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Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 268
Author(s):  
Salman Khalid ◽  
Jaehun Lee ◽  
Heung Soo Kim

This paper introduces a new loading condition considering the combined thermo-electro-mechanical coupling effect in a series solution-based approach to analyze the free-edge interlaminar stresses in smart composite laminates. The governing equations are developed using the principle of complementary virtual work. The assumed stress fields satisfy the traction-free and free-edge boundary conditions. The accurate stress states of the composite structures are acquired through the procedure of generalized eigenvalue problems. The uniform temperature is employed throughout the laminate, and the electric field loading is applied to the symmetric piezo-bonded actuators to examine the combined effect of thermal and electrical stresses on the overall deformation of smart composite laminates. It was observed that the magnitude of the peeling stresses generated by mechanical loading was reduced by the combined thermal and electric excitation loading (up to 25.3%), which in turn resulted in expanding the service life of the smart composite structures. The proposed approach is implemented on three different layup configurations. The efficiency of the current methodology is confirmed by comparing the results with the 3D finite element (FEM) solution computed by ABAQUS.


Materials ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 499
Author(s):  
Olaf Dudek ◽  
Wojciech Klein ◽  
Damian Gąsiorek ◽  
Mariusz Pawlak

3D printing of a composite structure with shape memory materials requires a special approach to the subject, at the stage of the design and printing process. This paper presents the design steps during the development of a 3D-printed composite structure with shape memory material. The connection points between the SMA fibers and the printer filament are developed in the MATLAB environment. Finite element method is used to simulate the shortening of the shape memory material under the influence of temperature and its effect on the printed polymer material is presented. In the MATLAB environment, evolutionary algorithms were used to determine the shape of the SMA fiber alignment. This work demonstrates the use of shape memory effect in 3D printed smart composite structures, where the component takes a predetermined shape. The structure obtained as a result of such printing changes with the heat generated by the current voltage, making it the desired fourth dimension.


2021 ◽  
Vol 11 (22) ◽  
pp. 10738
Author(s):  
Anis Daou ◽  
Raid G. Alany ◽  
Gianpiero Calabrese

Drug delivery through the Blood–Brain Barrier (BBB) represents a significant challenge. Despite the current strategies to circumvent the BBB, nanotechnology offers unprecedented opportunities for combining selective delivery, improved bioavailability, drug protection, and enhanced pharmacokinetics profiles. Chitosan nanocarriers allow for a more efficacious strategy at the cellular and sub-cellular levels. Boron Neutron Capture Therapy (BNCT) is a targeted chemo-radiotherapeutic technique that allows the selective depletion of cancer cells by means of selective tagging of cancer cells with 10B, followed by irradiation with low-energy neutrons. Consequently, the combination of a polymer-based nanodelivery system enclosing an effective BNCT pharmacophore can potentially lead to the selective delivery of the load to cancer cells beyond the BBB. In this work, synthesized novel boronated agents based on carborane-functionalized Delocalized Lipophilic Cations (DLCs) are assessed for safety and selective targeting of tumour cells. The compounds are then encapsulated in nanocarriers constituted by chitosan to promote permeability through the BBB. Additionally, chitosan was used in combination with polypyrrole to form a smart composite nanocapsule, which is expected to release its drug load with variations in pH. Results indicate the achievement of more selective boron delivery to cells via carboranyl DLCs. Finally, preliminary cell studies indicate no toxicity was detected in chitosan nanocapsules, further enhancing its viability as a potential delivery vehicle in the BNCT of brain tumours.


2021 ◽  
Author(s):  
Katia Caamaño ◽  
Jaquín Calbo ◽  
Raquel Heras-Mozos ◽  
Bruno J. C Vieira ◽  
Joao C. Waerenborgh ◽  
...  

The design of efficient food contact materials that maintain optimal levels of food safety is of paramount relevance to reduce the increasing foodborne illnesses. In this work, we develop a smart composite MOF-based material that fosters a unique prolonged antibacterial activity. The composite is obtained by entrapping a natural preserving food molecule, carvacrol, into the mesoporous MIL-100(Fe) material following a direct and biocompatible impregnation method and obtaining particularly high payloads. By exploiting the intrinsic redox nature of MIL-100(Fe) material it is possible to achieve a prolonged activity against E. coli bacteria due to a triggered two-step carvacrol release of films containing the carvacrol@MOF composite. Essentially, it was discovered that based on the underlying chemical interaction among MIL-100(Fe) and carvacrol, it is possible to undergo a reversible charge transfer process between the metallic MOF counterpart and the carvacrol upon certain physical stimuli. During this process, the preferred carvacrol binding site has been monitored by IR, Mössbauer and EPR spectroscopies and is supported by theoretical calculations.


Author(s):  
Li Wang ◽  
Xiaohu Chen ◽  
Xiyang Zeng ◽  
Kun Luo ◽  
Shiyi Zhou ◽  
...  

2021 ◽  
Author(s):  
RELEBOHILE GEORGE QHOBOSHEANE ◽  
MUTHU RAM PRABHU ELENCHEZHIAN ◽  
VAMSEE VADLAMUDI ◽  
KENNETH REIFSNIDER ◽  
RASSEL RAIHAN

This work in on the development of an ordinary differential equation (ODE) model coupled with statistical methods for the prediction of fracture toughness of a magnetostrictive, piezoelectric smart self-sensing Fiber Reinforced Polymer (FRP) composite. The smart composite with sensing properties encompasses Terfenol-D alloy nanoparticles and Single Walled Carbon NanoTubes (SWCNT). To explore various configurations the of nanoparticle constituents’ effect on fracture toughness within the FRP composite, the ODE model developed within a finite element analysis (FEA) environment is considered to attain fracture observations across the solution space. The acquired FEA data is then used to feed the machine-learning (ML) algorithms to obtain composite fracture toughness predictions. A comparison and development of artificial neural networks (ANN), decision trees and support vector machines (SVM) models for FRP smart self-sensing composite fracture toughness prediction is done. Qualitative results stating if the sample has fractured or not and quantitative data giving the fracture toughness and strain energy release rate for the smart self-sensing FRP composites is attained. A comparison of all predictions from the developed models for both fracture toughness is corroborated with literature data.


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