88 research outputs found
Multifunctional fire-resistant and flame-triggered shape memory epoxy nanocomposites containing carbon dots
No full textCarbon quantum dots (CDs) are widely used as semiconductor systems, due to their facile synthesis and optical characteristics. CDs have been employed to enhance the light emission, UV resistance, and anticorrosive performances of epoxy nanocomposites (ENCs). Herein, we investigated the use of CDs to prepare multifunctional cycloaliphatic
ENCs showing shape recovery capability. Following a waste-to-wealth approach, CDs were obtained from humic acid by a hydrothermal route. ENCs containing CDs exhibited photoluminescence, while the simultaneous addition of CDs and hexadecyltrimethoxysilane accounted for heat/flame-triggered shape recovery capability
and hydrophobicity (contact angles as high as 137°). The polar character of silane-functionalized CDs allowed for their segregation at the surface of ENCs, making them very fire resistant. In particular, the CDs exerted an outstanding thermal shielding effect on the surface of ENCs, lowering the heat transfer at the boundary layer
and increasing the time to flaming combustion up to ∼ 76 %. Besides, the graphitic nature of CDs and their charring behavior led to a huge increase in the back temperature at the ignition point (up to ∼ 30 %) during burn-through tests. Notwithstanding the low loadings (not exceeding 0.3 wt%) of CDs, ENCs lost their structural integrity only after almost 1 min of blowpipe flame application to their surface, whereas the resin counterpart degraded in less than 20 s. Besides, cone calorimetry tests carried out on ENCs highlighted a significant reduction of total smoke release (up to ∼ 40 %) compared to unfilled epoxy. Finally, we demonstrated the possibility of using multifunctional ENCs containing CDs as unique identification technology for polypropylene packaging, opening
new perspectives on the effective protection of genuine products from counterfeiting
Flame retardant and shape recovery epoxy nanocomposites containing carbon quantum dots
No full textMultifunctional epoxy nanocomposites, containing waste-derived carbon quantum dots at very low loadings (namely, 0.1 and 0.3 wt.%), are designed and developed. They exhibit photoluminescence, high hydrophobicity, fire resistance, and heat/flame-triggered shape recovery features. These all-in-one peculiarities can be achieved using very simple formulations, without employing halogen- or phosphorus-based flame retardants, avoiding any specific modification of the polymer network
Multifunctional nanostructured composites containing biomass-derived functional additives
No full textDue to their superior thermal stability and chemical resistance, epoxy resins represent a primary choice in several industrial applications, including the fabrication of protective and functional coatings. The addition of properly designed fillers allows for the preparation of coatings showing surface hydrophobicity, anti-icing, shape recovery capability, luminescence, and improved flame retardance, required to contrast the high flammability of such materials. Herein, we propose two approaches to enhance these properties by sustainable sol-gel methodologies: the functionalization of hemp microparticles (HMPs), obtained from waste hemp fibers, to turn them into hydrophobic and anti-icing fillers [1], and the tailoring of carbon quantum dots (CQDs), hydrothermally synthesized starting from humic acids, to make them able to act as flame retardant and hydrophobic agents [2]. To give an idea, thanks to their suitable surface chemistry and hierarchical rough structure, 2 wt.% of hydrophobic HMPs, cast on aeronautical carbon fiber-reinforced panels, showed up to 30° higher water contact angle (CA) at room temperature and doubled icing time at -30 °C than unfilled epoxy resin coatings. On the other side, 0.1 wt.% of silanized CQDs added into the epoxy matrix, without using phosphorus and halogen-based flame retardants, could lead to nanocomposites exhibiting photoluminescence, high hydrophobicity (up to 137° of CA), fire resistance, and heat/flame-triggered shape recovery features
Multifunctional Shape Memory Epoxy Nanocomposites Containing Carbon Dots
No full textRecent statistics have demonstrated that the direct reuse of polymeric wastes can be considered one of the most sustainable and advantageous approaches. However, many applications require the use of products with specific shape and geometry: in this context, thermosetting wastes may be very difficult to readapt for new uses. To overcome this challenging issue, the scientific literature has witnessed the potential of shape memory epoxy thermosets. The shape recovery can be triggered by various stimuli, including pH, heat, and electricity, though the most studied is the thermally-induced shape recovery. Shape memory epoxies are employed as components in the transportation industry, where the demand for transparent and fire-resistant coatings is
enormously increasing. Mostly, the flame retardancy of such coatings is improved by the chemical modification of the polymer matrix, together with the use of specific flame retardants. Unfortunately, these approaches often lead to the loss of transparency and a detrimental effect on the overall mechanical performance of the final product.
