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How do you wash a car with graphene coating? 

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Such graphene-based nanostructured hybrid materials could have great potential as an antistatic and self-cleaning coating in various applications.
We demonstrate the first example of efficient and cost-effective graphene on silicon solar cells prepared using spray coating.
Experiments reveal that epoxy coating loading with more than 0.1 vol% of graphene exhibits a noticeable corrosion-promotion activity when the coating is mechanically damaged.
Results showed that reducing coating thickness and increasing spraying pressure are beneficial in preparing a graphene coating with high thermal conductivity.
We find that graphene coating provides remarkable resistance to the loads, and the intercalated water layer offers additional protection.
Graphene because of its inert and impermeable nature holds a great promise as a protective coating against corrosion and oxidation.
This work opens up a whole new vista on how to disperse graphene.
By keeping the height of the vertical graphene coating between 60 and 100 nm, the coating is able to effectively kill bacteria, while being completely harmless to mammalian cells.
This study provides an experimental reference for the application of a graphene coating in anti-/deicing.
Atomically smooth graphene as a surface coating has potential to improve implant properties.

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What is encapsulation?
4 answers
Encapsulation is the process of enclosing one material within another, creating particles of various sizes like nanoscale, micrometer, or millimeter-sized particles. The material being enclosed is known as the payload, core, or fill phase, while the enclosing material is referred to as the coating, membrane, shell, or capsule. Encapsulation finds extensive applications in fields like medicine, food supplements, and functional foods, aiding in targeted deliveries of drugs and sensitive compounds with reduced side effects. It is a promising strategy for nanomedicine development, especially when biomolecules or pharmaceutical ingredients are encapsulated in microemulsions, offering benefits like better drug solubilization and ease of formulation. However, the regulatory landscape for encapsulated materials, especially in micro and nanotechnology, varies across countries, with concerns about potential health and environmental impacts.
Risk Battery Thermal Runaway?
5 answers
The risk of thermal runaway in lithium-ion batteries is a critical safety concern that has garnered significant attention from various sectors due to its potential for causing fires and explosions. Thermal runaway occurs when the battery's internal temperature and pressure rise uncontrollably, leading to a self-sustaining chain reaction. This phenomenon can be triggered by several factors, including thermal abuse, electrical faults, and mechanical damage. Research has shown that the thermal management of batteries using composite phase change materials (CPCMs) can effectively reduce the risk of thermal runaway by absorbing excess heat and maintaining the battery's temperature within safe limits. Additionally, the development of predictive models integrating fault tree analysis, dynamic Bayesian networks, and support vector regression offers a promising approach for early warning and dynamic risk prediction of thermal runaway events. The onset temperature for thermal runaway and the severity of the reaction can vary depending on the battery's state of charge (SoC), with higher SoCs leading to more severe reactions. Moreover, the design of the battery, including the venting mechanisms and the chemistry of the cathode material, plays a significant role in the battery's behavior under abuse conditions and its ability to mitigate the consequences of thermal runaway. Experimental studies have also highlighted the importance of understanding the effects of radiative heat transfer on thermal runaway propagation within a battery pack, suggesting that radiation can either facilitate or mitigate the spread of thermal runaway depending on various factors. Furthermore, the ambient conditions, such as pressure and temperature, significantly influence the thermal runaway characteristics and the associated fire behaviors. In summary, managing the risk of thermal runaway in lithium-ion batteries requires a multifaceted approach that includes advanced thermal management strategies, predictive modeling for early detection, and careful consideration of battery design and operating conditions.
What are the potential applications of nanomaterials in the fight against COVID-19?
5 answers
Nanomaterials offer diverse applications in combating COVID-19. They are utilized in the production of personal protective equipment (PPE), development of efficient prevention measures like nano-based disinfectants, creation of nano-biosensors for early detection, and as carriers for vaccines and gene editing tools. Nanotechnology also aids in faster diagnostic methods using nanomaterials, and in the application of carbon nanomaterials such as graphene and carbon nanotubes for combating the SARS-CoV-2 virus. These applications highlight the significant role of nanotechnology in providing solutions for detection, prevention, and treatment strategies against COVID-19, showcasing the versatility and effectiveness of nanomaterials in the ongoing battle against the pandemic.
