Phenolic compounds in olive leaves have an excellent antioxidant activity and good antimicrobial properties. These bioactive molecules have beneficial properties for health, arousing great scientific and commercial interest. This study reports lyophilized olive leaf extracts (OLE) encapsulated by spray-drying using maltodextrins, maltodextrins–pectin and maltodextrins–gum Arabic as encapsulating agents. Lyophilized OLE were collected from two varieties cultivated in a harsh pedo-climatic conditions of the arid region of Tunisia. The effects of the genetic factor and the different encapsulating agents on the physicochemical properties of microcapsules and their behavior during storage, as well as their antimicrobial activities, were studied. Microcapsules successfully passed heat treatment and storage conditions and their antimicrobial activities were preserved. The encapsulating agent combination improved the encapsulation efficiency and the product yield in Zarrazi variety compared to Dhokar one. In addition, Dhokar variety microparticles showed the best heat stability at 4 and 25 °C after 90 days of storage and the higher inhibition percent against bacteria. The results of the present study evidenced that the best conditions for OLE encapsulation were obtained when the maltodextrins–pectin and maltodextrins–gum Arabic were combined to form a hybrid coating material.

New-generation batteries are attracting increasing interest in response to today’s energy storage challenges, as evidenced by the steady rise in scientific publications on the topic. However, their industrial deployment remains limited due to the complexity of aging mechanisms, which are still poorly understood and difficult to control. While several promising developments have emerged in laboratory settings, they remain too immature to be scaled up. These aging processes, which directly affect the performance, safety, and lifespan of battery systems, also determine their technical and economic viability. This review offers a comparative analysis of aging phenomena—both specific to individual technologies and common across systems—drawing on findings from accelerated testing, post-mortem analyses, and modeling. It highlights critical failures such as interface instability, loss of active material, and mechanical stress, while also identifying shared patterns and the unique features of each technology. By combining experimental data with theoretical approaches, the article proposes an integrated framework for understanding and prioritizing aging mechanisms by technology type. It underscores the limitations of current characterization techniques, the urgent need for harmonized testing protocols, and the importance of standardized data sharing. Finally, it outlines possible avenues for improving the understanding and mitigation of aging phenomena.

Sodium-ion batteries are attracting increasing interest for stationary energy storage, yet their aging behavior and modeling remain less mature than for lithium-ion systems. This paper presents an initial experimental and modeling study of commercial 18650 sodium-ion cells (0.9 Ah) based on capacity measurements, incremental capacity analysis (ICA), differential voltage analysis (DVA), electrochemical impedance spectroscopy (EIS), and distribution of relaxation times (DRT). Cycling tests were performed under selected operating conditions, and the early aging response was monitored through repeated electrochemical characterization. The impedance spectra were interpreted using a DRT-guided equivalent circuit model (ECM), which was then used to reconstruct the cell voltage in the time domain under a representative current profile. The results show good alignment between measured and simulated voltage under moderate dynamic conditions, while also highlighting the limitations of the model under sharp current transients. In addition, the study reveals inter-cell variability and temperature-dependent degradation trends that deserve further statistical and mechanistic investigation. Overall, this work establishes a physically constrained modeling framework that links frequency-domain diagnostics to time-domain behavioral modeling and provides a basis for future state-of health prediction studies on commercial sodium-ion cells.