Master Thesis MSTR-2022-01

BibliographyMithawala, Himanshu Ujwal: A Comparative Evaluation and Implementation of Droplet Detection in Non-contact Liquid-handler for Nano to Microliter Volumes.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Master Thesis No. 1 (2022).
81 pages, english.

Life science research plays an essential role in the progress of society. Especially in this century, life science research has progressed exponentially, which has been orchestrated by the advancement in laboratory technology. For instance, spectrometers, centrifuges, thermocyclers, microscopes, lab hoods, autoclaves, lab freezers, and chromatography systems are a few examples of instruments and equipment. This has made it possible to carry out vast number of experiments rapidly and in a cost effective manner. Traditionally, a major bottleneck in the research flow has been liquid handling as bio-samples must regularly be transferred between containers of varied sizes and distributed onto substrates of varying types. In many life science applications, samples of sub-microlitre volumes are needed with high throughput for setting up experiments, dilution or normalization. The accuracy and precision of liquid dispensing systems is detrimental to incorporate quality into their processes while reducing overall cost and time. A dispensing verification is employed in the system to validate the performance of the dispenser, by detection and analysis of sub-microlitre droplets on the fly. Various different methodologies and their advantages and disadvantages, for contact and non-contact detection of droplets are studied in this thesis. The use of a light beam field and light interference to detect passing droplets is characterized along with the various electronic and physical factors affecting the detection. The physical geometry of the droplet formation and break up is discussed, correlating these with the corresponding light interference and ways to improve the signal quality are considered to extract maximum features from the signal as possible. Further, a detailed analysis of these droplet signals is carried out and various machine learning approaches are explored to reasonably estimate droplet volume and ensure that the device meets the quality assurance requirements.

Department(s)University of Stuttgart, Institute of Architecture of Application Systems
Superviser(s)Aiello, Prof. Marco; Mazhar, Umair
Entry dateFebruary 7, 2022
   Publ. Computer Science