Abstract
The implementation of mathematical modelling to achieve improved sensor response times and increased sample throughput without compromising performance is described. Using an experimental system based upon polymer modified internal reflection infrared spectroscopy the value of multi-disciplinary collaboration within the field of chemical sensor development is demonstrated. Through refinement of the application of a Fickian mathematical model, quantitation of analyte species was achieved using reduced data sets. For example, the analyte TeCE displayed a system equilibration time of greater than 1450 s. However, mathematical modification of the Fickian diffusion model, permitted consistent quantifiable absorbance projection for this compound with sensing times as brief as 329 s. The impact of this mathematical approach for the determination of five industrially significant analytes is presented. Mathematical approaches, which allow quantification of model fit to analytical data and define the impact of reducing term numbers on data sets, is also presented.
| Original language | English |
|---|---|
| Pages (from-to) | 170-177 |
| Number of pages | 8 |
| Journal | Sensors and Actuators, B: Chemical |
| Volume | 107 |
| Issue number | 1 SPEC. ISS. |
| DOIs | |
| Publication status | Published - 27 May 2005 |
Keywords
- Curve fitting
- Diffusion modelling
- IR Sensing
- Polymer enrichment
- Sensor development