Enhancing tensile strength, adhesive joining of CFPEEK and microstructure properties of epoxy by Nd2O3 rare-earth nanoparticles reinforcement

dc.contributor.authorAsmael, Mohammed
dc.contributor.authorAl Mahmoud, Zummurd
dc.contributor.authorSahmani, Saeid
dc.contributor.authorSafaei, Babak
dc.date.accessioned2026-02-06T18:40:11Z
dc.date.issued2024
dc.departmentDoğu Akdeniz Üniversitesi
dc.description.abstractThis study is conducted to enhance the tensile and adhesive shear performance of pure epoxy (Ep) by the addition of Neodymium oxide (Nd2O3) nanoparticles (NPs). The polymer nanocomposite (PNC) was prepared with the addition of various weight percentage (wt%) of Nd2O3. The effect of various content of Nd2O3 NPs on mechanical features such as stress-strain, ultimate tensile strength (UTS), Young's modulus, elongation and toughness were explored by conducting the tensile stress analysis. Besides, the joining strength of the adhesive was explored by performing the lap shear strength (LSS), through implementing the developed PNC as an adhesive layer to join Polyetheretherketone (CFPEEK). As a result of a good dispersal of the NPs in the polymer matrix and interaction between the Nd2O3 and Ep, it was revealed that a remarkable enhancement in the mechanical behavior was obtained by the addition of 5 wt% for tensile properties where the improvement in UTS reached 454.2 %. Although, the maximum enhancement in lap shear properties obtained by the addition of 3 wt % for where the improvement reached 153.3 % compared to pure Ep. An absence of further increase in mechanical properties was noticed in case of further increase above 5 wt%, due to NPs agglomeration. Additionally, to predict the tensile stress and shear force of the PNC with the addition of various wt% of Nd2O3, various machine learning (ML) models were accomplished specifically one-way ANOVA, regression and the artificial neural network (ANN). The most significant model performance was accomplished by one-way ANOVA where the RMSE for tensile and shear force are 0.4758 and 0.1264 respectively with prediction error 2.866 % and 7.434 %.
dc.identifier.doi10.1016/j.mtcomm.2024.110854
dc.identifier.issn2352-4928
dc.identifier.orcid0009-0007-5872-3868
dc.identifier.orcid0000-0002-1675-4902
dc.identifier.orcid0000-0003-2853-0460
dc.identifier.scopus2-s2.0-85207690654
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1016/j.mtcomm.2024.110854
dc.identifier.urihttps://hdl.handle.net/11129/13173
dc.identifier.volume41
dc.identifier.wosWOS:001350268900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofMaterials Today Communications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260204
dc.subjectNeodymium oxide
dc.subjectEpoxy
dc.subjectMechanical properties
dc.subjectPolymer composite
dc.subjectAdhesive bonding
dc.subjectMachine learning
dc.titleEnhancing tensile strength, adhesive joining of CFPEEK and microstructure properties of epoxy by Nd2O3 rare-earth nanoparticles reinforcement
dc.typeArticle

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