Design and Characterization of an Ethosomal Gel Encapsulating Rosehip Extract

: Rising environmental awareness drives green consumers to purchase sustainable cosmetics based on natural bioactive compounds. The aim of this study was to deliver Rosa canina L. extract as a botanical ingredient in an anti-aging gel using an eco-friendly approach. Rosehip extract was first characterized in terms of its antioxidant activity through a DPPH assay and ROS reduction test and then encapsulated in ethosomal vesicles with different percentages of ethanol. All formulations were characterized in terms of size, polydispersity, zeta potential, and entrapment efficiency. Release and skin penetration/permeation data were obtained through in vitro studies, and cell viability was assessed using an MTT assay on WS1 fibroblasts. Finally, ethosomes were incorporated in hyaluronic gels (1% or 2% w/v) to facilitate skin application, and rheological properties were studied. Rosehip extract (1 mg/mL) revealed a high antioxidant activity and was successfully encapsulated in ethosomes containing 30% ethanol, having small sizes (225.4 ± 7.0 nm), low polydispersity (0.26 ± 0.02), and good entrapment efficiency (93.41 ± 5.30%). This formulation incorporated in a hyaluronic gel 1% w/v showed an optimal pH for skin application (5.6 ± 0.2), good spreadability, and stability over 60 days at 4 °C. Considering sustainable ingredients and eco-friendly manufacturing technology, the ethosomal gel of rosehip extract could be an innovative and green anti-aging skincare product

Quantification of the Uncertainty of the Peak Pressure Value in the Vented Deflagrations of Air-Hydrogen Mixtures

In the problem of the protection by the consequences of an explosion is actual for many industrial application involving storage of gas like methane or hydrogen, refuelling stations and so on. A simple and economic way to reduce the peak pressure associated to a deflagration is to supply to the confined environment an opportune surface substantially less resistant then the protected structure, typically in stoichiometric conditions, the peak pressure reduction is around the 8 bars for a generic hydrocarbon combustion in an adiabatic system lacking of whichever mitigation system. In general the problem is the forecast of the peak pressure value (PMAX) of the explosion. This problem is faced using CFD codes modelling the structure in which the explosion is located and setting the main parameters like concentration of the gas in the mixture, the volume available, the size of vent area and obstacles (if included) and so on. In this work the idea is to start from empirical data to train a Neural Network (NN) in order to find the correlation among the parameters regulating the phenomenon. Associated to this prediction a fuzzy model will provide to quantify the uncertainty of the predicted value

Application of a Neuro-Fuzzy System to calculate the Uncertainty of the peak pressure value during the deflagrations of air – hydrogen mixtures

In the problem of the protection by the consequences of an explosion is actual for many indus- trial application involving storage of gas like methane or hydrogen, refuelling stations and so on. A simple and economic way to reduce the peak pressure associated to a deflagration is to supply to the confined environ- ment an opportune surface substantially less resistant then the protected structure, typically in stoichiometric conditions, the peak pressure reduction is around the 8 bars for a generic hydrocarbon combustion in an adia- batic system lacking of whichever mitigation system. In general the problem is the forecast of the peak pres- sure value (PM) of the explosion. This problem is faced using CFD codes modelling the structure in which the explosion is located and setting the main parameters like concentration of the gas in the mixture, the volume available, the size of vent area and obstacles (if included) and so on. In this work the idea is to start from em- pirical data to train a Neural Network (NN) in order to find the correlation among the parameters regulating the phenomenon. Associated to this prediction a fuzzy model will provide to quantify the uncertainty of the predicted value