Sustainable composite from furfuryl alcohol and wood flour with outstanding fire resistance and its prediction using neural networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28110%2F25%3A63597158" target="_blank" >RIV/70883521:28110/25:63597158 - isvavai.cz</a>
Alternative codes found
RIV/70883521:28610/25:63597158
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2468217925001297?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2468217925001297?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.jsamd.2025.100976" target="_blank" >10.1016/j.jsamd.2025.100976</a>
Alternative languages
Result language
angličtina
Original language name
Sustainable composite from furfuryl alcohol and wood flour with outstanding fire resistance and its prediction using neural networks
Original language description
Novel composites were successfully produced using renewable green sources, furfuryl alcohol resin, commonly obtained from biomass, and wood flour. Compared with a conventional melt-blending technique used for the preparation of wood-plastic composites, this unique approach, utilizing low-viscosity thermoset resin with high affinity for wood, enables the avoidance of excessive treatment of wood flour. Four flame retardants possessing different flame-retardant mechanisms (expandable graphite (EG), ammonium dihydrogen phosphate (ADP), Exolit OP560 and dimethyl propane phosphonate) at two loading levels (7.5 and 15 wt%) were used to suppress the flammability of the composites evaluated by a cone calorimeter test, limiting oxygen index and UL 94. All investigated flame retardants significantly reduced maximum value of heat release rate (HRR) (EG and ADP approx. up to 75 %) and, moreover, ADP and EG significantly reduced the total smoke production (EG up to 25 % and ADP up to 96 %) confirming outstanding and unusual flammability suppression considering HRR reduction and a decrease in smoke production rate (SPR) at the same time. Besides that, the neural network prediction models for HRR and SPR from test time and mass loss rate were created and trained, giving the possibility to predict HRR and SPR values from simple and cheap tests, providing only mass loss rate at specific conditions.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20505 - Composites (including laminates, reinforced plastics, cermets, combined natural and synthetic fibre fabrics; filled composites)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Science: Advanced Materials and Devices
ISSN
2468-2284
e-ISSN
2468-2179
Volume of the periodical
10
Issue of the periodical within the volume
4
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
12
Pages from-to
nestránkováno
UT code for WoS article
001574085900001
EID of the result in the Scopus database
2-s2.0-105015820182