From evaluation to licencing of low-cost miniature conductivity temperature and dissolved oxygen sensor technology

Measuring water parameters is required in many industries and in environmental science. The most widely measured and required parameters are temperature, salinity and dissolved oxygen. These measurements are used in multiple applications such as weather forcasting, climate modelling, water quality assessment, sewage processing and aquaculture. In many cases a high precision measurement and continuous data is required. Commercial sensors with high performance have delivered this data and have enabled dramatic advances in these fields. However, they are expensive (~$10000) and large preventing widespread use in high density data collection systems. Smaller and cheaper ($500) sensors are available, but currently are not sufficiently accurate or precise for many applications.

The University of Southampton and the National Oceanography Centre have developed a unique ultra miniature high-precision salinity temperature and dissolved oxygen sensor for water analysis. These parameters are all measured on a small glass chip (8 x 10 mm) with patterned metal tracks to form the sensors. Salinity is calculated from a combination of temperature and conductivity measured with four micro electrodes in contact with the water. This chip is plugged into custom made electronics that operates the chip and stores the data and communicates with the outside world. The total sensor system is the size of a marker pen.

The technology has significant market potential (estimated $420M at 2011 rates) and has received significant commercial interest. However, the current barrier to commercialisation is a technical problem with long term stability. This is only 0.016 C in three months, but many applications require only 0.001 C stability over 3 months.

We have identified that the source of the problem as water uptake which causes swelling (1.6% by volume) of the polymer that we use to package and insulate the metal tracks. This swelling causes the chip and metal track sensors to bend or elongate over time causing the drift. This affects the temperature and conductivity measurement and hence the salinity accuracy.

The solution is to replace the polymer insulator with a hard and water resistant material such as Silicon Oxide. Silicon oxide is widely used in electronics where very thin (100 nm, one ten thousandth of a millimetre) layers are deposited. In our chip we require a much thicker layer > 0.01 mm. The challenge is to develop a process to manufacture these thick layers and new sensor chips.

Once this technical problem is resolved, we will conduct short and long term testing to verify sufficient performance for the markets and applications. We will do this in partnership with sensor companies who we hope to work with to bring the product to market. The outcome of the project should be a license agreement with a company and a new product line.

This project will improve the prospect of achieving commercial uptake by solving the current technological barrier to adoption namely long term drift caused by water uptake into the sensor insulation. Further it will increase its value by improving the performance of measurement, making it comparable with instruments that are an order of magnitude larger and cost 20 times more. This will greatly extend the addressable market and should maximise sales.
The project will also enable extensive knowledge exchange and market building activities which will improve industry engagement, will significantly increase awareness of the technology in user groups and for potential licensees. This activity will lead to a license agreement with a company enabling commercialisation of the technology, bringing global impact in multiple sectors.
Wider societal and environmental benefit will stem from the availability of a new low-cost high performance water quality sensor that will enable more frequent, more widespread and better quality measurement and management of our environment. For example this will enable improved response times to events (such as storm driven flooding and pollution events). More better data will also enable improved forecasting and modelling of the environment (e.g. for weather forecasting).