Erivaldo Antônio da Silva

This study evaluated the partial and total replacement of zinc oxide (ZnO) with magnesium oxide (MgO) in styrene–butadiene rubber (SBR) composites, as well as the incorporation of ground tire rubber (GTR), aiming to develop more sustainable elastomer formulations. Ten formulations were prepared with varying ZnO/MgO ratios (100/0 to 0/100), with and without 20 phr of GTR. The composites were characterized by particle size distribution, morphology, rheometric behavior, density, crosslink density, mechanical properties, abrasion resistance, compression behavior, and thermo-oxidative aging. The results showed that hybrid ZnO/MgO activation systems exhibited a synergistic effect, enhancing vulcanization kinetics and mechanical performance compared to single-activator systems. Total replacement of ZnO by MgO was less effective, leading to reduced crosslink density and inferior properties. The addition of GTR increased compound viscosity and altered morphology but improved abrasion and compression resistance without significantly affecting tensile strength. Aging tests indicated good thermal stability, with maintenance or improvement of tensile properties due to post-curing effects. Overall, the combination of reduced ZnO content with MgO and GTR represents a viable approach for producing SBR composites with adequate performance and lower environmental impact.

This study compares the extraction of oils and bioactive compounds from Acmella oleracea using supercritical CO₂, pressurized R-134a, and propane under systematically designed experimental conditions. Extraction yields ranged from 1.16–3.35% for CO₂, 1.90–2.35% for R-134a, and 1.30–5.42% for propane. Propane achieved the highest yields and the fastest plateau (~35 min), producing extracts dominated by unsaturated fatty acids (linoleic acid ≈ 85%). Supercritical CO₂ generated the most diverse chemical profile, combining alkamides (spilanthol), triterpenoids (β-amyrone), and lipids, with a plateau at approximately 50 min, whereas R-134a selectively enriched β-amyrin acetate (~70%) with intermediate kinetics (~45 min). These yield values are typical for non-oilseed species, in which the low natural abundance of the target metabolites renders solvent selectivity more relevant than the total extract mass. Statistical modeling (R² > 0.96) confirmed that pressure was the main driver of CO₂ and propane extraction, whereas temperature dominated R-134a performance. The distinct selectivity patterns revealed by Gas chromatography–mass spectrometry (GC-MS) indicate that each solvent generates compositionally different extracts aligned with specific industrial applications in cosmetics, pharmaceuticals, and nutraceuticals. The unified comparison of these three fluids under a consistent experimental design provides practical insights for rational solvent selection: propane favors unsaturated lipids, CO₂ preserves multifunctional compositions, and R-134a targets triterpenoid esters, supporting the economic feasibility of producing enriched, solvent-free plant extracts.

Land-use and land-cover change detection is critical for monitoring deforestation and urban expansion. In this study, we propose an unsupervised change detection approach that leverages multi-temporal satellite imagery combined with a classic machine learning algorithm trained on automatically generated pseudo-labels. Four distinct study areas were analyzed: a tropical forest region in the Brazilian Amazon, an agricultural frontier in the Amazon, a Brazilian Savanna area undergoing transformation, and a rapidly expanding urban zone around the new Istanbul Airport, in Türkiye. The performance of the proposed approach was evaluated and compared with modern unsupervised change detection methods, including the Wavelet Energy Correlation Screening and the Temporal Convolutional Autoencoder methods. The results demonstrate that the proposed framework achieved consistently high accuracy across all four study areas, with F1-scores of approximately 0.92 in dense forest, 0.87 in an agricultural frontier, 0.91 in the savanna area, and 0.89 in an urban expansion zone. Overall, the model outperformed or matched the performance of the baseline methods, attesting to its adaptability and generalization capability in diverse environmental contexts worldwide.

This study evaluated the extraction of oils from the seeds of bacupari (Garcinia brasiliensis Mart.) and leiteira (Tabernaemontana catharinensis), using carbon dioxide (CO₂) in the supercritical state. The effects of temperature (40, 50, and 60 °C) and pressure (20, 24, and 28 MPa) on the yield and extraction kinetics were investigated. The results indicated that, within the studied limits, temperature had a negligible influence on the process, while pressure had a greater impact on the yields owing to its effect on the density of supercritical CO₂ and the solubility of the extracted compounds. The maximum yields obtained were 14.8% for bacupari and 15.2% for leiteira, with most of the oil extracted within the first 30 min, indicating initial rapid extraction. Chemical composition analysis revealed relevant bioactive compounds in bacupari, including oleic acid (35%) and delta-tocopherol (19.6%). In leiteira, the main compounds identified were hexanedioic acid (29.2%) and stigmast-5-ene (7.95%). These results suggest the potential application of these oils in the pharmaceutical, cosmetic, and food sectors, while also highlighting the feasibility of using supercritical CO₂ as an extraction method for these plant matrices.

