Advancing Hybrid Nanocatalyst Research: A Python-based
Visualization of Similarity Analysis for Interdisciplinary and
Sustainable Development

Fernando      Gomes Souza; Kaushik      Pal; Fabíola      Maranhão; Carlos      Zanoni; Daniele      Brandão; Michelle      Colão; Gabriel      Silva; Jeffrey      Ampah; Karine      Velasco
doi:10.2174/0115734137274085231214100609
Abstract

Background: This study presents a comprehensive analysis of hybrid nanocatalysts, which amalgamate attributes of both heterogeneous and homogeneous catalysts.
Aim: To achieve a holistic understanding of the topic, we embarked on a meticulous exploration across multiple databases.
Methods: The Web of Science repository yielded 239 pertinent documents, while the Scopus database offered a more exhaustive collection of 1,887 documents. Although Google Scholar suggested a staggering 25,000 articles, its unclear selection criteria raised questions about the precision and dependability of its data. Hence, our study primarily relied on the Scopus database to ensure an extensive sample and analytical rigor. Using the Python-boosted visualization of Similarities methodology, we illuminated interconnections among various terminologies, identifying burgeoning areas within hybrid nanocatalyst research.
Results: Our findings emphasized the ascending trajectory toward innovating materials with superior properties in hybrid nanocatalysis. This trajectory accentuated the pivotal role of interdisciplinary collaboration and sustainable methodologies. Advanced analytical techniques, notably X-ray diffraction, emerged as quintessential in delineating the nuanced relationship between hybrid nanocatalysts' structural and functional attributes. We also spotlighted Energy-Dispersive X-ray Spectroscopy's capability in fine-tuning hybrid nanocatalysts' properties, enhancing their catalytic efficacy and selectivity. An intriguing trend our study unearthed was the surge in interest toward integrating natural enzymes as potential catalysts within hybrid nanocatalysts, positioning them as beacons for sustainable and cost-efficient catalyst development.
Conclusion: By synthesizing these insights, this research underlines the significance of diverse characterization techniques and the ethos of interdisciplinary collaboration. The derived knowledge offers a repository for fellow researchers, guiding further inquiries, especially regarding integrating natural enzymes in hybrid nanocatalyst innovation.
Keywords: Hybrid nanocatalysts, data mining, data analysis, X-ray diffraction, Energy-dispersive X-ray spectroscopy, Natural enzyme, Python Boosted Visualization of Similarities.
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Graphical Abstract

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