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Abstract Infrared thermography is a non-destructive technique that can be exploited in many fields including polymer composite investigation. Based on emissivity and thermal diffusivity variation; components, defects, and curing state of the composite can be identified. However, manual processing of thermal images that may contain significant artifacts, is prone to erroneous component and property determination. In this study, thermal images of different graphite/graphene-based polymer composites fabricated by hand, planetary, and batch mixing techniques were analyzed through an automatic machine learning model. Filler size, shape, and location can be identified in polymer composites and thus, the dispersion of different samples was quantified with a resolution of ~ 20 µm despite having artifacts in the thermal image. Thermal diffusivity comparison of three mixing techniques was performed for 40% graphite in the elastomer. Batch mixing demonstrated superior dispersion than planetary and hand mixing as the dispersion index (DI) for batch mixing was 0.07 while planetary and hand mixing showed 0.0865 and 0.163 respectively. Curing was investigated for a polymer with different fillers (PDMS took 500 s while PDMS-Graphene and PDMS Graphite Powder took 800 s to cure), and a thermal characteristic curve was generated to compare the composite quality. Therefore, the above-mentioned methods with machine learning algorithms can be a great tool to analyze composite both quantitatively and qualitatively. 

Paper-based biosensors are a potential paradigm of sensitivity achieved via microporous spreading/microfluidics, simplicity, and affordability. In this paper, we develop decorated paper with graphene and conductive polymer (herein referred to as graphene conductive polymer paper-based sensor or GCPPS) for sensitive detection of biomolecules. Planetary mixing resulted in uniformly dispersed graphene and conductive polymer ink, which was applied to laser-cut Whatman filter paper substrates. Scanning electron microscopy and Raman spectroscopy showed strong attachment of conductive polymer-functionalized graphene to cellulose fibers. The GCPPS detected dopamine and cytokines, such as tumor necrosis factor-alpha (TNF-α), and interleukin 6 (IL-6) in the ranges of 12.5–400 µM, 0.005–50 ng/mL, and 2 pg/mL–2 µg/mL, respectively, using a minute sample volume of 2 µL. The electrodes showed lower detection limits (LODs) of 3.4 µM, 5.97 pg/mL, and 9.55 pg/mL for dopamine, TNF-α, and IL-6 respectively, which are promising for rapid and easy analysis for biomarkers detection. Additionally, these paper-based biosensors were highly selective (no serpin A1 detection with IL-6 antibody) and were able to detect IL-6 antigen in human serum with high sensitivity and hence, the portable, adaptable, point-of-care, quick, minute sample requirement offered by our fabricated biosensor is advantageous to healthcare applications. 

Graphene nanocomposites are a promising class of advanced materials for sensing applications; yet, their commercialization is hindered due to impurity incorporation during fabrication and high costs. The aim of this work is to prepare graphene–polysulfone (G−PSU) and graphene–polyvinylidene fluoride (G−PVDF) nanocomposites that perform as multifunctional sensors and are formed using a one-step, in situ exfoliation process whereby graphite is exfoliated into graphene nanoflakes (GNFs) directly within the polymer. This low-cost method creates a nanocomposite while avoiding impurity exposure since the raw materials used in the in situ shear exfoliation process are graphite and polymers. The morphology, structure, thermal properties, and flexural properties were determined for G−PSU and G−PVDF nanocomposites, as well as the electromechanical sensor capability during cyclic flexural loading, temperature sensor testing while heating and cooling, and electrochemical sensor capability to detect dopamine while sensing data wirelessly. G−PSU and G−PVDF nanocomposites show superior mechanical characteristics (gauge factor around 27 and significantly enhanced modulus), thermal characteristics (stability up to 500 °C and 170 °C for G−PSU and G−PVDF, respectively), electrical characteristics (0.1 S/m and 1 S/m conductivity for G−PSU and G−PVDF, respectively), and distinguished resonant peaks for wireless sensing (~212 MHz and ~429 MHz). These uniquely formed G−PMC nanocomposites are promising candidates as strain sensors for structural health monitoring, as temperature sensors for use in automobiles and aerospace applications, and as electrochemical sensors for health care and disease diagnostics. 

Abstract The central nervous system’s (CNS) dopaminergic system dysfunction has been linked to neurological illnesses like schizophrenia and Parkinson’s disease. As a result, sensitive and selective detection of dopamine is critical for the early diagnosis of illnesses associated with aberrant dopamine levels. In this research, we have investigated the performance of electrochemical screen-printed sensors for different concentrations of dopamine detection using graphene-based conductive PEDOT: PSS(G-PEDOT: PSS) and Polyaniline(G-PANI) inks on the working electrode and compared the sensitivity. SEM characterization technique has been performed to visualize the microstructures of the proposed inks. We have investigated cyclic voltammetry (CV) electrochemical techniques with ferri/ferrocyanide redox couple to assess the efficiency of the designed electrodes in detecting dopamine. G-PANI ink has shown to have better LOD and stability to detect dopamine with screen-printed electrodes. Further, we have also studied electrochemical analysis for the selective detection of dopamine without the interference of Ascorbic Acid (AA). 

Abstract Infrared (IR) thermography is a non-contact method of measuring temperature that analyzes the infrared radiation emitted by an object. Properties of polymer composites are heavily influenced by the filler material, filler size, and filler dispersion, and thus thermographic analysis can be a useful tool to determine the curing and filler dispersion. In this study, we investigated the curing mechanisms of polymer composites at the microscale by capturing real-time temperature using an IR Thermal Camera. Silicone polymers with fillers of Graphene, Graphite powder, Graphite flake, and Molybdenum disulfide (MoS2) were subsequently poured into a customized 3D printed mold for thermography. The nanocomposites were microscopically heated with a Nichrome resistance wire, and real-time surface temperatures were measured using different Softwares. This infrared thermal camera divides the target area into 640 × 480 pixels, allowing measurement and analysis of the sample with a resolution of 65 micrometers. Depending on the filler material, the temperature rises to a certain maximum point before curing, and once curing is complete, polymer composites exhibit a rapid temperature change indicating a transition from viscous fluid to solid. MoS2, Polydimethylsiloxane (PDMS) without filler, and PDMS with larger filler are ranked in order of maximum constant temperature. PDMS (without filler) cures in 500s, while PDMS-Graphene and PDMS Graphite Powder cure in about 800s. The curing time for PDMS Graphite flake is slightly longer (950s), while MoS2 is around 520s. Therefore, this technique can indicate the influence of fillers on the curing of composites at the microscale, which is difficult to achieve by conventional methods such as differential scanning calorimetry. This nondestructive, low-cost, fast infrared thermography can be used to analyze the properties of polymer composites with different fillers and dispersion qualities in a variety of applications including precision additive manufacturing and quality control of curable composite inks.