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The 1,7-diacetate-4,10-diacetamide substituted 1,4,7,10-tetraazacyclododecane structural unit is common to several responsive Magnetic Resonance Imaging (MRI) contrast agents (CAs). While some of these complexes (agents capable of sensing fluctuations in Zn, Ca etc. ions) have already been tested in vivo, the detailed physico-chemical characterization of such ligands have not been fully studied. To fill this gap, we synthesized a representative member of this ligand family possessing two acetate and two n-butylacetamide pendant side-arms (DO2A2M = 1,4,7,10-tetraazacyclodoecane-1,7-di(acetic acid)-4,10-di(N-butylacetamide)), and studied its complexation properties with some essential metal and a few lanthanide(III) (Ln(III)) ions. Our studies revealed that the ligand basicity, the stability of metal ion complexes, the trend of stability constants along the Ln(III) series, the formation rates of the Ln(III) complexes and the exchange rate of the bound water molecule in the Gd(III) complex fell between those of Ln(DOTA) and Ln(DOTA-tetra(amide)) complexes (DOTA = 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid, DOTAM = 1,4,7,10-tetrakis(carbamoylmethyl)-1,4,7,10-tetraazacyclododecane). The only exception is the stability of Cu(DO2A2M) which was found to be only slightly lower than that of Cu(DOTA) (log K = 19.85 vs. 21.98). This is likely reflects exclusive coordination of the negatively charged acetate donor atoms to the Cu ion forming an octahedral complex with the amides remaining uncoordinated. The only anomaly observed during the study was the rates of acid assisted dissociation of the Ln(III) complexes, which occur at a rate similar to those observed for the Ln(DOTA) complexes. These data indicate that even though the Ln(DO2A2M) complexes have lower thermodynamic stabilities, their kinetic inertness should be sufficient for in vivo use.
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http://dx.doi.org/10.1016/j.jinorgbio.2020.111042 | DOI Listing |
Environ Sci Technol
December 2024
Beijing Key Laboratory for Green Catalysis and Separation, Key Laboratory of Beijing on Regional Air Pollution Control, Key Laboratory of Advanced Functional Materials, Education Ministry of China, Laboratory of Catalysis Chemistry and Nanoscience, Department of Chemical Engineering and Technology, College of Materials Science and Engineering, Beijing University of Technology, Beijing 100124, China.
Chlorinated and oxygenated volatile organic compounds (CVOCs and OVOCs) pose a significant threat to human health. Catalytic oxidation effectively removes these pollutants, but catalyst deactivation is a challenge. Our study focused on the hydrolysis oxidation of chlorobenzene (CB) and ethyl acetate (EA) over Ru/MO/HZSM-5 (M = W, Mo).
View Article and Find Full Text PDFJ Med Internet Res
December 2024
College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Background: Wearable technologies have become increasingly prominent in health care. However, intricate machine learning and deep learning algorithms often lead to the development of "black box" models, which lack transparency and comprehensibility for medical professionals and end users. In this context, the integration of explainable artificial intelligence (XAI) has emerged as a crucial solution.
View Article and Find Full Text PDFChaos
December 2024
Department of Physics and Astronomy, Northwestern University, Evanston, Illinois 60208, USA.
In the classical control of network systems, the control actions on a node are determined as a function of the states of all nodes in the network. Motivated by applications where the global state cannot be reconstructed in real time due to limitations in the collection, communication, and processing of data, here we introduce a control approach in which the control actions can be computed as a function of the states of the nodes within a limited state information neighborhood. The trade-off between the control performance and the size of this neighborhood is primarily determined by the condition number of the controllability Gramian.
View Article and Find Full Text PDFChaos
December 2024
Potsdam Institute for Climate Impact Research, Telegrafenberg, Potsdam 14473, Germany.
Adaptive dynamical networks are ubiquitous in real-world systems. This paper aims to explore the synchronization dynamics in networks of adaptive oscillators based on a paradigmatic system of adaptively coupled phase oscillators. Our numerical observations reveal the emergence of synchronization cluster bursting, characterized by periodic transitions between cluster synchronization and global synchronization.
View Article and Find Full Text PDFChaos
December 2024
Department of Electrical and Computer Engineering, the Clarkson Center for Complex Systems Science, Clarkson University, Potsdam, New York 13699, USA.
Artificial Neural Networks (ANNs) have proven to be fantastic at a wide range of machine learning tasks, and they have certainly come into their own in all sorts of technologies that are widely consumed today in society as a whole. A basic task of machine learning that neural networks are well suited to is supervised learning, including when learning orbits from time samples of dynamical systems. The usual construct in ANN is to fully train all of the perhaps many millions of parameters that define the network architecture.
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