Thermochromic sensor design based on Fe(II) spin crossover/polymers hybrid materials and artificial neural networks as a tool in modelling

Abstract This article explores the use of multi-objective evolutionary machine learning techniques to find the minimum number of sensors from a pull of 6 sensors as well as the minimum number of analytical signals belonging to each selected sensor for the design of an optimal colourimetric temperature sensor. The analytical information was obtained with a calibrated neural network that provides the best temperature estimation with respect to the selected colourimetric sensor responses from a previously developed sensor array. The sensor array was developed by embedding the linear spin crossover material [Fe-(NH 2 trz) 3 ](BF 4 ) 2 into polymers with different polarity, offering different thermochromic responses related to different morphologies of the spin crossover particles when embedded in each polymer. The different thermochromic responses are tracked by the green component of the RGB colour space and the a * from CIEL* a * b * obtained with a conventional photographic digital camera. These two colour signals are used as analytical parameters for the subsequent computer processing and model calibration. The use of multi-objective optimization techniques for neural network calibration demonstrated that only 3 signals coming from 3 sensors of the 6 studied are sufficient to provide optimal temperature estimation. The optimized selection was the green channel from polyurethane hydrogel D6 and PVC prepared in THF and a * from PMMA prepared in toluene.