ArticleRSC advances2025
Experimental and artificial intelligence molecular models to predict quenching behavior of carbon materials from petroleum waste for sustainable corrosion monitoring.
Article in RSC advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
2 authors.
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Abstract
Quantum dots have attracted a lot of interest because of their special optical characteristics and potential for a variety of uses, such as in inks and sensors. Additionally, petroleum coke recycling is crucial for resource efficiency, economic growth, and environmental sustainability. It solves problems linked to carbon emissions and landfill waste. Industries can reduce their environmental impact while generating new opportunities for innovation and expansion. Petroleum coke quantum dots (PCQDs) were synthesized using the reflux method at 120 °C for 12 h, then characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and electron pair resonance spectroscopy (EPR), and were utilized to differentiate Fe
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