Spectral Detection Technology And Its Application Based On Biomimetic Nanofibers

Molecular imprinting technology（MIT）is an effective method for preparing functional materials with molecular recognition capabilities based on the principle of biomimetic recognition.Electrospinning molecularly imprinted nanofibers（MINF）not only has the specific adsorption function of molecularly imprinted polymers（MIPs）,but also has a comparative advantage:large specific surface area to adsorb more target material.Surface enhanced Raman spectroscopy（SERS）technology has attracted wide attention due to its simple and rapid operation and effective detection of low-concentration materials.In this paper,based on the specific recognition and enrichment of molecularly imprinted nanofibers,combined the molecular imprinting technique,nanomaterials,and SERS detection to prepare a gold-molecularly imprinted nanofiber-reinforced basement membrane for SERS detection to achieve specific recognition.Develop detection technologies based on new composite nanomaterials to increase the selectivity and sensitivity of traditional spectroscopic detection technologies.Phthalic acid esters（plasticizer）PAEs is a kind of endocrine disrupting Chemicals（EDCs）used in plastic products widely.Plasticizers and plastics are only linked by molecular forces thus a small amount of PAEs easily migrate out to cause negative impact to reproductive biology,immune,endocrine system,etc.Therefore,it is of great significance to establish a quick,simple and reliable method for detecting trace PAEs.In this dissertation,the most used PAEs,dimethyl phthalate（DMP）,were selected as template molecules to achieve the specific recognition of DMP molecules and a new detection method was established.Polyether sulfone（PES）was used as functional monomer and DMP as template molecule.Molecularly imprinted nanofiber membrane（MINF）with selective adsorption was prepared by electrospinning.The binding properties and specific adsorption performance of the polymer were studied by static equilibrium binding method,experimental results showed that the maximum adsorption capacity of the fiber is 45.12μmol/g;MINF adsorption of template molecules is much higher than other structural analogs.The nano-gold particles were prepared by HEPES reduction method.The size of nano-gold particles was prepared by changing the reducing agent,reaction time and the amount of surfactant.The prepared 30-70 nm gold nanoparticles were used as a heavy metal material for Raman signal enhancement and were doped into a fibril membrane spinning solution to prepare a gold-molecularly imprinted nanofiber reinforced basement membrane（Au-MINF）.The SERS activity of Au-MINF was found stronger.Single factor experiments were used to select the main influencing factors.Three-factor three-level experiments were designed and the results were analyzed by response surface analysis（RSA）.The results showed that the influence of various factors on the Raman signal strength was Nanogold mass>Nanogold size>Mass ratio of DMP to PES,optimal basement membrane composition as follows:nanogold diameter 52.543163 nm,nanogold mass51.441913 mg,mass ratio of DMP to PES 0.102034,predicted Raman signal enhancement theoretical value is 4.899706 to standardized intensity.Comparing the actual performance of the enhanced basement membrane（4.82 standard intensity）with the results of the simulation calculation,the two are quite consistent,indicating that the simulation calculation can be an effective method for designing a complex molecularly imprinted SERS enhancement membrane.Raman detection of DMP solutions with different concentrations was performed using the SERS base film prepared above,the minimum detection concentration was 10^-8 mol/L.Three characteristic peaks in the original data were selected for partial least-squares regression（PLSR）to establish the concentration model.After confirming the reliability of the model,the bottled water and the lake water were tested.The results showed that the linear regression equation of the calculated concentration and actual concentration of the bottled water DMP concentration model was y=1.06826x+0.04982,and the determinatin coefficient R² was 0.9990.The standard deviation was 0.0682,while the lake water sample was y=1.00135x+0.07575,the determinatin coefficient R² was 0.97439,the standard deviation was 0.15818.The results were good,indicating that the model can be used to detect trace DMP concentration.