Micro/nano hierarchical substrates with different micropillar spacings were designed and prepared for capture of tumor cells. The cell capture efficiency of hierarchical substrates with low-density micropillar arrays was similar to that of nanostructured substrate. Increasing the density of micopillars could significantly improve the capture efficiency. The maximum capture efficiency was achieved on the hierarchical substrate with micropillar spacings of 15m, but further reducing the micropillar spacings did not increase the cell capture efficiency. It was also found that hierarchical substrates with appropriate spacing of micropillars appeared more favorable for cell attachment and spreading, and thus enhancing the cell-material interaction. These results suggested that optimizing the micropillar arrays, such as the spacing between adjacent micropillars, could give full play to the synergistic effect of hierarchical hybrid micro/nanostructures in the interaction with cells. This study may provide promising guidance to design and optimize micro/nano hierarchical structures of biointerfaces for biomedical application.
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http://dx.doi.org/10.1088/2057-1976/ac14a3 | DOI Listing |
Adv Sci (Weinh)
January 2025
Université Paris-Saclay, CNRS, Institut de Chimie Moléculaire et des Matériaux d'Orsay, Orsay, 91400, France.
To efficiently capture, activate, and transform small molecules, metalloenzymes have evolved to integrate a well-organized pocket around the active metal center. Within this cavity, second coordination sphere functionalities are precisely positioned to optimize the rate, selectivity, and energy cost of catalytic reactions. Inspired by this strategy, an artificial distal pocket defined by a preorganized 3D strap is introduced on an iron-porphyrin catalyst (sc-Fe) for the CO-to-CO electrocatalytic reduction.
View Article and Find Full Text PDFChem Rec
January 2025
Department of Chemical Engineering, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia.
In recent times, chemical looping offered a sustainable alternative for upgrading light hydrocarbons into olefins. Olefins are valuable platform chemicals that are utilized for diverse applications. To close the wide shortfall in their global supply, intensified efforts are ongoing to develop on-purpose production technologies.
View Article and Find Full Text PDFNPJ Comput Mater
January 2025
School of Mechanical, Aerospace, and Manufacturing Engineering, University of Connecticut, Storrs, CT USA.
Machine learning has advanced the rapid prediction of inorganic materials properties, yet data scarcity for specific properties and capturing thermodynamic stability remains challenging. We propose a framework utilizing a Graph Neural Network with composition-based and crystal structure-based architectures, combined with a transfer learning scheme. This approach accurately predicts energy-related properties (e.
View Article and Find Full Text PDFQuant Imaging Med Surg
January 2025
Department of Information Management, National Yunlin University of Science & Technology, Yunlin.
Background: Image segmentation is crucial in medical diagnosis, helping to identify diseased areas in images for more accurate diagnoses. The U-Net model, a convolutional neural network (CNN) widely used for medical image segmentation, has limitations in extracting global features and handling multi-scale pathological information. This study aims to address these challenges by proposing a novel model that enhances segmentation performance while reducing computational demands.
View Article and Find Full Text PDFPhys Chem Chem Phys
January 2025
School of Chemistry & Chemical Engineering, South China University of Technology, Guangzhou 510640, P. R. China.
The hydrogenation of bicarbonate, a byproduct of CO captured in alkaline solutions, into formic acid (FA) using glycerol (GLY) as a hydrogen source offers a promising carbon-negative strategy for reducing CO emissions. While Pd-based catalysts are effective in this reaction, they often require high temperatures, leading to low FA yield due to strong hydrogen adsorption on Pd surfaces. In this work, metal-organic framework derived N-doped carbon encapsulated CoNi alloy nanoparticles (CoNi@NC) were prepared, acid-leached, and employed as a support to modulate the electronic structure of Pd-based catalysts.
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