What are the properties of recycled concrete aggregates when strengthened by different types of pozzolan slurry?
5 answers
Strengthening recycled concrete aggregates (RCA) with various types of pozzolan slurry has shown significant improvements in properties. Studies have highlighted that treatments like pozzolan slurry with fly ash and cement enhance the mechanical properties of RCA, with an optimum replacement percentage of 60%. Additionally, combining gradation and chemical strengthening methods can effectively improve the mechanical properties and durability of RCA. Furthermore, utilizing a pozzolanic treatment slurry containing 15% cement and 5% rice husk ash (RHA) can produce concrete with characteristics similar to natural concrete aggregates, improving mechanical strength and water transport properties. These findings underscore the effectiveness of pozzolan slurry treatments in enhancing the properties of recycled concrete aggregates for sustainable construction practices.
What are the differences between PA6 and PA12 carbon fibre?
4 answers
PA6 and PA12 carbon fiber composites exhibit variations in mechanical properties. PA12 demonstrates superior mechanical and fatigue properties compared to PA6, despite PA12's almost amorphous state. On the other hand, a study comparing chopped carbon fiber reinforced polyamides found that as fiber content increased, the efficiency of the fiber in composites decreased, with the matrix type having a more significant impact on the composite properties than the fiber type. Additionally, the incorporation of multiwalled carbon nanotubes in a PA12/PA6 blend resulted in improved water diffusion behavior, with a slower diffusion rate and decreased water uptake at saturation, attributed to the rise in overall crystallinity and selective migration of nanotubes towards the more hygroscopic PA6 component.
Pure PCL can be foaming with external oil or water bath?
5 answers
Pure PCL can be foamed using supercritical carbon dioxide (scCO2) as a foaming agent, as demonstrated in various studies. When scCO2 was utilized as a dispersion medium for nanocomposite preparation and as a blowing agent, poor clay dispersion and non-uniform porous structures were observed. Additionally, the presence of clay in PCL nanocomposites resulted in increased cell density and reduced cell size during the foaming process, attributed to the higher viscosity of the melt. Furthermore, the foaming of PCL-based composites using supercritical carbon dioxide was analyzed, showing significant influence of process conditions on the properties of solid foams, with optimal parameters determined for specific applications. Therefore, external oil or water baths are not necessary for foaming pure PCL, as supercritical carbon dioxide can effectively serve as a foaming agent.
Definition of Graphene Oxide?
4 answers
Graphene oxide (GO) is a two-dimensional material derived from oxidized graphite, featuring a hexagonal lattice structure with oxygen functional groups decorating the carbon plane. It can be synthesized through exfoliation of graphite oxide using methods like sonication or mechanical stirring, with the modified Hummer’s method being a widely used technique. GO's versatile properties, including excellent electrical, mechanical, and thermal characteristics, make it suitable for various applications such as polymer composites, energy conversion materials, sensors, FET transistors, and photonic applications. Additionally, GO has emerged as a significant material in photonics, electronics, and optoelectronics, offering exceptional performance in technologies like solar energy harvesting, energy storage, medical diagnosis, image display, and optical communications.
What is value of Al2O3 thermal conductivity at liquid phase?
5 answers
The thermal conductivity of Al2O3 in the liquid phase varies based on different compositions and conditions. Research indicates that the thermal conductivity of Al2O3 can be enhanced when dispersed in liquids, such as water, to create nanofluids, leading to significant improvements in heat transfer properties. Additionally, the thermal conductivity of molten silicates containing Al2O3 has been studied, showing that it is relatively insensitive to temperature changes but depends on the composition ratio, particularly the ratio of Non-Bridging Oxygen ions per Tetrahedrally coordinated cation (NBO/T). Furthermore, the use of Al2O3 nanoparticles in water-based nanofluids has been explored, with studies showing that the thermal conductivity increases with higher nanoparticle concentrations, especially at elevated temperatures.
