Spectrophotometric Detection of Copper in Water by Lab-on-a-chip Technology: Application to Electroplating
Mossotti, Giulia
Periolatto, Monica
Catania, Felice
Perrucci, Francesco
Scaltrito, Luciano
Ferrero, Sergio

How to Cite

Mossotti G., Periolatto M., Catania F., Perrucci F., Scaltrito L., Ferrero S., 2022, Spectrophotometric Detection of Copper in Water by Lab-on-a-chip Technology: Application to Electroplating, Chemical Engineering Transactions, 96, 481-486.


Copper electroplating requires baths with high metal concentration, as far as 100 g/L. The monitoring is mandatory in terms of good results of the process and safety of the wastewater products. A colorimetric method was chosen to detect Cu concentration, effective both for high and low metal amounts. In the range 1-20g/L, the spectrophotometric analysis shows a well-defined absorbance peak, due to Cu in solution, at about 805 nm, giving rise to a calibration curve with good linearity toward metal concentration. The detection field can be enlarged at least from 100 g/L to 10 ppm easily by proper dilution, for higher concentrations, or by adjusting the optical length, for small copper amounts. The influence of pH and aging was also investigated.
Meanwhile, for concentrations lower than 10 ppm, the addition of Zincon™ as a complexing agent is required. Zincon-Cu complex solutions show in fact a new absorbance peak at 665 nm, well visible even on 100 ppb copper solutions, useful for the drawing of a linear calibration curve. Moreover, even in this case the increase of the optical path allows the detection of copper ions in concentrations as small as few ppb.
Collected results point out the proposed colorimetric method as a good candidate to address the need for capillary and frequent monitoring of copper in water, in a wide range of concentrations. The aim is to design an affordable and versatile sensor for routine monitoring. The laboratory process can be easily optimized and adapted with the lab-on-a-chip technology applied to a microfluidic technology, by reducing the volumes of samples and reagents, miniaturizing the sensors, and automatizing the whole process, from sampling to data recovery.