The introduction of smart virtual assistants (VAs) and corresponding smart devices brought a new degree of freedom to our everyday lives. Voice-controlled and Internet-connected devices allow intuitive device controlling and monitoring from all around the globe and define a new era of human-machine interaction. Although VAs are especially successful in home automation, they also show great potential as artificial intelligence-driven laboratory assistants. Possible applications include stepwise reading of standard operating procedures (SOPs) and recipes, recitation of chemical substance or reaction parameters to a control, and readout of laboratory devices and sensors. In this study, we present a retrofitting approach to make standard laboratory instruments part of the Internet of Things (IoT). We established a voice user interface (VUI) for controlling those devices and reading out specific device data. A benchmark of the established infrastructure showed a high mean accuracy (95% ± 3.62) of speech command recognition and reveals high potential for future applications of a VUI within the laboratory. Our approach shows the general applicability of commercially available VAs as laboratory assistants and might be of special interest to researchers with physical impairments or low vision. The developed solution enables a hands-free device control, which is a crucial advantage within the daily laboratory routine.
The manual counting of colonies on agar plates to estimate the number of viable organisms (so-called colony-forming units-CFUs) in a defined sample is a commonly used method in microbiological laboratories. The automation of this arduous and time-consuming process through benchtop devices with integrated image processing capability addresses the need for faster and higher sample throughput and more accuracy. While benchtop colony counter solutions are often bulky and expensive, we investigated a cost-effective way to automate the colony counting process with smart devices using their inbuilt camera features and a server-based image processing algorithm. The performance of the developed solution is compared to a commercially available smartphone colony counter app and the manual counts of two scientists trained in biological experiments. The comparisons show a high accuracy of the presented system and demonstrate the potential of smart devices to displace well-established laboratory equipment.
In this study, the wave-induced distribution of 13 microplastic (MP) samples of different size, shape, and density was investigated in a wave flume with a sandy mobile beach bed profile. The particle parameter were chosen based on an occurrence probability investigated from the field. MP abundances were analyzed in cross-shore and vertical direction of the test area after over 40,000 regular waves. It was found, that MP particles accumulated in more shallow waters with increasing size and density. Particles with high density (ρs>1.25 g/cm3) have been partly confined into deeper layers of the sloping beach during the formation of the bed profile. Particles with a density lower than that of water used in the experiments floated constantly in the surf zone or deposited on the beach caused by wave run-up. A correlation was found between the settling velocity of the MP particles and the flow velocity at the accumulation point and a power function equation developed. The obtained results were critically discussed with findings from the field and further laboratory studies.
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