The interaction between metal nanoparticles and bacteria belongs to the central issues in a dynamically growing bionanotechnological research. Herein, we investigated the adhesion efficiency of gold nanoparticles (30 nm) for various bacterial strains, both Gram-positive (Bacillus subtilis, Staphylococcus carnosus) and Gram-negative (Neisseria subflava, Stenotrophomonas maltophilia). The thorough microscopic (SEM/TEM) observations revealed that the nanoparticles do not penetrate into the bacterial cells but adhere to the walls. Large differences in the adhered nanoparticles amount were observed for the investigated strains (B. subtilis >> S. carnosus > N. subflava > S. maltophilia). A direct correlation between the number of the attached nanoparticles and the ζ-potential of the bacterial strains was found, and the results were rationalized in terms of the DLVO model. The calculated DLVO energy profiles revealed that the activation barriers for the adhesion process are rather small (1.45-1.55 kT), and the primary energy minima of 120-170 kT are favorable for the effective adsorption process. The established linear correlation between the nanoparticles adhered to the cell surface and the size of the critical volume around the bacterial cell, where the attraction forces dominate, implies that the observed dramatic differences in the attachment efficiency result from the availability of the nanoparticles in the critical volume of the surrounding suspensions. Owing to non-specific interactions governed by the ζ-potential mainly, the obtained results can be readily extended for the other bacteria-nanoparticle systems, providing a rational background for future advances in bacteria detection and thorough characterization via SERS method as well as for nanoparticles assemblies towards nanoelectronics.

In microbiology it is diagnostically useful to recognize various genera and species of bacteria. It can be achieved using computer-aided methods, which make the recognition processes more automatic and thus significantly reduce the time necessary for the classification. Moreover, in case of diagnostic uncertainty (the misleading similarity in shape or structure of bacterial cells), such methods can minimize the risk of incorrect recognition. In this article, we apply the state of the art method for texture analysis to classify genera and species of bacteria. This method uses deep Convolutional Neural Networks to obtain image descriptors, which are then encoded and classified with Support Vector Machine or Random Forest. To evaluate this approach and to make it comparable with other approaches, we provide a new dataset of images. DIBaS dataset (Digital Image of Bacterial Species) contains 660 images with 33 different genera and species of bacteria.

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Non-cytotoxic, temperature-responsive and antibacterial polymer/silver nanoparticles nanocomposite coatings with prolonged action.