following the "random forest" machine learning algorithm for regression analysis. As a result, the design of tailor-made TOCNF-based films can be achieved with reduced experimental expenditure. We envision this approach to be useful in facilitating adoption of TOCNF for the design of emerging flexible electronics, and related platforms.

Alkuperäiskieli	Englanti
Artikkeli	4748
Julkaisu	Scientific Reports
Vuosikerta	8
Numero	1
DOI - pysyväislinkit	https://doi.org/10.1038/s41598-018-23114-x
Tila	Julkaistu - 1 jouluk. 2018
OKM-julkaisutyyppi	A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

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10.1038/s41598-018-23114-x

CHEM-Ozkan-et al-Films based-Scientific Reports-2018Final published version, 3,16 MBLisenssi: CC BY

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abstract = "We systematically investigated the effect of film-forming polyvinyl alcohol and crosslinkers, glyoxal and ammonium zirconium carbonate, on the optical and surface properties of films produced from TEMPO-oxidized cellulose nanofibers (TOCNFs). In this regard, UV-light transmittance, surface roughness and wetting behavior of the films were assessed. Optimization was carried out as a function of film composition following the {"}random forest{"} machine learning algorithm for regression analysis. As a result, the design of tailor-made TOCNF-based films can be achieved with reduced experimental expenditure. We envision this approach to be useful in facilitating adoption of TOCNF for the design of emerging flexible electronics, and related platforms.",

author = "Merve {\"O}zkan and Maryam Borghei and Alp Karako{\c c} and Rojas, {Orlando J.} and Jouni Paltakari",

year = "2018",

month = dec,

day = "1",

doi = "10.1038/s41598-018-23114-x",

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T1 - Films based on crosslinked TEMPO-oxidized cellulose and predictive analysis via machine learning

AU - Özkan, Merve

AU - Borghei, Maryam

AU - Karakoç, Alp

AU - Rojas, Orlando J.

AU - Paltakari, Jouni

PY - 2018/12/1

Y1 - 2018/12/1

N2 - We systematically investigated the effect of film-forming polyvinyl alcohol and crosslinkers, glyoxal and ammonium zirconium carbonate, on the optical and surface properties of films produced from TEMPO-oxidized cellulose nanofibers (TOCNFs). In this regard, UV-light transmittance, surface roughness and wetting behavior of the films were assessed. Optimization was carried out as a function of film composition following the "random forest" machine learning algorithm for regression analysis. As a result, the design of tailor-made TOCNF-based films can be achieved with reduced experimental expenditure. We envision this approach to be useful in facilitating adoption of TOCNF for the design of emerging flexible electronics, and related platforms.

AB - We systematically investigated the effect of film-forming polyvinyl alcohol and crosslinkers, glyoxal and ammonium zirconium carbonate, on the optical and surface properties of films produced from TEMPO-oxidized cellulose nanofibers (TOCNFs). In this regard, UV-light transmittance, surface roughness and wetting behavior of the films were assessed. Optimization was carried out as a function of film composition following the "random forest" machine learning algorithm for regression analysis. As a result, the design of tailor-made TOCNF-based films can be achieved with reduced experimental expenditure. We envision this approach to be useful in facilitating adoption of TOCNF for the design of emerging flexible electronics, and related platforms.

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U2 - 10.1038/s41598-018-23114-x

DO - 10.1038/s41598-018-23114-x

M3 - Article

AN - SCOPUS:85044281478

SN - 2045-2322

VL - 8

JO - Scientific Reports

JF - Scientific Reports

IS - 1

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ER -

Films based on crosslinked TEMPO-oxidized cellulose and predictive analysis via machine learning

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