In this case, we had to resort to a different solution, namely emulating human actions such as keyboard button presses or mouse clicks in software. While this approach in principle allows for automating the interaction with any device via its native control software (proprietary or free) and completely removes the need for direct communication with the hardware via low-level commands, APIs, or SDKs, it is of course not very robust. If any unforeseen events occur during the emulated interactions (such as a notification or popup grabbing keyboard or mouse input focus), it can lead to the automation routine sending the inputs to the wrong window, which can result in unforeseen or undesired actions. Moreover, it is very hard to account for unexpected errors that can occur during the measurement when using this approach. We took great care to ensure that the correct window is maximized and has input focus before sending any commands, use a dedicated PC for running the measurement software and automation procedure, and read pixel values from the screen to ensure that the hardware is online and ready to measure before attempting the automatic measurement. However, the caveats mentioned above still remain to some extent. Nevertheless, so far this approach worked very well for us, and we were able to reliably automate DLS and Zeta potential measurements following this approach. By carefully analyzing the database to which the measurement results are saved, we can also extract all relevant data and metadata when the measurements are complete, upload the results to an electronic lab notebook (see also next section on software), and use the characterization results as input for ML or AI models in closed loop optimization processes without any human interaction.