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https://www.selleckchem.com/products/ca3.html Water, the most important molecule on the Earth, possesses many essential and unique physical properties that are far from completely understood, partly due to serious difficulties in identifying the precise microscopic structures of water. Hence, identifying the structures of water nanoclusters is a fundamental and challenging issue for studies on the relationship between the macroscopic physical properties of water and its microscopic structures. For large-scale simulations (at the level of nm and ns) of water nanoclusters, a calculation method with simultaneous accuracy at the level of quantum chemistry and efficiency at the level of an empirical potential method is in great demand. Herein, a machine-learning (ML) water model was utilized to explore the microscopic structural features at different length scales for water nanoclusters with a size up to several nm. The ML water model can be employed to efficiently predict the structures of water nanoclusters with a similar accuracy to that of density functio sizes and processes with relatively long durations.Fingerprints form when fingers touch a solid surfaceand are considered the best way for individual identification. However, the current latent fingerprint (LFP) developing methods cannot meet the demand for high sensitivity and being convenient and healthy. Herein, bifunctional Fe3O4@SiO2-CsPbBr3 powders have been designed and fabricated and exhibit good magnetic and strong fluorescent properties. The magnetism of Fe3O4 can avoid dust flying, while the fluorescence of CsPbBr3 ensures the high definition of LFPs. Clear fingerprints have been detected on various solid substrates using the Fe3O4@SiO2-CsPbBr3 powders instead of eikonogen. Detailed characterization studies suggest that the ammonium cationic groups on the surface of nanoparticles (NPs) have strong adhesive interactions with the residues of fingerprints because of the electrostatic attraction between them.

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