Effect of GNPs on the Piezoresistive, Electrical and Mechanical Properties of PHA and PLA Films

Mármol, Gonzalo; Sanivada, Usha Kiran; Fangueiro, Raúl

doi:10.3390/fib9120086

Cited by 14 publications

(6 citation statements)

References 60 publications

(66 reference statements)

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“…An average of five readings was taken and the graph was drawn between voltage and current, where the slope of the graph gives the value of resistance of the sample. After obtaining the resistance R in Ω, electrical resistivity can be calculated by using the equation below [18]. Once the resistivity (ρ) in Ω m was obtained, the inverse of resistivity gave the conductivity (σ) in (S/m).…”

Section: Electrical Conductivitymentioning

confidence: 99%

“…Hence, researchers are looking towards GNPs as an alternative to graphene, where they can be produced by ball milling, shear exfoliation, etc. GNPs are attractive as they can be mixed into the polymeric matrix to produce coatings or nanocomposites, and their electrical conductivity is in the order of 6 × 10 3 S/m [18]. Coating of these nanomaterials can be performed via screen printing, which is one of the effective coating techniques alongside pad printing, flexography printing, gravure printing, and roll-to-roll printing.…”

Section: Introductionmentioning

confidence: 99%

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Joule-Heating Effect of Thin Films with Carbon-Based Nanomaterials

et al. 2022

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Smart textiles have become a promising area of research for heating applications. Coatings with nanomaterials allow the introduction of different functionalities, enabling doped textiles to be used in sensing and heating applications. These coatings were made on a piece of woven cotton fabric through screen printing, with a different number of layers. To prepare the paste, nanomaterials such as graphene nanoplatelets (GNPs) and multiwall carbon nanotubes (CNTs) were added to a polyurethane-based polymeric resin, in various concentrations. The electrical conductivity of the obtained samples was measured and the heat-dissipating capabilities assessed. The results showed that coatings have induced electrical conductivity and heating capabilities. The highest electrical conductivity of (9.39 ± 1.28 × 10−1 S/m) and (9.02 ± 6.62 × 10−2 S/m) was observed for 12% (w/v) GNPs and 5% (w/v) (CNTs + GNPs), respectively. The sample with 5% (w/v) (CNTs + GNPs) and 12% (w/v) GNPs exhibited a Joule effect when a voltage of 12 V was applied for 5 min, and a maximum temperature of 42.7 °C and 40.4 °C were achieved, respectively. It can be concluded that higher concentrations of GNPs can be replaced by adding CNTs, still achieving nearly the same performance. These coated textiles can potentially find applications in the area of heating, sensing, and biomedical applications.

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Section: Electrical Conductivitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joule-Heating Effect of Thin Films with Carbon-Based Nanomaterials

et al. 2022

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“…The blending of flexible PP resin with hard PP resin at various proportions gives rise to enhance the shape memory properties at the same time mechanical properties can also be tailored according to the end-use applications. GNPs are already proven by many researchers that, those are more effective as reinforcement for polymer composites to enhance many properties such as mechanical (Mármol et al , 2021), thermal (Arruda et al , 2022) and electrical (Sanivada et al , 2022). In addition to that, the resin blend is reinforced with GNPs which also adds its part to enhance the mechanical properties along with shape memory behaviour.…”

Section: Methodsmentioning

confidence: 99%

Effect of GNPs and resin blend on tear resistance of 4D printed shape memory photopolymer composite

Borra

Neigapula

2023

RPJ

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Purpose The tear strength (Ts) is a significant property for any kind of soft polymeric material such as rubber, elastomer, viscoelastic material and its composites, to quantify the suitability of a material for any shape memory applications. Many times, the soft elastomeric polymer material has to be capable enough to deform to a maximum extent of displacement but at the same time, it has to withstand the maximum load without fail. Along with shape recovery properties (i.e. the ability to recover its shape from programmed to the original), the success of the shape memory cycle is mainly depending on its stiffness and strength. It has to resist tear during stretching (i.e. programming stage) as repeatedly subjected to deformation, and, hence, it is important to study the tear behaviour for shape memory polymers (SMPs) and their composites. The purpose of the work is to investigate the effect of parameters on Ts of 4D printed specimen using Taguchi method. Design/methodology/approach The objective of the work is to tailor the Ts of SMPs by reinforcing the graphene nano particles (GNPs) in a blended photopolymer (PP) resin with flexible PP and hard PP resin. In this study, a total of nine experiments were designed based on the L9 orthogonal array (OA) using the design of experiments (DOEs). All the shape memory photopolymer composite’s (SMPPCs) specimens are fabricated using masked stereolithography (MSLA), also known as resin three-dimensional printing (R3DP) technique. Findings Specimens are tested using universal testing machine (UTM) for maximum tear force (Fmax) and displacement (δ) caused by tearing the specimen to evaluate the strength against the tear. The results showed that the Wt.% of resin blend highly influenced both Fmax and δ, while GNPs also had an impact on δ. The specimens are offering more tear resistance for those specimens blended with less Wt.% of flexible PP at the same time the specimens enable more δ for those specimens reinforced with 0.3 Wt.% GNPs at 10-s exposure time. The optimum combinations are A1, B1 and C3 for the Fmax and Ts and at the same time A1, B3 and C3 for δ. Research limitations/implications To customise the tear resistance of SMPPCs using MSLA 3 D printing, this study suggested a blend of PP resins reinforced with GNPs. This opens up a new path for creating novel, inexpensive multi-functional 4-dimensional (4D) printed parts. Originality/value The use of flexible PP and hard PP resin blends, fabricating the SMPPCs specimens using 3 D printed MSLA technology, investigating the effect of GNPs, resin blend and exposure time, optimizing the process parameters using Taguchi and the work were all validated using confirmation tests and regression analysis using test train method, which increases the originality and novelty.

