Hi Ahmad,

What is tricky, is the dye alone measurements. There you can have the app popping up a second window, where the user will need to add the relevant data from the BMG refering to the dye alone measurements and the corresponding concentrations.

That's all! If you need any further information or clarifications, let me know! Thank you!
PB
Hi Ahmad,
It would be super useful if we could remove the outliers from the 8 replicas, in the titration experiments done with the robots (in the BMG data files). What looks to be working is the removal of the highest and lowest values in each set of 8 replicas, eventually keeping the ''best'' 6.
Once you remove the outliers, in order to make the data coming from different host systems (different dyes, zeolite frameworks or pH conditions), it would make sense to normalize the signal intensity from 1 (average of best6 values referring to analyte concentration=0/average of best6 values referring to analyte concentration=0) to (average of best6 values referring to analyte concentration=X/average of best6 values referring to analyte concentration=0). Through that you can compare the quenching ratio occurring from each analyte, allowing for better comparison of different systems or different analytes. BE CAREFUL, we should not simply normalize the signal intensity based on the maximum one, cause we will end up having plots like the following:
What is tricky, is the dye alone measurements. There you can have the app popping up a second window, where the user will need to add the relevant data from the BMG refering to the dye alone measurements and the corresponding concentrations.
That's all! If you need any further information or clarifications, let me know! Thank you!
PB