Synthesis,properties And Application Of Multi-stimulus Response Materials Based On ESIPT Mechanism

Multi-stimuli responsive chromism(MRC)and multi-stimuli luminescence chromism(MRLC)materials show apparent change of color and luminescence under external stimuli,such as light,temperature,mechanical force,p H,electromagnetic environment.These materials have been widely used in opto-electronics,data storage,anti-counterfeiting materials,fluorescent sensors,and biological imagin.Several ESIPT(Excited-state Intramolecular Proton Transfer,ESIPT)fluorescent materials exhibit excellent properties such as large Stokes shift,dual luminescence,and environment-sensitive photophysics,enabling promising possibilities for multi-stimuli responsive material.In this thesis,we designed and synthesized two ovel ESIPT compounds and characterized their MRC/MRLC properties,and we applied one of them to the physical unclonable function(PUF)security label.The specific research work is as follows:(1)Novel MRC/MRLC materials based ESIPT——N,N'-bis(4-hydroxyindolyl)-p-phenylenediamine(p-BHIP),N,N'-bis(4-hydroxycarbazolyl)-p-phenylenediamine(p-BHCP)have been synthesized and characterized.By grinding,p-BHIP show a color variation from yellow-green to orange.When processing with acid,it turns brown.p-BHCP exhibits impressive chromism and luminescence chromism for eight external stimuli.Several spectroscopic experiments and single-crystal analyses,as well as coupled-cluster(CC)calculations,have demonstrated that the molecular tautomerization is responsible for the chromism behaviors.The advantages that real-time and reversible responses,high-contrast colors allow the material to be used in multi-dimensional anti-counterfeiting.(2)Because of the noticeable luminescence contrast between the dispersed state and the aggregated state,p-BHCP has been used to prepare the PUF safety label.Shapes and location p-BHCP particles are random,so it is impossible to be cloned or counterfeited accurately.PUF security label can be accurately verified in a few seconds through a deep learning algorithm ignoring the impact of the shooting equipment and shooting angle.