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“…Many researchers reported their research with reinforcements such as graphene nano platelet (GNPs), Fe 3 O 4 , GNPs oxide (GO), multi-walled carbon nanotubes, Nitonol, calcium sulphate whiskers (CSW), Nano silica filler (NSF), SiC, SiO 2 and many more to investigate their effect on shape memory properties. Among all, carbon-based reinforcements (Sanivada et al , 2022) such as GNPs are identified to show their unique properties (Mármol et al , 2021) to achieve improved shape recovery rate and higher shape fixity ratios (Zhang et al , 2021a, 2021b). GNPs are proven by many researchers for their novel abilities in many sectors such as automotive (Moreira et al , 2021), aerospace, defence applications, biomedical such as drug delivery systems, electronics such as smart sensors (Arruda et al , 2022), smart materials such as self-repair, shape changing materials, textiles and nano robots.…”

Section: Introductionmentioning

confidence: 99%

Synthesis and optimization for shape memory behaviour of 4D printed GNPs reinforced shape memory photopolymer composite

Borra

Neigapula

2023

RPJ

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Purpose Shape memory materials are functional materials having a good number of applications due to their unique features of programmable material technology such as self-stretching, self-assembly and self-tightening. Advancements in today’s technology led to the easy fabrication of such novel materials using 3D printing techniques. When an external stimulus causes a 3D printed specimen to change shape on its own, this process is known as 4D printing. This study aims to investigate the effect of graphene nano platelet (GNPs) on the shape memory behaviour of shape memory photo polymer composites (SMPPCs) and to optimize the shape-changing response by using the Taguchi method. Design/methodology/approach SMPPCs are synthesized by blending different weight fractions (Wt.%) of flexible or soft photopolymer (FPP) resin with hard photopolymer (HPP) resin, then reinforced with GNPs at various Wt.% to the blended PP resin, and then fabricated using masked stereolithography (MSLA) apparatus. The shape memory test is conducted to assess the shape recovery time (T), shape fixity ratio (Rf), shape recovery ratio (Rr) and shape recovery rate (Vr) using Taguchi analysis by constructing an L9 orthogonal array with parameters such as Wt.% of a blend of FPP and HPP resin, Wt.% of GNPs and holding time. Findings SMPPCs with A3, B3 and C2 result in a faster T with 2 s, whereas SMPPCs with A1, B1 and C3 result in a longer T with 21 s. The factors A and B are ranked as the most significant in the Pareto charts that were obtained, whereas C is not significant. It can be seen from the heatmap plot that when factors A and B increase, T is decreasing and Vr is increasing. The optimum parameters for T and Vr are A3, B3 and C2 at the same time for Rf and Rr are A1, B3 and C1. Research limitations/implications Faster shape recovery results from a higher Wt.% of FPP resin in a blend than over a true HPP resin. This is because the flexible polymer links in FPP resin activate more quickly over time. However, a minimum amount of HPP resin also needs to be maintained because it plays a role in producing higher Rf and Vr. The use of GNPs as reinforcement accelerates the T because nanographene conducts heat more quickly, releasing the temporary shape of the specimen more quickly. Originality/value The use of FPP and HPP resin blends, fabricating the 4D-printed SMPPCs specimens with MSLA technology, investigating the effect of GNPs and optimizing the process parameters using Taguchi and the work was validated using confirmation tests and regression analysis, which increases the originality and novelty.

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Effect of GNPs on the Piezoresistive, Electrical and Mechanical Properties of PHA and PLA Films

Cited by 14 publications

References 60 publications

Joule-Heating Effect of Thin Films with Carbon-Based Nanomaterials

Joule-Heating Effect of Thin Films with Carbon-Based Nanomaterials

Effect of GNPs and resin blend on tear resistance of 4D printed shape memory photopolymer composite

Synthesis and optimization for shape memory behaviour of 4D printed GNPs reinforced shape memory photopolymer composite

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