A sustainable alternative to synthesis optical sensing receptor for the detection of metal ions
Source abstract
Abstract
Abstract The development of new optical sensing receptors not only need to focus on its sensitivity and selectivity aspects, but should also emphasis on the sustainability of the development as a whole. This report proposed a green synthesis method to produce fluorescent carbon nanoparticles via thermal carbonisation of orange peels. This is a sustainable option since used agricultural waste as starting precursor, involved no excess or toxic chemical reagents, produced low or no waste side product, and employed simple synthesis steps. Parameters governing the precursor conversion into carbon rich residues were investigated and found that the optimum carbonisation occurs at the temperature range of 300–350 °C for an exposure time of 2 h. The carbon residue was then grinded into fine nanoparticles, dispersed in water and followed by sonication to promote better dispersion in water. Colloidal suspension remaining in the aliquot after centrifugation for 15 min at 13,400 rpm was collected and found to show strong fluorescence emission at 435 nm, when excited at the optimum wavelength of 325 nm. This unique optical property has been utilised for sensing application since the fluorescence intensity was significantly quenched in the presence of heavy metal ions. Analytical characteristic was evaluated using standard Stern–Volmer equation and the limit of detection was evaluated to be significantly low that enable the practical utilisation for quantification applications. It can serve as a less toxic fluorophore candidate to replace some of those existing quantum dots or dyes that are less sustainable in nature or in terms of its development approach.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Carbon and Quantum Dots Applications · Electrochemical sensors and biosensors · Luminescence and Fluorescent Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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