Chitosan, which is a derivative of chitin, is particularly popular due to its biodegradable and renewable nature. However, the properties of chitosan can be inconsistent due to the extraction process and its natural origin, which poses a challenge to its use in composite materials as a matrix. The properties of chitosan can be tuned by controlling the degree of deacetylation (the extent to which acetyl groups are removed from chitin to form chitosan) and molecular weight. This paper presents a detailed study on the extraction and characterization of chitosan from shrimp shells. The structural thermal and mechanical characterization were studied using several techniques: Fourier-transform infrared spectroscopy, X-ray diffraction, thermogravimetric analysis and differential scanning calorimetry. The intrinsic viscosity and deacetylation degree were determined using various methods. The results showed an average degree of deacetylation of 77%. The chitosan films exhibited a high tensile strength of 43.9 MPa and an elongation at break of 3.14%. The thermal analysis revealed that the films had a glass transition temperature of 88 °C and a maximum thermal degradation temperature of 320 °C. The findings of this research could contribute to the development of chitosan-based materials with improved properties, leading to its wider adoption in the future for composite matrix application. The simple and efficient method used for the extraction and purification of chitosan from shrimp shells makes it a cost-effective and eco-friendly alternative to synthetic polymers.

In recent years, piezoelectric polymers have been widely used as smart materials. Electromechanical applications are currently focused on energy harvesting, including the development of autonomous device and modeling the power can be harvested by using this materials. The topic of this paper is studying the most important properties of piezo-cellular polymers in energy harvesting. The aim presents a theoretical model of the power harvested by using the piezo-cellular polymers. The theoretical model allows to determinate the power density based on cell morphology, thickness, the dielectric constant and the other factors.

This paper investigates energy harvesting performances of porous piezoelectric polymer films to collect electrical energy from vibrations and power various sensors. The influence of void content on the elastic matrix, dielectric, electrical, and mechanical properties of porous piezoelectric polymer films produced from available commercial poly(ethylene-co-vinyl acetate) using an industrially applicable melt-state extrusion method (EVA) were examined and discussed. Electrical and mechanical characterization showed an increase in the harvested current and a decrease in Young’s modulus with the increasing ratio of voids. Thermal analysis revealed a decrease in piezoelectric constant of the porous materials. The authors present a mathematical model that is able to predict harvested current as a function of matrix characteristics, mechanical excitation and porosity percentage. The output current is directly proportional to the porosity percentage. The harvested power significantly increases with increasing strain or porosity, achieving a power value up to 0.23, 1.55, and 3.87 mW/m³ for three EVA compositions: EVA 0%, EVA 37% and EVA 65%, respectively. In conclusion, porous piezoelectric EVA films has great potential from an energy density viewpoint and could represent interesting candidates for energy harvesting applications. Our work contributes to the development of smart materials, with potential uses as innovative harvester systems of energy generated by different vibration sources such as roads, machines and oceans.

Among the levers carried in the era of Industry 4.0, there is that of using Artificial Intelligence models to serve the energy interests of industrial companies. The aim of this paper is to estimate the active electrical power generated by industrial units that self-produce electricity. To do this, we conduct a case study of the historical data of the variables influencing this parameter to support the construction of three analytical models three analytical models based on Deep Learning algorithms, which are Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), as well as the hybrid CNN algorithm coupled with LSTM (CNN-LSTM). Subsequently, and thanks to the evaluation of the created models through three mathematical metrics which are Root Mean Square Error (RMSE), Mean Square Error (MSE), and the variance score (R-squared), we were able to make a comparative study between these models. According to the results of this comparison, we attested that the hybrid model is the one that gives the best prediction results, with the following findings: the variance score was about 98.29%, the value of RMSE was exactly 0.1199 MW, and for MSE the error was equal to 0.0143 MW. The obtained results confirm the reliability of the hybrid model, which can help industrial managers save energy by acting upstream of the process parameters influencing the target variable and avoiding substantial energy bills.