Ethylene propylene diene monomer (EPDM) is a synthetic rubber widely used in industry and commerce due to its high thermal and chemical resistance. Nanotechnology has enabled the incorporation of nanomaterials into polymeric matrixes that maintain their flexibility and conformation, allowing them to achieve properties previously unattainable, such as improved tensile and chemical resistance. In this work, we summarize the influence of different nanostructures on the mechanical, thermal, and electrical properties of EPDM-based materials to keep up with current research and support future research into synthetic rubber nanocomposites.

In the last decade, natural fibers have had a significant impact on the research and development of innovative composites made with natural rubber, improving their properties over those of their counterparts that incorporate polluting synthetic fibers. In recent years, this fact has stimulated the research into several modified natural rubber composites reinforced with vegetable fibers. This paper reviews the scientific literature published in the last decade about the properties and characteristics of natural vegetable fibers and natural rubber used in composites. Nowadays the use of alternative materials has become necessary, considering that synthetic materials have caused irreversible damage to the environment, being associated with global warming, for this reason research and development with materials that print a lower carbon footprint during the manufacturing process and subsequent product manufacturing. This review is an invitation to the use of vegetable fibers, as well as vegetable-type matrices, in this case natural rubber as a binder system, it is fantastic to know the different works carried out by other scientists and engineers, in this way to project new compounds linked to innovation in processes that reduce the carbon footprint and its negative impact on our planet.

Nowadays, biocomposites represent a new generation of materials that are environmentally friendly, cost-effective, low-density, and not derived from petroleum. They have been widely used to protect the environment and generate new alternatives in the polymer industry. In this study, we incorporated untreated jute fibers (UJFs) and alkaline-treated jute fibers (TJFs) at 1–5 and 10 phr into TSR 10 natural rubber as reinforcement fillers. These composites were produced to be used in countersole shoes manufacturing. Untreated fibers were compared to those treated with 10% sodium hydroxide. The alkali treatment allowed the incorporation of fibers without compromising their mechanical properties. The TJF samples exhibited 8% less hardness, 70% more tensile strength, and the same flexibility compared to their pure rubber counterparts. Thanks to their properties and ergonomic appearance, the composites obtained here can be useful in many applications: construction materials (sound insulating boards, and flooring materials), the automotive industry (interior moldings), the footwear industry (shoe soles), and anti-static moldings. These new compounds can be employed in innovative processes to reduce their carbon footprint and negative impact on our planet. Using the Lorenz–Park equation, the loaded composites examined in this study exhibited values above 0.7, which means a competitive load–rubber interaction. Scanning electron microscopy (SEM) was used to investigate the morphology of the composites in detail.

Polymeric biocomposites are emerging as a new generation of eco-friendly and cost-effective materials that provide sustainable alternatives for the polymer industry while supporting environmental conservation. This study investigates the mechanical behavior of Low-Density Polyethylene (LDPE) compounds blended with natural rubber (NR) and reinforced with silanized Sugarcane Bagasse Ash (SCBA), chemically modified with bis(3 triethoxysilylpropyl) tetrasulfide (TESPT). Blends were formulated in LDPE/NR-SCBA weight ratios (wt%) of 90/10, 70/30, and 50/50, and processed at mixing speeds of 40 and 80 rpm to evaluate their potential as thermoplastic additives. Mechanical testing showed that blends mixed at 80 rpm achieved an 86% increase in elongation, while those processed at 40 rpm demonstrated a 78% enhancement in tensile strength. The incorporation of NR and vulcanizing systems markedly improved the overall mechanical properties of the composites. These biocomposites present promise for applications in the footwear industry (especially for soles) and for ergonomic molded components by conferring the advantageous combination of mechanical performance and esthetic appeal. Furthermore, development supports innovative manufacturing processes and contributes to reducing the industry`s carbon footprints, mitigating its negative impact on the planet.

The influence of the initial surface roughness on the wheel–rail contact has been a concern for railway systems especially since the introduction of rail grinding and wheel reprofiling as maintenance strategies. Currently, there are no universally accepted procedures for surface finishing of rails and wheels, so there is a need to keep studying the role of surface topography in reducing the maintenance costs and improving the wheel–rail system’s performance. This work studies the evolution of roughness of wheel samples during twin-disc tests performed to simulate wheel–rail contact up to 2.2 × 10⁵ cycles under two different lubrication conditions. The results showed that under dry conditions the surfaces with larger kurtosis at the beginning of the tests had a better response to rolling contact fatigue. Under lubricated conditions, however, the effect of the initial roughness was less significant. GRAPHICAL ABSTRACT

The efficiency decline of Francis turbines, a key component of hydroelectric power generation, presents a multifaceted challenge influenced by interconnected factors such as water quality, incidence angle, erosion, and runner wear. This paper is structured into two main sections to address these issues. The first section applies the Buckingham π theorem to establish a dimensional analysis (DA) framework, providing insights into the relationships among the operational variables and their impact on turbine wear and efficiency loss. Dimensional analysis offers a theoretical basis for understanding the relationships among operational variables and efficiency within the scope of this study. This understanding, in turn, informs the selection and interpretation of features for machine learning (ML) models aimed at the predictive maintenance of the target variable and important features for the next stage. The second section analyzes an extensive dataset collected from a Francis turbine in Colombia, a country that is heavily reliant on hydroelectric power. The dataset consisted of 60,501 samples recorded over 15 days, offering a robust basis for assessing turbine behavior under real-world operating conditions. An exploratory data analysis (EDA) was conducted by integrating linear regression and a time-series analysis to investigate efficiency dynamics. Key variables, including power output, water flow rate, and operational time, were extracted and analyzed to identify patterns and correlations affecting turbine performance. This study seeks to develop a comprehensive understanding of the factors driving Francis turbine efficiency loss and to propose strategies for mitigating wear-induced performance degradation. The synergy lies in DA’s ability to reduce dimensionality and identify meaningful features, which enhances the ML models’ interpretability, while ML leverages these features to model non-linear and time-dependent patterns that DA alone cannot address. This integrated approach results in a linear regression model with a performance (R²-Test = 0.994) and a time series using ARIMA with a performance (R²-Test = 0.999) that allows for the identification of better generalization, demonstrating the power of combining physical principles with advanced data analysis. The preliminary findings provide valuable insights into the dynamic interplay of operational parameters, contributing to the optimization of turbine operation, efficiency enhancement, and lifespan extension. Ultimately, this study supports the sustainability and economic viability of hydroelectric power generation by advancing tools for predictive maintenance and performance optimization.