Synthesis and characterization of Mono-disperse Carbon Quantum Dots from Fennel Seeds: Photoluminescence analysis using Machine Learning

We present the synthesis of mono-dispersed Carbon quantum dots via single-step thermal decomposition process using the fennel seeds

Akansha Dager; Takashi Uchida; Toru Maekawa; Masaru Tachibana

2019

Scholarcy highlights

  • We present the synthesis of mono-dispersed Carbon quantum dots via single-step thermal decomposition process using the fennel seeds
  • Fennel seeds have never been used for the synthesis of carbon quantum dots
  • The optical image of C-QDs dispersed in water under normal light, and UV light is shown in Fig. 2a,b, respectively
  • Our results show that given the advantage of priory and orthogonal constrained to ascertain the origin of the PL of C-QDs negative matrix factorization-auto relevance determination-soft orthogonal constraint analysis has merits over the MCR-ALS and principal component analysis
  • Machine learning techniques such as PCA, MCR-ALS, NMF-ARD-SO are useful in handling the large PL data-set and helped to ascertain the origin of the PL mechanism of as-synthesized C-QDs
  • We recommend the use of NMF-ARD-SO algorithm to analyze the PL mechanism because of its advantage over other Machine-learning techniques algorithms
  • Such Carbon quantum dots is unique and may find lots of applications in bio-sensing, cellular imaging, solar cell, and sensors

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