Research

The project “Holographic Identification for Safe Freshwater Environments” (HI-SaFE) addresses the urgent need for rapid, accurate, and cost-effective detection of some important waterborne pathogens that threaten public health and safety. Microorganisms such as Legionella, E. coli, and toxin-producing cyanobacteria like Microcystis spp. pose severe health risks, worsened by aging infrastructure and climate change. Traditional detection methods are often slow, costly, or limited. Holloid GmbH develops a groundbreaking solution based on distributed digital holographic microscopy, enabling rapid and precise detection of different pathogens, even at low concentrations. In this project, BOKU Institute of Colloid and Biointerface Science supports Holloid in developing this solution to find pathogens in water. By integrating portable, real-time monitoring and adaptable sampling strategies, the project’s outcomes can ensure swift responses to contamination events, enhancing safety for both drinking and recreational water sources and thereby contributing to public health.

When culturing any bacteria or cells, it is important to monitor the growth. The density of a culture determines, for example, its state and production yields. The current standard for measuring the growth of cultures is to measure the optical density (OD), in which the intensity of the light transmitted through the sample is compared to the intensity of the incident light. These measurements are often only taken at large time intervals and manually. They do not provide any information about the actual concentration of bacteria or cells. In this project, we will develop an automated system that provides users with the concentration of bacteria or cells during cultivation. The key enabling technology is high-throughput 3D microscopy, supported by precise automated dilution, integrated with the Hololoid holographic imaging and analytics platform.

In this project we want to couple the powerful methods of holographic microscopy for water analysis with state of the art IoT and AI methods. We will build and deploy several holographic microscopes to collect and characterize water samples locally at a few strategically chosen locations in the network. These holographic microscopes take samples fully autonomously and image each and every light scattering object in the sample volume. This is done by shining coherent light through the sample where some of the light is scattered by microscopic objects dispersed in the water. This scattered light interferes with the unscattered fraction of the illuminating light on the detector array of the microscope. By numerical back propagation we can locate each and every object in the sample volume that scatteres enough light to be distinguished. By repeating the procedure we can track the objects’ motion. In practice this method allows to characterize what is in the water in terms of plastic objects, plant fragments, sediments, small animals, algae and bacteria. In this project we want to correlate the wealth of information about dispersed objects in water with time series of currently available quality parameters. We will analyse the supply network and current sample collection sites to strategically place our holographic microscopes in the water supply network objective to make the early detection of environmental issues possible.