In our work, we report the design and development of new multifunctional epoxy nanocomposites that exhibit photoluminescence, high hydrophobicity, fire resistance, and heat/flame-triggered shape recovery features [1]. It is worth noting that these all-in-one peculiarities can be achieved using very simple formulations, without using halogen- or phosphorus-based flame retardants, avoiding any specific modification of the polymer network, and employing carbon dots at very low loadings (namely, 0.1 and 0.3 wt.%). These latter are synthesized starting from vegetable wastes by a sustainable hydrothermal route, hence also fulfilling the circular economy concept [1]
Flame retardant and shape recovery epoxy nanocomposites containing carbon quantum dots
No full textMultifunctional epoxy nanocomposites, containing waste-derived carbon quantum
dots at very low loadings (namely, 0.1 and 0.3 wt.%), are designed and developed. They exhibit
photoluminescence, high hydrophobicity, fire resistance, and heat/flame-triggered shape recovery
features. These all-in-one peculiarities can be achieved using very simple formulations, without
employing halogen- or phosphorus-based flame retardants, avoiding any specific modification of the
polymer network
Prediction and validation of fire parameters for a self-extinguishing and smoke suppressant electrospun PVP-based multilayer material through machine learning models
No full textElectrospinning is a technology largely employed to obtain polymer fibers with different functionalities. The electrospinning of polyvinylpyrrolidone (PVP) in the presence of silica nanoparticles, and the subsequent thermal treatment of these electrospun PVP-silica fibers, allows for the manufacturing of a self-extinguishing material stable in polar solvents. However, this material lacks consistency and does not sustain any load: this strongly limits its application in many industrial fields (e.g., the aerospace sector). Herein, we used cross-linked electrospun PVP-silica blankets and TiO2 nanoparticles to coat hemp blankets, producing a multilayer material (MM) by surface charge interaction. The MM exhibited lower stiffness than the original hemp fabric but still good mechanical behavior, V0 class at the UL 94 vertical burning test, and good stretchability even after direct flame exposure. Further, burn-through and cone calorimetry tests revealed that MM is an excellent smoke suppressant and fireproof fabric, with very low total smoke release values (as low as 4.9 vs. 33.3 m2/m2 measured for hemp) and its structure remained intact for at least 1 min. Finally, as all the aforementioned experimental activity, though necessary and unsubstantial, is usually quite time-consuming, two Machine Learning models were developed and exploited to predict the fire performances related to the multilayer material. Despite the incomplete starting datasets, the implemented models accounted for a successful prediction of the target parameters (namely, Time to Ignition and peak of Heat Release Rate), thanks to the assistance of ChatGPT and the exploitation of made-on-purpose decision trees
La concezione dei giudei come eretici tra tarda antichità e alto medioevo
No full textBetween Late Antiquity and Early Middle Ages, many times Jews are equated with heretics in Patristic literature, imperial law, council canons. Comparison with later hagiographical texts likening Jews to the heretics concurs in casting light on some sides of relationships between Jews, Christian and heretics and reconstructing a sort of Christian “ideology” about Judaism and other religious groups. Sources considered by the Author reflect a negative categorization of Judaism according to models and formulas which were crystallizing on the basis of ideological, political and religious propaganda. Such stereotypes shed light on the attitude and the positions of predominant groups towards presences, such as that of Jews, heretics and Muslims, felt like “different” or “other” ones, disrupting the Christian Orthodox community
Charles e Ray Eames
No full textThis essay critically develops the connections between cinema, art, advertising graphics, design, exhibition design and architecture in the cultural work of Charles and Ray Ea¬mes. The author identifies the two designers’ greatest and most demanding “democratic design” project in their film work
AI-driven design and optimization of flame retardant epoxy nanocomposites and textiles
No full textThe development and optimization of flame retardant (FR) materials usually require time-consuming, expensive, and destructive measurements. However, in most cases the material available for flammability and fire performance testing is limited. In this view, machine learning (ML) tools can be very useful to predict the fire parameters of polymeric materials or textiles, starting from an input dataset of properties (e.g., thermal or physico-chemical characteristics accessible in the literature) belonging to similar systems. Herein, we demonstrate the suitability of ML algorithms for the design and development of FR hybrid epoxy nanocomposites and functional textiles. Artificial neural network-based systems built on fully connected feed-forward artificial neural networks can successfully be employed for the prediction of heat release capacity of FR hybrid Mg(OH)2-epoxy nanocomposites. Electrospun fibres can be used to coat hemp blankets and obtain a fire shielding multilayer material. Despite the incomplete starting datasets, ML with generative AI (ChatGPT) approaches allow to exploit made-on-purpose decision trees and artificial neural networks to finely predict the time to ignition and the peak of heat release rate of the multilayer material
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