Assessment of biofilm formation on novel transparent coatings for application to a low cost optical sensor deployed in an estuarine environment
Richards, Chloe, Briciu Burghina, Ciprian ConstantinORCID: 0000-0001-8682-9116 and Regan, FionaORCID: 0000-0002-8273-9970
(2018)
Assessment of biofilm formation on novel transparent coatings for application to a low cost optical sensor deployed in an estuarine environment.
In: Europtrode 2018, 25 - 28 Mar 2018, Naples, Italy.
Water quality monitoring using autonomous optical sensors suffers many challenges in the aquatic environment of which biofouling is one that impacts cost of maintenance and data quality and integrity. In high fouling season sensors often require maintenance and cleaning every two weeks adding significant cost to the monitoring programme. The microbial colonisation occurs rapidly influencing the sensor measurements. Data collected shows that fouling affects different sensors in different ways. The Optical Colorimetric Sensor (OCS) [1] is designed to be deployable in the marine environment for long periods of time. The sensing elements are submerged to a depth of 1m, with the electronic and communication housing above the surface. The sensor head is the part of the sensor which houses the optics and the detection abilities of the sensor. As the sensor is designed to be deployed in the marine environment, biofilms will most likely form on all components; to minimise this effect the sensor head is constructed of copper which has strong antifouling properties. The OCS sensor head consists of five LEDs of differing wavelengths: IR (850nm), red (627nm), amber (583nm), green (515nm) and blue (430nm). Two silicon photodiode detectors (PDs), having a spectral response >20% ranging from 410nm to 1080nm are used to detect the light. These photodiodes are placed at 90˚ and 180˚ to the optical path of the light emitted from the LEDs. The work shows the diversity of biofilm types that can occur during multiple deployments and the impact of biofouling on the long-term datasets from the OCS.