Chiral transfer amidst one-dimensional linear polymers and twodimensional network covalent organic frameworks:Striking a fine balance between helicity and crystallinity

The current landscape of chiral covalent organic frameworks(COFs)predominantly centered on constructing asymmetric molecular-scale chirality,often introducing an inherent contradiction to the COF symmetry and limiting diversity.Herein,we overcome these challenges by achieving chiral transfer between one-dimensional(1D)imine linear polymers(LPs)and two-dimensional(2D)networkβ-ketoenamine COFs composed of achiral monomers.We successfully synthesize several 1D imine LPs with mesoscopic helical chirality,comprising achiral C2-symmetric terephthalaldehyde and diamine linkers in a chiral supramolecular transcription system.Leveraging the irreversible tautomerism mechanism within the linker replacement approach,terephthalaldehyde(TPA)units in these helical 1D LPs are substituted with C3-symmetric 1,3,5-triformylphloroglucinol(TP),yielding the corresponding 2D networkβ-ketoenamine COFs.Crystallinity and helicity of the resultantβ-ketoenamine COFs intimately hinge on reaction conditions,including the aldehyde stoichiometry of Tp and TPA,as well as the quantity and concentration of the catalyst employed.Under optimized conditions,the nucleation and growth were precisely governed,achieving a harmonious equilibrium of crystallinity and helicity within the generated 2D networkβ-ketoenamine COFs,even with covalent bond rupture,recombination,and topological transition(from[C2+C2]to[C3+C2]).Impressively,the ground state chirality inherent to helical 1D LPs seamlessly transfers to helical 2D networkβ-ketoenamine COFs.This study not only offers new perspectives on the development of chiral functional COFs,but also provides fresh insights into the precise control of COFs'microscopic morphology.

STGI:a spatio-temporal grid index model for marine big data

Marine big data are characterized by a large amount and complex structures,which bring great challenges to data management and retrieval.Based on the GeoSOT Grid Code and the composite index structure of the MongoDB database,this paper proposes a spatio-temporal grid index model(STGI)for efficient optimized query of marine big data.A spatio-temporal secondary index is created on the spatial code and time code columns to build a composite index in the MongoDB database used for the storage of massive marine data.Multiple comparative experiments demonstrate that the retrieval efficiency adopting the STGI approach is increased by more than two to three times compared with other index models.Through theoretical analysis and experimental verification,the conclusion could be achieved that the STGI model is quite suitable for retrieving large-scale spatial data with low time frequency,such as marine big data.