This study presents, for the first time, a systematic investigation of the use of micronized post-consumer silica gel as a reinforcing filler in natural rubber composites, in direct comparison with commercial silicas. Thirteen formulations were prepared using three types of silica (recycled, Copasil, and ZC-185P) and three coupling agents (TESPT, VTMO, and Chartwell C-515.71HR^®). The recycled silica exhibited high purity (97.33% Si) and irregular morphology but resulted in lower crosslink density (0.47–0.59 × 10⁻⁴ mol·cm⁻³) and inferior mechanical performance, with tensile strength up to 7.9 MPa and high abrasion loss (878–888 mm³). In contrast, ZC-185P silica combined with TESPT achieved the best results, with a tensile strength of 18.5 MPa, tear resistance of 99.36 N·mm⁻¹, and minimum abrasion loss of 170 mm³. Although less efficient in reinforcement, composites containing recycled silica were successfully applied in the production of a functional rubber mat, demonstrating their practical viability. The results confirm the potential for valorization of spent silica gel as an alternative raw material for sustainable composites, contributing to the circular economy.

The incorporation of residues into rubber composites has gained attention as a sustainable strategy to address waste management challenges while replacing commercial fillers. In this study, we investigated the potential use of water filter cartridge residue after exhaustion, composed of activated carbon, as a reinforcing filler in vulcanized natural rubber composites. Samples were prepared with 5, 10, 15, and 20 phr (per hundred rubber) of residue and compared to unfilled natural rubber. Stress vs. strain tests reached 13.9 MPa of tension at rupture for composites containing 10 phr of carbon-activated residues, representing a 21.9% increase compared to natural rubber. Interestingly, the tension at rupture for NR/AC_10phr reached values close to those of NR/CB_5phr (with carbon black N330) attaining 14.4 MPa. These results indicate that, even at relatively low concentrations, the carbon filter can offer partial substitution for commercial fillers. Moreover, the use of activated carbon from filter cartridges as filler in rubber composites provides an environmentally favorable alternative to energy-intensive regeneration processes for activated carbon.

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.

This study investigates the recycling of ophthalmic lens waste (OLW) in the production of vulcanized natural rubber (NR) composites, aiming to promote sustainability and reduce costs. To this end, Vietnamese natural rubber and ophthalmic lens waste were used, varying the filler content from 0 to 50 phr. Rheological tests demonstrated that the addition of OLW decreases the cure time. The crosslink density, assessed through the Flory–Rehner and Mooney–Rivlin methods, exhibited an increase with the incorporation of a reinforcement. Thermal and spectroscopic analyses demonstrated the thermal stability of the composites and the absence of chemical interactions between the polymer matrix and the OLW. Mechanical tests showed that the composites exhibit satisfactory tensile and tear resistance, although the filler primarily acts as a filler rather than a structural reinforcement. Thus, the incorporation of OLW in NR composites emerges as a viable alternative for the reuse of industrial waste, fostering more sustainable and efficient practices in the polymer industry.

X-ray imaging, as a technique of non-destructive testing, has demonstrated considerable promise in COVID-19 diagnosis, particularly if supplemented with artificial intelligence (AI). Both radiologic technologists and AI researchers have raised the alarm about having to use increased doses of radiation in order to get more refined images and, hence, enhance diagnostic precision. In this research, we assess whether the disparity in exposure to the radiation dose considerably influences the credibility of AI-based diagnostic systems for COVID-19. A heterogeneous dataset of chest X-rays acquired at varying degrees of radiation exposure was run through four convolutional neural networks: VGG16, VGG19, ResNet50, and ResNet50V2. Results indicated above 91% accuracies, demonstrating that greater radiation exposure does not appreciably enhance diagnostic accuracy. Low radiation exposure sufficient to be utilized by human radiologists is therefore adequate for AI-based diagnosis. These findings are useful to the medical community, emphasizing that maximum diagnostic accuracy using AI does not need increased doses of radiation, thus further guaranteeing the safe application of X-ray imaging in COVID-19 diagnosis and possibly other medical and veterinary applications.