Cab-nanoelectronics

Nanoelectronics Development of a new NMR microsensor

The NMR (Nuclear Magnetic Resonance) is used in a large number of applications from medicine and biological applications to geology and chemistry. The high field spectrometer is the main player in the NMR research, but there are some drawbacks of its use, like the high cost, high maintenance and lack of portability. A low field alternative was developed some time ago, and it has become a growing field of research. One of the latest developments is the use of microcoils, in order to compensate for the lack of homogeneity of the magnetic field at low frequency.

In our research we will use a conventional design in order to test and optimize the external circuit. The new NMR sensor will operate in low field using micromachined planar microcoils constructed in our clean room facilities. The external circuit design was designed in order to minimize the losses and increase the signal to noise ratio. A discrete modular NMR spectrometer has been constructed, based on a purchased FPGA board together with other modular components. A code written in a C compiler is used to send the basic instruction to the board, and a home-made Labview program was developed to control it, in order to have a more efficient, user friendly interface. The system and software was tested in order to remove the errors and defects. The current design is using two separate coils one for the receiver and one for transmitter. After testing and optimization of the system, the next phase of the project will be to replace the receiver coil with magnetoresitive sensors, which are more sensitive than coils at low field frequency.

Our project aims to create a portable, low cost, and highly sensitive NMR sensor to be used in biological and chemical applications, with the possibility of online measurements and integration with other sensors. The new sensor combined with MVDA (Multivariate Data Analysis) can give a great deal of information in cases where it was not possible to get useful data with classical techniques.