分析化学2025，Vol.53Issue(1)：94-103,10.DOI:10.19756/j.issn.0253-3820.241049
内源性衍生物对碳点化学传感性能的影响
Study on Influence of Endogenous Derivatives on Chemical Sensing Performance of Carbon Dots
摘要
Abstract
The blue fluorescent carbon dots(TMCDs)and cyan fluorescent carbon dots(TMCDs-H2O)were synthesized fromm-phenylenediamine and tricarballylic acid through air-assisted melting polymerization and one-step hydrothermal method,respectively.Air purging could effectively inhibit the side reactions and reduce the derivative structures in the carbon dots product.The structure and morphology of these two materials were systematically characterized using liquid nuclear magnetic resonance spectroscopy(NMR),mass spectrometry(MS),and transmission electron microscopy.Compared to TMCDs-H2O((3.12±0.63)nm),TMCDs showed a smaller average particle size(approximately(1.85±0.02)nm).The NMR and MS analysis revealed that although the main structure of both types of carbon dots was similar,TMCDs exhibited a simpler structure with higher degree of polymerization.These results suggested that supramolecular interactions might introduce numerous small molecule derivatives into TMCDs-H2O particles,resulting in lower polymerization degree,multiple substructures,and larger particle size characteristics for this material.When employed as chemical sensors for metal ion detection,in the linear range of 1×10-5-5×10-4 mol/L,the detection limits of Fe3+by TMCDs and TMCDs-H2O were 3.3×10-6 mol/L and 3.8×10-6 mol/L,respectively.The experimental results demonstrated that the recoveries of CDs and inductively coupled plasma optical emission spectrometer(ICP-OES)were similarity,whereas TMCDs displayed a considerable relative standard deviation.This study demonstrated that endogenously derived structures in CDs could enhance the performance of metal ion sensing.关键词
内源性衍生物/碳点/化学传感/金属离子检测/Fe3+传感机理Key words
Endogenous derivatives/Carbon dots/Chemical sensing/Detection of metal ion/Fe3+sensing mechanism引用本文复制引用
覃迎喜,王昱,杨莉花,刘紫薇,覃爱苗,冯亮..内源性衍生物对碳点化学传感性能的影响[J].分析化学,2025,53(1):94-103,10.基金项目
中国科学院青年创新促进会项目(No.2020185)、中国科学院大连清洁能源国家实验室榆林分院人工智能科技计划(No.DNL-YL A202203)、国家自然科学基金项目(No.22122307)和广西自然科学基金项目(No.2018JJA160029)资助. Supported by the Youth Innovation Promotion Association of Chinese Academy of Sciences(No.2020185),the AI S&T Program of Yulin Branch,Dalian National Laboratory for Clean Energy,CAS(No.DNL-YL A202203),the National Natural Science Foundation of China(No.22122307)and the Natural Science Foundation of Guangxi(No.2018JJA160029). （No.2020185）
