TY - GEN
T1 - MXENES/PAANA BASED SENSORS FOR COMPOSITE STRUCTURES
AU - Bulderberga, Olga
AU - Ovodok, Evgeni
AU - Stankevich, Stanislav
AU - Tarasovs, Sergejs
AU - Poznyak, Sergey
AU - Aniskevich, Andrey
N1 - Publisher Copyright:
©2022 Bulderberga et al.
PY - 2022
Y1 - 2022
N2 - Sensor technologies provide huge opportunities in structural health monitoring. Improving sensing of them could lead to wider applications. MXenes-based sensors are under investigation for the last several years, but improvements are always desired. By adding sodium salt of polyacrylic acid (PAANa) to MXenes colloidal solution several issues could be improved. By the work, it was defined that PAANa additive promotes wetting of hydrophobic surfaces of an epoxy matrix of the composite, thus allowing the formation of a uniform conductive layer of MXenes/PAANa. Furthermore, the high conductivity, which is typical for MXenes films, was preserved in MXenes/PAANa. The ability of the MXenes/PAANa thin-film sensor to monitor the structural integrity of the sample was approbated. A trained artificial neural network was developed and applied for the detection and location of damage in the composite plate with a further comparison with the experimental results.
AB - Sensor technologies provide huge opportunities in structural health monitoring. Improving sensing of them could lead to wider applications. MXenes-based sensors are under investigation for the last several years, but improvements are always desired. By adding sodium salt of polyacrylic acid (PAANa) to MXenes colloidal solution several issues could be improved. By the work, it was defined that PAANa additive promotes wetting of hydrophobic surfaces of an epoxy matrix of the composite, thus allowing the formation of a uniform conductive layer of MXenes/PAANa. Furthermore, the high conductivity, which is typical for MXenes films, was preserved in MXenes/PAANa. The ability of the MXenes/PAANa thin-film sensor to monitor the structural integrity of the sample was approbated. A trained artificial neural network was developed and applied for the detection and location of damage in the composite plate with a further comparison with the experimental results.
KW - Composite structure
KW - MXenes
KW - Sodium salt of polyacrylic acid
KW - Structural health monitoring
UR - https://www.scopus.com/pages/publications/85149304529
M3 - Conference paper
AN - SCOPUS:85149304529
T3 - ECCM 2022 - Proceedings of the 20th European Conference on Composite Materials: Composites Meet Sustainability
SP - 655
EP - 662
BT - Applications and Structures
A2 - Vassilopoulos, Anastasios P.
A2 - Michaud, Veronique
PB - Composite Construction Laboratory (CCLab), Ecole Polytechnique Federale de Lausanne (EPFL)
T2 - 20th European Conference on Composite Materials: Composites Meet Sustainability, ECCM 2022
Y2 - 26 June 2022 through 30 June 2022
ER -