How does the use of DEHP enhance the mechanical properties of polymers?
5 answers
The use of di(2-ethyl hexyl)phthalate (DEHP) as a plasticizer can have varying effects on the mechanical properties of polymers. Research indicates that DEHP, when added to PVC/PMMA blends, can lead to a decrease in stress at break and Young modulus, affecting the tensile behavior and hardness. On the other hand, studies have shown that post-processing heat treatment, like heat treating 3D printed PETG parts, can significantly enhance mechanical properties such as tensile and compressive strength, reducing the performance gap between 3D printing and injection molding. Additionally, the incorporation of sorbitol-derivatives into isotactic polypropylene (i-PP) through cryomilling has been found to improve tensile strength by reducing the average spherulite size of the polymer, enhancing its mechanical properties.
PCL can be foamed according to external thermal stimulus inthe process of batch foaming process?
4 answers
PCL can indeed be foamed using external thermal stimulus in the batch foaming process. Studies have shown that the foaming behavior of PCL can be influenced by various factors such as temperature, pressure, and foaming agent concentration. Additionally, the incorporation of sacrificial materials like poly(ethyleneoxide) (PEO) can enhance the foaming process of PCL by increasing its viscosity and improving the porosity and interconnectivity of the resulting scaffolds. Furthermore, the use of supercritical carbon dioxide (scCO2) as a foaming agent has been explored in the production of porous PCL/clay nanocomposites, where different dispersion mediums like CO2-ethanol mixtures have shown improved clay dispersion and more uniform cell structures in the foamed materials. These findings collectively support the feasibility of foaming PCL with external thermal stimulus in batch foaming processes.
What challenges are there in model predictive heat pump control?
5 answers
Model predictive control (MPC) of heat pump systems faces several challenges, primarily due to the complex dynamics and nonlinearities inherent in these systems. One significant challenge is the estimation and prediction of the coefficient of performance under variable operating conditions, such as disturbances and variable water flow rates, which complicates the development of accurate control-oriented models. Additionally, the optimal operation of integrated heat exchangers within these systems introduces further complexity due to system nonlinearities and the need for adequate model identification. Non-ideal working fluids in heat exchangers add another layer of difficulty, as nonlinear fluid behaviors invalidate simplified modeling techniques, and the unavailability of fluid property measurements inside heat exchangers hampers control efforts. Moreover, the prediction of the refrigerant charge amount (RCA) in electric heat pump (EHP) systems is crucial for performance optimization, yet existing data-driven approaches for estimating RCA suffer from poor generalization and overfitting. The integration of renewable energy sources, such as photovoltaic (PV) power plants, with heat pump systems for domestic hot water and space heating purposes poses a challenging control task due to the fluctuating nature of renewable energy generation and the desire to maximize electricity self-consumption while avoiding grid disturbances. Implementing advanced controllers like MPC in building polygeneration systems to support renewable electricity grids involves handling multiple inputs and outputs, uncertainties in forecast data, and plant constraints. Furthermore, the application of heat pump water heaters (HPWHs) for secondary frequency control in power systems introduces the need for detailed modeling and predictive control to manage power consumption during water heating effectively. The fluctuating generation of renewable energy sources and the integration of thermal storage to decouple heat demand from electricity supply further complicate the control strategy, necessitating innovative optimal control strategies to minimize energy consumption and costs. Lastly, the optimization of Ground Source Heat Pump (GSHP) systems' control is a non-linear dynamic optimization problem influenced by multiple parameters, making it challenging to fully optimize with traditional methods. Artificial Intelligence and Machine Learning are identified as promising approaches to address these challenges, yet their full implementation faces significant hurdles.