Synthesis and characterization of Mono-disperse Carbon Quantum Dots from Fennel Seeds: Photoluminescence analysis using Machine Learning
Synthesis and characterization of Mono-disperse Carbon Quantum Dots from Fennel Seeds: Photoluminescence analysis using Machine Learning
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DOI：
10.1038/s41598-019-50397-5
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发表时间：
2019-09-30
影响因子：
4.6
通讯作者：
Tachibana, Masaru
中科院分区：
文献类型：
--
作者：
Dager, Akansha;Uchida, Takashi;Tachibana, Masaru
本文以茴香种子（Foeniculum vulgare）为原料，采用一步热分解法合成了单分散的C-QDs。由于合成的C-QDs具有优异的胶体稳定性、光稳定性和环境稳定性（pH），并且不需要任何额外的表面钝化步骤来提高荧光。C-QDs表现出优异的PL活性和激发无关发射。据我们所知，利用天然碳源通过热解过程合成不依赖于激发的C-QDs是前所未有的。反应时间和温度对热解的影响为C-QDs的合成提供了新的思路。我们使用机器学习技术（ML），如PCA、MCR-ALS和NMF-ARD-SO，以便为合成C-QDs的PL机制的起源提供一个合理的解释。ML技术能够处理和分析大型PL数据集，并制度性地推荐用于PL分析的最佳激发波长。单分散c -量子点是非常理想的，在生物传感、细胞成像、LED、太阳能电池、超级电容器、印刷和传感器等领域具有广泛的潜在应用。
Herein, we present the synthesis of mono-dispersed C-QDs via single-step thermal decomposition process using the fennel seeds (Foeniculum vulgare). As synthesized C-QDs have excellent colloidal, photo-stability, environmental stability (pH) and do not require any additional surface passivation step to improve the fluorescence. The C-QDs show excellent PL activity and excitation-independent emission. Synthesis of excitation-independent C-QDs, to the best of our knowledge, using natural carbon source via pyrolysis process has never been achieved before. The effect of reaction time and temperature on pyrolysis provides insight into the synthesis of C-QDs. We used Machine-learning techniques (ML) such as PCA, MCR-ALS, and NMF-ARD-SO in order to provide a plausible explanation for the origin of the PL mechanism of as-synthesized C-QDs. ML techniques are capable of handling and analyzing the large PL data-set, and institutively recommend the best excitation wavelength for PL analysis. Mono-disperse C-QDs are highly desirable and have a range of potential applications in bio-sensing, cellular imaging, LED, solar cell, supercapacitor, printing, and sensors.