Urea-based carbon quantum dots for recognition of Fe3+ by fluorescence quenching and Pb2+ by fluorescence enhancement.

The present study focuses on synthesis of Urea-based carbon quantum dots (UCQDs) using a non-toxic, low-cost hydrothermal method without consumption of any harmful chemical. The synthesized UCQDs were characterized via UV-Vis absorbance, Photoluminescence (PL), and FTIR spectroscopy. Distinct UV-Vis absorption peaks due to π-π* and n-π* transitions and existence of excitation-independent PL emission peaks at ~450 nm and ~525 nm, validated the presence of carbogenic core and nitrogen-enriched surface states.

State-of-the-art methods in healthcare text classification system: AI paradigm.

Machine learning has shown its importance in delivering healthcare solutions and revolutionizing the future of filtering huge amountd of textual content. The machine intelligence can adapt semantic relations among text to infer finer contextual information and language processing system can use this information for better decision support and quality of life care. Further, a learnt model can efficiently utilize written healthcare information in knowledgeable patterns.