Daniele Brandão

Crude oil drives the global economy but also represents one of the most serious environmental threats when it reaches aquatic ecosystems, causing severe and persistent damage. Traditional decontamination methods remain limited in efficiency, cost, and operational time, highlighting the need for more sustainable and effective alternatives. In this context, nanotechnology emerges as a strategic tool for hydrocarbon mitigation, with magnetic nanoparticles (Fe₃O₄) standing out for their ability to adsorb organic compounds and be magnetically recovered through external magnetic fields. This study proposes an innovative and eco-friendly approach: the green synthesis of magnetic nanoparticles using the extract of banana peel from the Prata cultivar (Musa spp.), an abundant and low-cost agroindustrial residue, followed by cationic modification with cetyltrimethylammonium bromide (CTAB) to enhance petroleum removal efficiency. A Plackett–Burman experimental design was applied to optimize synthesis parameters, and the resulting nanoparticles were characterized by XRD, FTIR, SEM–EDS, and magnetic force measurements. The results showed that the green route, combined with cationic modification, produced nanoparticles with smaller crystallite size, high colloidal stability, strong magnetic responsiveness, and an oil removal efficiency exceeding 90% in less than 20 minutes. In conclusion, green synthesis coupled with CTAB functionalization offers a sustainable, economical, and high-performance alternative for the treatment of oily wastewater, converting agricultural waste into high-value materials and contributing to environmental remediation.

This article is a narrative expansion of a talk delivered to graduate students of the Programa de Engenharia e Tecnologia (PENT) at the Universidade Federal do Rio de Janeiro (UFRJ). Blending personal anecdotes with institutional context, it traces the evolution of two laboratory lines—LaBioS and LabOPTIMA—while illustrating how data-driven methodologies and ethical artificial intelligence (AI) are reshaping research on nanocomposites, drug delivery, renewable energy, and environmental remediation. Student-led projects, thematic analysis of scientific literature, and reflections on academic development are interwoven to guide newcomers as they navigate the complexities of contemporary graduate science.

Background/Objectives: Radiotherapy, radiobiology, and oncology have evolved rapidly over the past six decades. This progress has generated vast but fragmented bodies of scientific evidence. The present study aimed to systematically map and interpret their conceptual and temporal development using artificial intelligence (AI)-based methods. It highlights the integration between molecular mechanisms, clinical applications, and technological innovation within a precision radiotherapy framework. Methods: A corpus of 3343 unique articles (1964–2025) was retrieved from Scopus, PubMed, and Web of Science. Records were harmonized through deduplication, lemmatization, and metadata normalization. Topic modeling using Latent Dirichlet Allocation (LDA) and co-occurrence network analysis were applied to identify dominant research axes. Semantic and temporal analyses were conducted to reveal patterns, emerging trends, and translational connections across decades. Results: Three historical phases were identified. The first was a period of limited production (1964–1990). The second showed moderate growth (1991–2010). The third, from 2011 to 2024, represented exponential expansion, with publication peaks in 2020 and 2023. LDA revealed two principal axes. The first, a clinical–anatomical axis, focused on cancer sites, treatment modalities, and prognosis. The second, a mechanistic–molecular axis, centered on DNA repair, radiosensitivity, and biomarkers. Case synthesis from 2014–2025 defined five operational classes: DNA repair and molecular response; precision oncology and genomic modeling; individual radiosensitivity; mechanisms of radioresistance; and advanced technologies such as FLASH radiotherapy and optimized brachytherapy. Conclusions: AI-driven semantic and temporal analyses showed that radiotherapy has matured into an interconnected and interdisciplinary domain. The derived Precision Radiotherapy Implementation Plan translates molecular and computational insights into clinically actionable strategies. These approaches can enhance survival, reduce toxicity, and inform equitable health policies for advanced cancer care.

