Valorization of food industrial waste: Green synthesis of carbon quantum dots and novel applications

Thakur, Sweezee; Bains, Aarti; Sridhar, Kandi; Kaushik, Ravinder; Chawla, Prince; Sharma, Minaxi

doi:10.1016/j.chemosphere.2023.140656

Cited by 8 publications

(1 citation statement)

References 78 publications

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“…Carbon quantum dots (CQDs) are a new class of fluorescent nanoparticles (NPs) that have attracted the interest of many researchers worldwide. These carbon-based nanomaterials display biocompatibility, chemical stability, green synthesis, as well as surface modifications with different functional groups [7][8][9]. Furthermore, it is possible to adjust their structural composition to improve spectral properties [10].…”

Section: Introductionmentioning

confidence: 99%

Multichannel Sensor for Detection of Molybdenum Ions Based on Nitrogen-Doped Carbon Quantum Dot Ensembles

Cruz,

Souza,

de Souza

et al. 2024

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Trace elements such as cobalt (Co), molybdenum (Mo), and zinc (Zn) play necessary roles in different biological functions. Co is a microelement that influences the vascular system. Mo works as an enzymatic cofactor of three enzymes (aldehyde oxidase, sulfite oxidase, and xanthine oxidase dehydrogenase). However, these elements are difficult to detect, since the analytical methods developed have a high cost, which restrict their applicability. In this sense, fluorescent sensors are an alternative for detecting trace elements, such as Mo4+ ions. Herein, a new multichannel trace elements sensor has been proposed to detect Mo entities. In this sense, two different N-CQDs were synthesized and fully characterized. The N-CQDs presented quantum yield values of 25.93% and 6.02% and excellent solubility in water. Also, a mixture of these two carbon-based nanoparticles was used to identify and to quantify Mo in water between seven different trace elements. The method was found to reach 1.28 and 3.88 ppm for limit of detection (LOD) and quantification (LOQ), respectively. To further verify the potential of the detection platform, the multichannel sensor was applied to identify the different concentrations of metal ions (Fe2+, Co2+, Mn2+, Cu2+, Zn2+, Mg2+, and Mo4+) in water. The data matrix was treated using different algorithms, such as K-Means and Discriminant Analysis (DA). The detection strategy has successfully identified the molybdenum ions at 5 ppm. This result shows the potential application of a multichannel sensor toward the detection of Mo entities, since it is comparable with the molybdenum test already available on the market.

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