Fire hazard of epoxy-based transparent wood

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F23%3A97134" target="_blank" >RIV/60460709:41320/23:97134 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/s10973-023-12360-5" target="_blank" >http://dx.doi.org/10.1007/s10973-023-12360-5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10973-023-12360-5" target="_blank" >10.1007/s10973-023-12360-5</a>

Result language

angličtina

Original language name

Fire hazard of epoxy-based transparent wood

Original language description

Transparent wood is a modern bio-renewable material with great potential for both science and industrial applications. However, the fire hazard of transparent wood is still almost unexplored. This study aims to investigate the impact of pristine basswood modification to epoxy-based transparent wood on the fire hazard and to train neural networks for the prediction of heat release rate from mass loss rate of pristine basswood, epoxy-based transparent wood, and epoxy resin. Transparent wood was prepared by lignin modification in pristine small-leaved basswood (Tilia cordata Mill.) and subsequent vacuum infiltration by epoxy resin. The fire hazard of the samples was determined by the cone calorimeter at four heat fluxes of 20-50 kW m(-2). The fire hazard of investigated materials was compared based on the critical heat flux, ignition temperature, heat release rate, effective heat of combustion and time to flashover. Transparent wood showed higher resistance to ignition (higher critical heat flux and ignition temperature) than pristine wood. However, other parameters (heat release rate and effective heat of combustion) were higher (worse) and the time to flashover was lower (worse) for transparent wood than for pristine wood. Trained neural networks for predicting heat release rate from the mass loss rate of wood (both pristine and transparent) and epoxy resin showed coefficients of determination from 0.70 to 0.92. Trained neural networks with a coefficient of determinations above 0.90 are usable for low-cost heat release rate measurements in both science and industrial applications.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10406 - Analytical chemistry

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY

ISSN

1388-6150

e-ISSN

1388-6150

Volume of the periodical

148

Issue of the periodical within the volume

19

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

15

Pages from-to

9893-9907

UT code for WoS article

001034504000002

EID of the result in the Scopus database

2-s2.0-85165561095