Background/Objectives: Radiotherapy, radiobiology, and oncology have evolved rapidly over the past six decades, generating vast and fragmented bodies of scientific evidence. This study aimed to systematically map and interpret their conceptual and temporal development using artificial intelligence (AI)–based methods, highlighting the integration between molecular mechanisms, clinical applications, and technological innovation toward a precision radiotherapy framework. Methods: A corpus of 3,343 unique documents (1964–2025) was retrieved from Scopus, PubMed, and Web of Science. Records were harmonized through deduplication, lemmatization, and metadata normalization. Topic modeling using Latent Dirichlet Allocation (LDA) and co-occurrence network analysis were applied to identify dominant research axes. Semantic and temporal analyses were conducted to reveal patterns, emerging trends, and translational connections across decades. Results: Three historical phases were identified: an initial period of limited production (1964–1990), moderate growth (1991–2010), and exponential expansion (2011–2024), with peaks in 2020 and 2023. LDA revealed two principal axes: a clinical–anatomical axis focused on cancer sites, treatment modalities, and prognosis, and a mechanistic–molecular axis centered on DNA repair, radiosensitivity, and biomarkers. The co-occurrence supergraph showed high density (0.5) and low modularity (0.02), indicating strong thematic integration. Case synthesis from 2014–2025 defined five operational classes: DNA repair and molecular response, precision oncology and genomic modeling, individual radiosensitivity, mechanisms of radioresistance, and advanced technologies such as FLASH radiotherapy and optimized brachytherapy. Conclusions: AI-driven semantic and temporal analyses revealed that radiotherapy has matured into an interconnected, interdisciplinary domain. The derived Precision Radiotherapy Implementation Plan translates molecular and computational insights into clinically actionable strategies that can enhance survival, reduce toxicity, and inform equitable health policies for advanced cancer care.

The development of thermally conductive and electromagnetic interference (EMI) shielding nanocomposites is becoming increasingly critical for next-generation electronic devices, where effective heat dissipation and high signal integrity are essential. This report summarizes a talk by Professor Sung-Ryong Kim from the Korea National University of Transportation, emphasizing structural design strategies—such as layered architectures, segregated structures, three-dimensional (3D) interconnected networks, and stretching-induced alignment—to enhance both thermal conductivity and EMI shielding effectiveness in polymer-based nanocomposites. By integrating nanofillers such as graphene fluoride (GF), MXene, and carbon nanotubes, Professor Kim’s work highlights how rational structural design can yield ultrathin films with thermal conductivity values up to 210 W/m·K and EMI shielding effectiveness (SE) reaching 62 dB under optimal filler contents. The strategies address not only GHz and THz frequency shielding demands but also practical industry concerns like cost, scalability, and mechanical robustness. Reflecting on potential future directions, Professor Kim underscored the promise of integrating artificial intelligence (AI) to expedite predictive material optimization, and he expressed enthusiasm for collaborating with computational scientists to guide advanced composites research. This convergence of data-driven modeling, sustainable polymer matrices, and industrial manufacturing constraints points the way to commercializable, high-performance thermoconductive EMI shielding solutions.

From 1990 to 2024, this study presents a groundbreaking bibliometric and sentiment analysis of nanocomposite literature, distinguishing itself from existing reviews through its unique computational methodology. Developed by our research group, this novel approach systematically investigates the evolution of nanocomposites, focusing on microstructural characterization, electrical properties, and mechanical behaviors. By deploying advanced Boolean search strategies within the Scopus database, we achieve a meticulous extraction and in-depth exploration of thematic content, a methodological advancement in the field. Our analysis uniquely identifies critical trends and insights concerning nanocomposite microstructure, electrical attributes, and mechanical performance. The paper goes beyond traditional textual analytics and bibliometric evaluation, offering new interpretations of data and highlighting significant collaborative efforts and influential studies within the nanocomposite domain. Our findings uncover the evolution of research language, thematic shifts, and global contributions, providing a distinct and comprehensive view of the dynamic evolution of nanocomposite research. A critical component of this study is the “State-of-the-Art and Gaps Extracted from Results and Discussions” section, which delves into the latest advancements in nanocomposite research. This section details various nanocomposite types and their properties and introduces novel interpretations of their applications, especially in nanocomposite films. By tracing historical progress and identifying emerging trends, this analysis emphasizes the significance of collaboration and influential studies in molding the field. Moreover, the “Literature Review Guided by Artificial Intelligence” section showcases an innovative AI-guided approach to nanocomposite research, a first in this domain. Focusing on articles from 2023, selected based on citation frequency, this method offers a new perspective on the interplay between nanocomposites and their electrical properties. It highlights the composition, structure, and functionality of various systems, integrating recent findings for a comprehensive overview of current knowledge. The sentiment analysis, with an average score of 0.638771, reflects a positive trend in academic discourse and an increasing recognition of the potential of nanocomposites. Our bibliometric analysis, another methodological novelty, maps the intellectual domain, emphasizing pivotal research themes and the influence of crosslinking time on nanocomposite attributes. While acknowledging its limitations, this study exemplifies the indispensable role of our innovative computational tools in synthesizing and understanding the extensive body of nanocomposite literature. This work not only elucidates prevailing trends but also contributes a unique perspective and novel insights, enhancing our understanding of the nanocomposite research field.

