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The differentiation between missing linker defects and missing cluster defects in MOFs is difficult, thereby limiting the ability to correlate materials properties to a specific type of defects. Herein, we present a novel and easy synthesis strategy for the creation of solely "missing cluster defects" by preparing mixed-metal (Zn, Zr)-UiO-66 followed by a gentle acid wash to remove the Zn nodes. The resulting material has the UiO-66 structure, typical for well-defined missing cluster defects. The missing clusters are thoroughly characterized, including low-pressure Ar-sorption, iDPC-STEM at a low dose (1.5 pA), and XANES/EXAFS analysis. We show that the missing cluster UiO-66 has a negligible number of missing linkers. We show the performance of the missing cluster UiO-66 in CO sorption and heterogeneous catalysis.
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http://dx.doi.org/10.1021/jacs.1c05357 | DOI Listing |
Lancet Reg Health West Pac
September 2025
Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.
Background: There is ongoing controversy as to whether surgical intervention to haematoma evacuation benefits patients with acute intracerebral haemorrhage (ICH). This study aimed to evaluate the association of surgical intervention to evacuate the haematoma and 6-month functional outcome in participants of the third Intensive Care Bundle with Blood Pressure Reduction in Acute Cerebral Haemorrhage Trial (INTERACT3).
Methods: This was a secondary analysis of INTERACT3, which enrolled adults (age ≥18 years) spontaneous ICH patients within 6 h after onset.
Brief Bioinform
August 2025
School of Computer Science, Xi'an Polytechnic University, 710048, Xi'an, China.
Cancer, with its inherent heterogeneity, is commonly categorized into distinct subtypes based on unique traits, cellular origins, and molecular markers specific to each type. However, current studies primarily rely on complete multi-omics datasets for predicting cancer subtypes, often overlooking predictive performance in cases where some omics data may be missing and neglecting implicit relationships across multiple layers of omics data integration. This paper introduces Multi-Layer Matrix Factorization (MLMF), a novel approach for cancer subtyping that employs multi-omics data clustering.
View Article and Find Full Text PDFJ Chromatogr A
September 2025
Luoyang R&D Center of Technology, SINOPEC Engineering (Group) Co., Ltd, Luoyang 471003, China. Electronic address:
Conventional one-dimensional gas chromatography methods for gasoline quality monitoring require separate analyses for different component classes, limiting analytical efficiency and unconventional additive detection. This study presents a comprehensive two-dimensional gas chromatography with flame ionization detection (GC × GC-FID) platform enabling simultaneous quantification of regulated components and rapid screening of unconventional additives in a single analytical run. The method achieved excellent agreement with ASTM standards and high repeatability for BTEX (benzene, toluene, ethylbenzene, and xylenes) and oxygenates in gasoline.
View Article and Find Full Text PDFPLoS One
September 2025
School of Civil Engineering, Shandong Jianzhu University, Jinan, China.
In engineering structure performance monitoring, capturing real-time on-site data and conducting precise analysis are critical for assessing structural condition and safety. However, equipment instability and complex on-site environments often lead to data anomalies and gaps, hindering accurate performance evaluation. This study, conducted within a wind farm reinforcement project in Shandong Province, addresses these challenges by focusing on anomaly detection and data imputation for weld nail strain, anchor cable axial force, and concrete strain.
View Article and Find Full Text PDFbioRxiv
September 2025
Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH, USA.
High-throughput spatial transcriptomics (ST) now profiles hundreds of thousands of cells or locations per section, creating computational bottlenecks for routine analysis. Sketching, or intelligent sub-sampling, addresses scale by selecting small, representative subsets. While effective for scRNA-seq data, existing sketching methods, which optimize coverage in expression space but ignore physical location, can introduce spatial bias when applied to ST data.
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