Vortex-like vs. turbulent mixing of a Viscum album preparation affects crystalline structures formed in dried droplets

Abstract

Various types of motion introduced into a solution can affect, among other factors, the alignment and positioning of molecules, the agglomeration of large molecules, oxidation processes, and the production of microparticles and microbubbles. We employed turbulent mixing vs. laminar flow induced by a vortex vs. diffusion-based mixing during the production of Viscum album Quercus L. 10^-3 following the guidelines for manufacturing homeopathic preparations. The differently mixed preparation variants were analyzed using the droplet evaporation method. The crystalline structures formed in dried droplets were photographed and analyzed using computer-supported image analysis and deep learning. Computer-supported evaluation and deep learning revealed that the patterns of the variant succussed under turbulence are characterized by lower complexity, whereas those obtained from the vortex-mixed variant are characterized by greater complexity compared to the diffusion-based mixed control variant. The droplet evaporation method could provide a relatively inexpensive means of testing the effects of liquid flow and serve as an alternative to currently used methods.

Keywords: Crystallization; Deep-learning; Droplet evaporation; Homeopathy; Turbulent and laminar flow.

Figure 1

Examples of central structures formed inside dried droplets of Viscum album Quercus L. 3× variants prepared by different mixing techniques: machine-made turbulent succussions (a), laminar flow induced by handmade vortex (b), and diffusion-based mixing (c). Images with local connected fractal dimension equal to or similar to that of the variant’s mean are presented. Photographs were taken in darkfield and magnification of 100×.

Figure 2

Distribution of image patches grouped after applying a semi-supervised deep learning approach for categories less fractal, medium fractal, and more fractal found in the patterns from dried droplets of Viscum album Quercus 3× produced with different mixing procedures: diffusion-based mixing (D), turbulent vertical succussions (T), and laminar flow induced by handmade vortex (L). The patches obtained from the L mixing procedure show the highest fractal composition, having 56.60% of the patches in the “more fractal” category. The patches obtained from the T mixing procedure exhibit the lowest fractal composition, having 65.2% of the patches in the “less fractal” category.

Figure 3

Distribution of image patches grouped after applying an unsupervised deep learning approach for categories less fractal (closer to 0 along the x-axis), medium fractal (closer to 7 along the x-axis), and more fractal (closer to 13 along the x-axis) found in the patterns from dried droplets of Viscum album Quercus 3× produced with different mixing procedures: D—diffusion-based mixing (middle), T—turbulent vertical succussions (left), L—laminar flow induced by handmade vortex (right). The 13 groups obtained from the unsupervised approach emphasize the fractal tendency already exhibited in Fig. 2 for the different mixing methods.

Figure 4

Patch distribution at image level for images obtained from unsupervised deep learning applied on dried droplets of Viscum album Quercus 3× produced by means of diffusion-based mixing (variant D) (a), turbulent mixing (variant T) (b), and laminar flow induced by a handmade vortex (variant L) (c). The bias toward more and less fractal behavior of the dried droplet is visible for the laminar and turbulent modalities, while the diffusion modality presents both biases.

Figure 5

Confusion matrix. Results of the classification task using support vector machine on images characterized as feature vectors obtained from unsupervised deep learning applied on dried droplets of Viscum album Quercus 3× preparation produced through diffusion-based mixing, turbulent succussing, or laminar flow. These results provide more profound insights into the separability of the different mixing procedures.