Investigation for the influence of Sm2O3 and CeO2 nanoparticles on the microstructure and electrochemical behavior of epoxy and prediction of mechanical characterizations of adhesive joining of CFPEEK via machine learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F25%3A10260200" target="_blank" >RIV/61989100:27230/25:10260200 - isvavai.cz</a>
Result on the web
<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001568964100001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001568964100001</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.jmrt.2025.08.253" target="_blank" >10.1016/j.jmrt.2025.08.253</a>
Alternative languages
Result language
angličtina
Original language name
Investigation for the influence of Sm2O3 and CeO2 nanoparticles on the microstructure and electrochemical behavior of epoxy and prediction of mechanical characterizations of adhesive joining of CFPEEK via machine learning
Original language description
With the increase demand for lightweight material combined with high mechanical and electrochemical properties, in this study we proposed a novel polymer nanocomposites (PNCs). Due to the unique characterizations of rare earth metal oxides nanoparticles, cerium oxide (CeO<inf>2</inf>)/samarium oxide (Sm<inf>2</inf>O<inf>3</inf>) were integrated to enhance microstructural, mechanical, electrochemical and the joint efficiency performance of epoxy resin matrix. The influences of various dispersion content of CeO<inf>2</inf>/Sm<inf>2</inf>O<inf>3</inf> were explored. The adhesive joint efficiency and maximum shear force were determined by performing single lap joint through applying a thin layer of the synthesized PNCs to join carbon fiber polyetheretherketone (CFPEEK). Superior ultimate tensile strength was obtained by doping 7 wt% CeO<inf>2</inf>/Sm<inf>2</inf>O<inf>3</inf> as the enhancement reached 483.482 % and 490.380 % respectively. The maximum joint efficiency reached 68.43 % and was achieved by doping 3 wt% of CeO<inf>2</inf>. An optimum enhancement in electrical conductivity was achieved by 1 wt% and 5 wt% of CeO<inf>2</inf>, while optimum enhancement in insulation or coating properties obtained by 5 wt% of Sm<inf>2</inf>O<inf>3</inf>. In addition, machine learning algorithms, including artificial neural networks, random forest, extreme gradient boosting (XGBoost), and k-nearest neighbors were applied to predict the investigated material properties. XGBoost provided robust predictions across both mechanical and electrochemical properties. © 2025 The Authors.
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
20301 - Mechanical engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
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 Materials Research and Technology
ISSN
2238-7854
e-ISSN
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Volume of the periodical
38
Issue of the periodical within the volume
1
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
22
Pages from-to
4917-4938
UT code for WoS article
001568964100001
EID of the result in the Scopus database
2-s2.0-105025686540