Sugarcane has primarily been used for sugar and ethanol production. It creates large quantities of residual lignocellulosic biomass such as sugarcane bagasse, leaves, tops, and vinasse. Biomass is a sustainable prospect for biorefineries aiming to optimize production processes. We detail recent research developments in recycling sugarcane, including energy generation and pyrolysis to obtain biofuels, for example. To produce biochar, the energy cost of operating at high temperatures and large-scale production remain as obstacles. The energy generation prospects can be enhanced by pellet production; however, it requires an improvement in quality control for long-term storage or long-distance transportation. In civil construction, the materials still need to prove their long-term efficiency and reliability. Related to adsorbent materials, the use of sugarcane bagasse has the advantage of being low-cost and environmentally friendly. Nevertheless, the extraction, functionalization, and modification of cellulose fibers, to improve their adsorption properties or even mode of operation, still challenges. The synthesis of nanostructures is still lacking high yields and the ability to scale up. Finally, controlling dispersion and orientation and avoiding fiber agglomeration could improve the mechanical response of composites using sugarcane bagasse. The different possibilities for using sugarcane and its residues reinforce the importance of this material for the industry and the global economy. Thus, the present work addresses current challenges and perspectives of different industrial processes involving sugarcane aiming to support future research on waste-derived subjects.

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.

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.

The growing demand for more sustainable routes and processes in the mixture separation and purification industry has generated a need to search for innovations, with new solvent alternatives being a possible solution. In this context, a new class of green solvents, known as deep eutectic solvents (DESs), has been gaining prominence in recent years in both academic and industrial spheres. These solvents, when compared to ionic liquids (ILs), are more environmentally friendly, less toxic, low-cost, and easier to synthesize. In addition, they have significantly lower melting points than their precursors, offering a promising option for various applications in this industrial sector. Understanding and studying the thermodynamic behavior of systems composed of these substances in purification and separation processes, such as liquid–liquid extraction and azeotropic distillation, is extremely important. This work aimed to study the phase behavior of liquid–liquid equilibrium (LLE) and vapor–liquid equilibrium (VLE) of water + 1-butanol + DES (choline chloride + glycerol) systems with a molar ratio of 1:2. Experimental LLE data, obtained at 298.15 K and 101.3 kPa, and VLE data, obtained at 101.3 kPa and in the temperature range of 364.05 K–373.85 K, were submitted to the thermodynamic quality/consistency test, proposed by Marcilla et al. and Wisniak, and subsequently modeled using the gamma–gamma approach for the LLE and gamma–phi for the VLE. The non-random two-liquid (NRTL) model was used to calculate the activity coefficient. The results are presented for the VLE in a temperature–composition phase diagram (triangular prism) and triangular phase diagrams showing the binodal curve and tie lines (LLE). The separation and distribution coefficients of LLE were determined to evaluate the extractive potential of the DES. For the VLE, the values of the relative volatility of the system were calculated, considering the entrainer free-basis, to evaluate the presence or absence of azeotropes in the range of collected points. From these data, it was possible to compare DES with ILs as extracting agents, using data from previous studies carried out by the research group. Therefore, the results indicate that the NRTL model is efficient at correlating the fluid behavior of both equilibria. Thus, this study serves as a basis for future studies related to the understanding and design of separation processes.

The application of artificial intelligence within forensic image analysis marks a significant step forward for the non-destructive examination of evidence, a crucial practice for maintaining the integrity of a crime scene. While non-destructive testing (NDT) methods are established, the integration of AI, particularly for analyzing ballistic evidence, requires further exploration. This preliminary study directly addresses this gap by focusing on the use of deep learning to automate the analysis of bullet holes. This work investigated the performance of two state-of-the-art convolutional neural networks (CNNs), YOLOv8 and R-CNN, for detecting ballistic markings in digital images. The approach treats digital image analysis itself as a form of non-destructive testing, thereby preserving the original evidence. The findings demonstrate the potential of AI to augment forensic investigations by providing an objective, data-driven alternative to traditional assessments and increasing the efficiency of evidence processing. This research confirms the feasibility and relevance of leveraging advanced AI models to develop powerful new tools for Forensic Science. It is expected that this study will contribute worldwide to help (1) the police indict criminals and prove innocence; (2) the justice system judges and proves people guilty of their crimes.