This work explores the possibility of increasing the density of negatively charged nitrogen-vacancy centers ([NV(-)]) in nanodiamonds using nitrogen-rich type Ib diamond powders as the starting material. The nanodiamonds (10-100 nm in diameter) were prepared by ball milling of microdiamonds, in which the density of neutral and atomically dispersed nitrogen atoms ([N(0)]) was measured by diffuse reflectance infrared Fourier transform spectroscopy. A systematic measurement of the fluorescence intensities and lifetimes of the crushed monocrystalline diamonds as a function of [N(0)] indicated that [NV(-)] increases nearly linearly with [N(0)] at 100-200 ppm. The trend, however, failed to continue for nanodiamonds with higher [N(0)] (up to 390 ppm) but poorer crystallinity. We attribute the result to a combined effect of fluorescence quenching as well as the lower conversion efficiency of vacancies to NV(-) due to the presence of more impurities and defects in these as-grown diamond crystallites. The principles and practice of fabricating brighter and smaller fluorescent nanodiamonds are discussed.

BACKGROUND Specialty coffee fascinates people with its bountiful flavors. Currently, flavor descriptions of specialty coffee beans are only offered by certified coffee cuppers. However, such professionals are rare, and the market demand is tremendous. The hypothesis of this study was to investigate the feasibility to train machine learning (ML) and deep learning (DL) models for predicting the flavors of specialty coffee using near‐infrared spectra of ground coffee as the input. Successful model development would provide a new and objective framework to predict complex flavors in food and beverage products. Results In predicting seven categories of coffee flavors, the models developed using the ML method (i.e. support vector machine) and the deep convolutional neural network (DCNN) achieved similar performance, with the recall and accuracy being 70–73% and 75–77% respectively. Through the proposed visualization method – a focusing plot – the potential correlation among the highly weighted spectral region of the DCNN model, the predicted flavor categories, and the corresponding chemical composition are presented. Conclusion This study has proven the feasibility of applying ML and DL methods on the near‐infrared spectra of ground coffee to predict specialty coffee flavors. The effective models provided moderate prediction for seven flavor categories based on 266 samples. The results of classification and visualization indicate that the DCNN model developed is a promising and explainable method for coffee flavor prediction. © 2021 Society of Chemical Industry

Stopped-flow time courses for chemiluminescence (CL) of the KIO4-luminol-Mn(2+) system showed an instantaneous jump in initial signal followed by two distinct bands. A kinetic model of the form [formula in text] with ten adjustable parameters was proposed to account for CL intensity (I) versus time (t) profiles. The three terms in the model represent the three CL bands. Each band was comprised of a rise part and an exponential decay corresponding to the formation and deactivation of the CL emitter. CL bands could have originated from different CL pathways with the participation of reactive species such as O2(-), (•)OH and (1)O2 generated in the reactions involving IO4(-), O2 and Mn(2+). Subsequent reactions of these reactive species with luminol induced CL emissions. Simulation parameters together with peak positions and intensities of the three CL bands were found to vary in different manners by changing conditions such as reagent concentration, pH and temperature. The temperature-dependence of the rate constants yielded activation energies of 73.2 ± 2.8, 70.1 ± 2.4 and 67.2 ± 1.2 kJ mol(-1) for the three decay processes. Moreover, different substances exhibited a significant influence on the three CL bands and their simulation parameters. The numerous parameters and characteristics of CL emissions could serve as multiple probes for detecting analytes, making this system promising for potential analytical applications.

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