|
|
|
|
|
|
Synthesis of Carbon Quantum Dots Based on Gelatin and Study on It’s Optical Property |
WANG Xue-chuan1,2, BAI Peng-xia2, LUO Xiao-min1, LI Ji1 |
1. College of Bioresources Chemical and Materials Engineering, National Demonstration Center for Experimental Light Chemistry Engineering Education, Shaanxi University of Science & Technology, Xi’an 710021, China
2. College of Chemistry and Chemical Engineering, Shaanxi University of Science & Technology, Xi’an 710021, China |
|
|
Abstract In this article, the blue luminescent carbon quantum dots (CQDs) were prepared by hydrothermal method using pyrolysis of gelatin. The temperature, time of the prepared CQDs were optimized via single-factor experiments to select the optimal conditions for the preparation of CQDs. The results showed that fluorescence of carbon quantum dots was the strongest when the carbonization temperature was 200 ℃, and time was 6 h. At the same time, the obtained carbon quantum dots under the optimal conditions was characterized by transmission electron microscope (TEM), UV-visible spectroscopy, photoluminescence spectroscopy (PL), fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD), the result indicating that the quantum yield of carbon quantum dots prepared by this method is 39.4%, and the quantum yield is relatively higher than that of carbon quantum dots that do not doped, which may be due to the presence of N elements that increase the quantum yield, and the prepared carbon quantum dots not only have rich oxygen-containing functional groups but also have good photobleaching performance, and the morphology of the carbon quantum dots is mainly spherical with uniform dispersion and no obvious lattice fringes, which is consistent with the morphology of the carbon quantum dots reported in related literatures; And the carbon quantum dots had weak absorption at 250~300 nm, but there was no obvious characteristic absorption peak, which may be due to the n—π* transition of the C=O group; In addition, the pH, the Xenon lamp irradiation time, the concentration of carbon quantum dots, the type of solvent, andionic strengthon the fluorescence properties of carbon quantum dots were discussed. The results showed that the irradiation time of the xenon lamp and ionic strength had little effect on the fluorescence performance of the carbon quantum dots. The fluorescence intensity is relatively weak under peracid or overbase conditions, which may be due to protonated or non-protonated effects resulting in decreased fluorescence intensity under peracid or overbase conditions. And as the concentration of the carbon quantum dots increased, the fluorescence intensity increased first and then decreased. For the solvent type, the fluorescence intensity in the polar solvent was greater than the fluorescence intensity in the non-polar solvent which indicated that the carbon quantum dots had good water solubility by this method.
|
Received: 2018-04-02
Accepted: 2018-08-28
|
|
|
[1] Li Y, Huang H, Ma Y, et al. Sensors and Actuators B: Chemical,2014, 205(15): 227.
[2] Li Z, Wang Y, Ni Y, et al. Sensors and Actuators B: Chemical,2015, 207(8): 490.
[3] Chen L, Wu C, Du P, et al. Talanta,2017, 164(1): 100.
[4] Abdullah A N, Jung E L, Insik I, et al. Molecular Pharmaceutics,2013, 10(10): 3736.
[5] Shen J, Zhu Y, Chen C, et al. Chem. Commun.,2011, 47(9): 2580.
[6] Liu R, Wu D, Feng X, et al. J. Am. Chem. Soc., 2011, 133(39): 15221.
[7] Tang L, Ji R, Cao X, et al. ACS Nano,2012, 6(6): 5102.
[8] Dong Y, Shao J, Chen C, et al. Carbon,2012, 50(12): 4738.
[9] Qu D, Zheng M, Zhang L, et al. Scientific Reports,2014, 4: 5294.
[10] Schneider J, Reckmeier C J, Xiong Y, et al. Journal of Physical Chemistry,2017, 121(3): 2014.
[11] Han B, Wang W, Wu H, et al. Colloids and Surfaces B: Biointerfaces,2012, 100: 209.
[12] Mojtaba J, Biuck H. Biosensors and Bioelectronics,2016, 81(15): 143.
[13] Kirman C R, Aylward L L, Suh M, et al. Chem. -Biol. Interact.,2013, 204(1): 13.
[14] Zhang Ruizhong, Chen Wei. Biosensors and Bioelectronics,2014, 55(15): 83.
[15] Hu Y, Yang J, Ren J, et al. Carbon,2015, 93:999.
[16] Liu S, Tian J, Wang L, et al. Adv Mater.,2012, 24(15): 2037.
[17] Liang Qinghua, Ma Wangjing, Shi Yao, et al. Carbon,2013, 60(13): 421.
[18] MA Qing-yun, ZHANG Ji-mei, ZHANG Kun, et al(马庆运,张纪梅,张 坤,等). New Chemical Materials(化工新型材料),2017, 45(2): 70.
[19] Tang L, Ji R, Li X, et al. Particle & Particle Systems Characterization,2013, 30(6): 523.
[20] Gao X, Du C, Zhuang Z, et al. Journal of Materials Chemistry C,2016, 4(29): 6927.
