Nanospaces within enzymes play a crucial role in chemical reactions in biological systems, garnering significant attention from supramolecular chemists. Inspired by the highly efficient catalysis of enzymes, artificial supramolecular hosts have been developed and widely employed in various reactions, paving the way for innovative and selective catalytic processes and offering new insights into enzymatic catalytic mechanisms. In supramolecular macrocycle systems, weak non-covalent interactions are exploited to enhance substrate solubility, increase local concentration, and stabilize the transition state, ultimately accelerating reaction rates and improving product selectivity. In this review, we will focus on the opportunities and challenges associated with the catalysis of chemical reactions by supramolecular macrocycles in the aqueous phase. Key issues to be discussed include limitations in molecular interaction efficiency in aqueous media, product inhibition, and the incompatibility of catalysts or conditions in "one-pot" reactions.
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http://dx.doi.org/10.1039/d4cc05733c | DOI Listing |
Sci Rep
January 2025
Department of Forest Engineering, Faculty of Forestry, Kastamonu University, Kastamonu, Türkiye, Turkey.
Rapid urban growth is a subject of worldwide interest due to environmental problems. Population growth, especially migration from rural to urban areas, leads to land use and land cover (LULCC) changes in urban centres. Therefore, LULCC and urban growth analyses are among the studies that will help decision-makers achieve better sustainable management and planning.
View Article and Find Full Text PDFBioData Min
January 2025
School of Computer Science, Fudan University, Shanghai, China.
This survey explores the transformative impact of foundation models (FMs) in artificial intelligence, focusing on their integration with federated learning (FL) in biomedical research. Foundation models such as ChatGPT, LLaMa, and CLIP, which are trained on vast datasets through methods including unsupervised pretraining, self-supervised learning, instructed fine-tuning, and reinforcement learning from human feedback, represent significant advancements in machine learning. These models, with their ability to generate coherent text and realistic images, are crucial for biomedical applications that require processing diverse data forms such as clinical reports, diagnostic images, and multimodal patient interactions.
View Article and Find Full Text PDFTalanta
January 2025
MOE Key Laboratory for Analytical Science of Food Safety and Biology, College of Chemistry, Fuzhou University, Fuzhou, China. Electronic address:
The current surface-enhanced Raman scattering (SERS) substrates typically feature a single energy level, posing challenges in coordinating electromagnetic enhancement (EM) and chemical enhancement (CM), thereby limiting the sensitive detection of numerous crucial target molecules. In this study, novel aggregated nanorings (a-NRs) hybridizing Ag, Au and AgCl are constructed as SERS substrates. On one hand, the obtained a-NRs exhibit robust localized surface plasmon resonance absorption, whose wavelength can be tuned to match three commonly used laser wavelengths (532, 633 and 785 nm) to gain strong EM effect.
View Article and Find Full Text PDFCancer Chemother Pharmacol
January 2025
Department of Obstetrics and Gynecology, All India Institute of Medical Sciences, Bibinagar, Hyderabad, Telangana, 508126, India.
Introduction: Gynecological cancers, such as ovarian, cervical, and endometrial malignancies, are notoriously challenging due to their intricate biology and the critical need for precise diagnostic and therapeutic approaches. In recent years, groundbreaking advances in nanotechnology and nanobots have emerged as game-changers in this arena, offering the promise of a new paradigm in cancer management. This comprehensive review delves into the revolutionary potential of these technologies, showcasing their ability to transform the landscape of gynecological oncology.
View Article and Find Full Text PDFJ Phys Chem A
January 2025
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P. R. China.
Microkinetic modeling of heterogeneous catalysis serves as an efficient tool bridging atom-scale first-principles calculations and macroscale industrial reactor simulations. Fundamental understanding of the microkinetic mechanism relies on a combination of experimental and theoretical studies. This Perspective presents an overview of the latest progress of experimental and microkinetic modeling approaches applied to gas-solid catalytic kinetics.
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