A simple and soft chemical deaggregation method producing single-digit detonation nanodiamonds.

Detonation nanodiamonds (DNDs) are a class of very small and spherical diamond nanocrystals. They are used in polymer reinforcement materials or as drug delivery systems in the field of nanomedicine. Synthesized by detonation, only the final deaggregation step down to the single-digit nanometer size (<10 nm) unfolds their full potential. Existing deaggregation methods mainly rely on mechanical forces, such as high-power sonication or bead milling. These techniques entail drawbacks such as contamination of the sample and the need for a specialized apparatus. In this paper, we report a purely chemical deaggregation method by simply combining oxidation in air followed by a boiling acid treatment, to produce highly stable single-digit DNDs in a suspension. The resulting DNDs are surface functionalized with carboxyl groups, the final boiling acid treatment removes primary metal contaminants such as magnesium, iron or copper and the nanoparticles remain dispersed over a wide pH range. Our method can be easily carried out in a standard chemistry laboratory with commonly available laboratory apparatus. This is a key step for many DND-based applications, ranging from materials science to biological or medical applications.

Machine-Learning Optimization of Multiple Measurement Parameters Nonlinearly Affecting the Signal Quality.

Determination of optimal measurement parameters is essential for measurement experiments. They can be manually optimized if the linear correlation between them and the corresponding signal quality is known or easily determinable. However, in practice, this correlation is often nonlinear and not known a priori; hence, complicated trial and error procedures are employed for finding optimal parameters while avoiding local optima. In this work, we propose a novel approach based on machine learning for optimizing multiple measurement parameters, which nonlinearly influence the signal quality. Optically detected magnetic resonance measurements of nitrogen-vacancy centers in fluorescent nanodiamonds were used as a proof-of-concept system. We constructed a suitable dataset of optically detected magnetic resonance spectra for predicting the optimal laser and microwave powers that deliver the highest contrast and signal-to-noise ratio values by means of linear regression, neural networks, and random forests. The model developed by the considered neural network turned out to have a coefficient of determination significantly higher than that of the other methods. The proposed method thus provided a novel approach for the rapid setting of measurement parameters that influence the signal quality in a nonlinear way, opening a gate for fields like nuclear magnetic resonance, electron paramagnetic resonance, and fluorescence microscopy to benefit from it.