[21] Song J P, Li J, Guo Z Y, et al. RSC Adv.,2017, 7(21): 12827.
[22] Wu Z L, Zhang P, Gao M, et al. Journal of Materials Chemistry B,2013, 1(22): 2868.
[23] De B K, Karak N. RSC Adv.,2013, 3: 8286. |
[1] |
LEI Hong-jun1, YANG Guang1, PAN Hong-wei1*, WANG Yi-fei1, YI Jun2, WANG Ke-ke2, WANG Guo-hao2, TONG Wen-bin1, SHI Li-li1. Influence of Hydrochemical Ions on Three-Dimensional Fluorescence
Spectrum of Dissolved Organic Matter in the Water Environment
and the Proposed Classification Pretreatment Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 134-140. |
[2] |
GU Yi-lu1, 2,PEI Jing-cheng1, 2*,ZHANG Yu-hui1, 2,YIN Xi-yan1, 2,YU Min-da1, 2, LAI Xiao-jing1, 2. Gemological and Spectral Characterization of Yellowish Green Apatite From Mexico[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 181-187. |
[3] |
HAN Xue1, 2, LIU Hai1, 2, LIU Jia-wei3, WU Ming-kai1, 2*. Rapid Identification of Inorganic Elements in Understory Soils in
Different Regions of Guizhou Province by X-Ray
Fluorescence Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 225-229. |
[4] |
WANG Hong-jian1, YU Hai-ye1, GAO Shan-yun1, LI Jin-quan1, LIU Guo-hong1, YU Yue1, LI Xiao-kai1, ZHANG Lei1, ZHANG Xin1, LU Ri-feng2, SUI Yuan-yuan1*. A Model for Predicting Early Spot Disease of Maize Based on Fluorescence Spectral Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3710-3718. |
[5] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
[6] |
SONG Yi-ming1, 2, SHEN Jian1, 2, LIU Chuan-yang1, 2, XIONG Qiu-ran1, 2, CHENG Cheng1, 2, CHAI Yi-di2, WANG Shi-feng2,WU Jing1, 2*. Fluorescence Quantum Yield and Fluorescence Lifetime of Indole, 3-Methylindole and L-Tryptophan[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3758-3762. |
[7] |
YI Min-na1, 2, 3, CAO Hui-min1, 2, 3*, LI Shuang-na-si1, 2, 3, ZHANG Zhu-shan-ying1, 2, 3, ZHU Chun-nan1, 2, 3. A Novel Dual Emission Carbon Point Ratio Fluorescent Probe for Rapid Detection of Lead Ions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3788-3793. |
[8] |
YANG Ke-li1, 2, PENG Jiao-yu1, 2, DONG Ya-ping1, 2*, LIU Xin1, 2, LI Wu1, 3, LIU Hai-ning1, 3. Spectroscopic Characterization of Dissolved Organic Matter Isolated From Solar Pond[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3775-3780. |
[9] |
LI Xiao-li1, WANG Yi-min2*, DENG Sai-wen2, WANG Yi-ya2, LI Song2, BAI Jin-feng1. Application of X-Ray Fluorescence Spectrometry in Geological and
Mineral Analysis for 60 Years[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 2989-2998. |
[10] |
XUE Fang-jia, YU Jie*, YIN Hang, XIA Qi-yu, SHI Jie-gen, HOU Di-bo, HUANG Ping-jie, ZHANG Guang-xin. A Time Series Double Threshold Method for Pollution Events Detection in Drinking Water Using Three-Dimensional Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3081-3088. |
[11] |
MA Qian1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, CHENG Hui-zhu1, 2, ZHAO Yan-chun1, 2. Research on Classification of Heavy Metal Pb in Honeysuckle Based on XRF and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2729-2733. |
[12] |
JIA Yu-ge1, YANG Ming-xing1, 2*, YOU Bo-ya1, YU Ke-ye1. Gemological and Spectroscopic Identification Characteristics of Frozen Jelly-Filled Turquoise and Its Raw Material[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2974-2982. |
[13] |
YANG Xin1, 2, XIA Min1, 2, YE Yin1, 2*, WANG Jing1, 2. Spatiotemporal Distribution Characteristics of Dissolved Organic Matter Spectrum in the Agricultural Watershed of Dianbu River[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2983-2988. |
[14] |
CHEN Wen-jing, XU Nuo, JIAO Zhao-hang, YOU Jia-hua, WANG He, QI Dong-li, FENG Yu*. Study on the Diagnosis of Breast Cancer by Fluorescence Spectrometry Based on Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2407-2412. |
[15] |
ZHU Yan-ping1, CUI Chuan-jin1*, CHENG Peng-fei1, 2, PAN Jin-yan1, SU Hao1, 2, ZHANG Yi1. Measurement of Oil Pollutants by Three-Dimensional Fluorescence
Spectroscopy Combined With BP Neural Network and SWATLD[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2467-2475. |
|
|
|
|