A gram-scale, low-cost and sustainable synthesis of single-crystalline graphene quantum dots derived from biomass waste.

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The high value-added conversion of biomass lignin has been the paramount in the field of lignin utilization especially for high performance energy conversion and storage devices. The majority of lignin-based...

To overcome the numerous disadvantages of existing testing technology, a novel, fast, nondestructive, and quantitative technology for quality evaluation of Chinese eaglewood (CE) based on near-infrared (NIR) technology was proposed in this study. The extractives of CE were qualitatively analyzed to determine the types of volatile compounds using gas chromatography-mass spectroscopy and were quantitatively determined using high performance liquid chromatography (HPLC). Agarotetrol was quantitatively determined by the HPLC analysis. The content was found to range widely from 0.016 to 0.104 mg/g. A quantitative prediction model aimed at quality control was proposed based on the qualitative and quantitative results coupled with a partial least squares regression. The coefficient of correlation and residual predictive deviation of the prediction model were determined to be 0.9697 and 5.77, respectively. The practical tests showed an average error of 0.000327%, which indicated that the method was able to provide a novel, quick, and effective quality evaluation of CE.

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