The exploration of specific heavy doping of silver atoms into icosahedral Au clusters and their electronic structures and properties has been somewhat limited. Herein, we report two heavily Ag doped nanoclusters, [AuAg(CHNOS)(Dppf)Cl] and [AuAg(CHNOS)(Dppf)Cl](SbF) (AuAg-0 and AuAg-1, respectively) [CHNOSH = 2-mercaptobenzoxazole, and Dppf = 1,1'-bis(diphenylphosphino)ferrocene]. The electronic structures and superatomic orbitals of nanoclusters were determined by density functional theory (DFT) calculations, and the energy degeneracy of the superatomic orbitals of AuAg-1 is higher than that of AuAg-0. Transient absorption spectroscopy was performed, revealing that AuAg-0 significantly extends the excited-state lifetime. Both nanoclusters were supported on activated carbon for the oxygen reduction reaction. DFT calculations confirm that the catalytic activities mainly stem from the carbon atom of ferrocene rather than the iron atom. This study not only sheds light on the preparation of icosahedral alloy clusters but also provides insights into the regulation of icosahedral superatomic structure and electrocatalytic properties.
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http://dx.doi.org/10.1021/acs.jpclett.3c02884 | DOI Listing |
Med Phys
January 2025
Deparment of Radiation Oncology, Duke University, Durham, North Carolina, USA.
Background: Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post-SRS management. Differentiating radionecrosis from tumor recurrence non-invasively remains a major clinical challenge, as conventional imaging techniques often necessitate surgical biopsy for accurate diagnosis. Machine learning and deep learning models have shown potential in distinguishing radionecrosis from tumor recurrence.
View Article and Find Full Text PDFThe detection of lead ions (Pb) is crucial due to its harmful effects on health and the environment. In this article, what we believe to be a novel dielectric-metal hybrid structure localized surface plasmon resonance (LSPR) sensor for ultra-trace detection of Pb is proposed, featuring a zinc sulfide layer, silver nanodisks (Ag-disks), and graphene oxide (GO) covering the Ag-disks. The sensor works by detecting the variation of gold nanoparticles (AuNPs) on its surface when Pb cleaves a substrate strand linked to a DNAzyme, causing the AuNPs modified on the substrate strand to disperse.
View Article and Find Full Text PDFNat Commun
January 2025
Key Laboratory of Bioactive Materials for the Ministry of Education, College of Life Sciences, State Key Laboratory of Medicinal Chemical Biology, and Frontier of Science Center for Cell Response, Nankai University, Tianjin, 300071, China.
Nanozymes play a pivotal role in mitigating excessive oxidative stress, however, determining their specific enzyme-mimicking activities for intracellular free radical scavenging is challenging due to endo-lysosomal entrapment. In this study, we employ a genetic engineering strategy to generate ionizable ferritin nanocages (iFTn), enabling their escape from endo-lysosomes and entry into the cytoplasm. Specifically, ionizable repeated Histidine-Histidine-Glutamic acid (9HE) sequences are genetically incorporated into the outer surface of human heavy chain FTn, followed by the assembly of various chain-like nanostructures via a two-armed polyethylene glycol (PEG).
View Article and Find Full Text PDFBackground And Objective: Serum protein electrophoresis (SPEP) plays a critical role in diagnosing diseases associated with M-proteins. However, its clinical application is limited by a heavy reliance on experienced experts.
Methods: A dataset comprising 85,026 SPEP outcomes was utilized to develop artificial intelligence diagnostic models for the classification and localization of M-proteins.
J Hazard Mater
January 2025
University of Belgrade, Faculty of Technology and Metallurgy, Karnegijeva 4, Belgrade 11120, Serbia. Electronic address:
Effective protection of groundwater requires an accurate health risk assessment of contaminants; however, the diversity of pollution sources, variability, and uncertainties in exposure parameters present significant challenges in this assessment. In this study, groundwater risk estimates associated with NO, and F, along with fourteen heavy metal(loid)s (V, Cr, Mn, Fe, Ni, Cu, As, Co, Cd, Se, Pb, Hg, Zn, and Al) in an agricultural area were optimized by implementing positive matrix factorization (PMF), multilinear regression, and two-dimensional Monte Carlo simulations to characterize source-specific health risks. Groundwater pollution was analyzed considering regional variations, including differences in elevation, land use and land cover, and soil types.
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