This research presents a comprehensive bibliometric and sentiment analysis of nanocomposite literature from 1990 to 2024. Employing cutting-edge computational methods, the study delves deep into the progression of microstructural characterization, electrical properties, and mechanical behaviors of nanocomposites. The analysis utilizes advanced Boolean search strategies from the Scopus database, ensuring a thorough extraction of thematic content. The results explore various themes and insights, shedding light on trends evident in the Scopus database, particularly the prominence of research on nanocomposite microstructure, electrical attributes, and mechanical performance. The paper also offers textual analytics and bibliometric data, showcasing critical collaborative efforts and influential studies. Significant discoveries encompass the evolution of research language, shifts in thematic focus, and global contributions, providing a comprehensive perspective on the current landscape of nanocomposite research and its dynamic evolution. Moving forward, the "State-of-the-Art and Gaps Extracted from Results and Discussions" section of the paper delves into the most recent advancements in nanocomposite research. It delves into various types of nanocomposites, with a particular emphasis on their microstructural characteristics, electrical properties, mechanical dynamics, and the application of nanocomposite films. This section identifies key research themes, traces historical progress, and highlights emerging trends while underscoring the significance of collaboration and the influence of pivotal studies that have shaped the field. Lastly, in the "Literature Review Guided by Artificial Intelligence" section, the paper introduces a revised approach for researching nanocomposites through AI-guided techniques. This approach prioritizes articles published in 2023 based on citation frequency. Here, the focus is on exploring the relationship between nanocomposites and their electrical properties, emphasizing their fundamental interactions and impact on electrical characteristics. Various nanocomposite systems are covered, highlighting their composition, structure, and functionality. Findings from recent studies are integrated to provide a comprehensive overview of the current state of knowledge in this area. Notably, the sentiment analysis, anchored by an average sentiment score of 0.638771, underscores a positive trajectory in academic discourse, emphasizing the growing recognition of the potential of nanocomposites. The bibliometric exploration maps the intellectual domain, emphasizing pivotal research themes and the influence of crosslinking time on nanocomposite attributes. While this study is thorough, it acknowledges its limitations and advocates for broader database inclusion in future research endeavors. This work elucidates the prevailing trends in nanocomposite research, emphasizing the indispensable role of computational tools in comprehending this vast wealth of information.

Geopolymers offer an alternative to Portland cement, boasting high mechanical strength, as well as chemical and thermal durability. It is an inorganic polymer that has environmental advantages in its production, in addition to being considered a high-performance material. This study seeks to evaluate the mechanical properties of geopolymeric mortars reinforced with Carbon Nanotubes (CNT) as well as the method of incorporation into the mortar. CNTs are promising materials as reinforcements in geopolymeric matrices for the development of construction materials with specific applications aimed at better performance. The studied matrix results from the alkaline activation of metakaolin (MK) with sodium silicate and sodium hydroxide. CNTs were added in percentages of 0.1%, 0.3%, and 0.5% by mass of metakaolin. The evaluated properties for the resulting composite were compressive strength and flexural strength. Scanning Electron Microscopy (SEM) and Infrared Spectroscopy (FTIR) tests were carried out to evaluate the dispersion of CNTs in MK (before activation). The results show that when well distributed in the matrix, CNTs promote an increase in compressive and flexural strength of the samples.