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A photo-rheological fluid (PRF) is a smart fluid which exhibits different viscosity under UV irradiation. A PRF is comprehensively presented in this work, with particular focus on its responses under UV off/on conditions. The isomeric conversion from SP to MC and vice versa under UV off and on, respectively, showed unequal rates of transformation. As a result, a complex non-linear hysteretic response was observed. To be used indifferent types of sensors and actuators which can exploit its rheological properties, it is essential the PRF have linearized hysteresis behavior. To minimize the asymmetric non-linear hysteresis characteristics under UV on and off conditions, the well-known long-lasting phosphor SAO (SrAl2O4:Eu , Dy) was incorporated. The incorporation of SAO in the PRF improved the linearity of the PRF response, although the conversion rate was not identical under UV off/on conditions. The SAO particles were observed to settle over time due to phase splitting, undermining the usefulness of the SAO-PRF composite. Instead of improving the PRF response by further adjusting the PRF composite, a software approach based on Long Short-Term Memory Recurrent Neural Networks (LSTM RNN) was employed to model and compensate the asymmetric non-linear hysteresis response, ensuring the realization of sensors and actuators that exploit PRF as hardware. (Received March 2, 2021; Accepted March 5, 2021)

Ultraviolet Light-Responsive Photorheological Fluid for Sensors and Actuators Realized by Phosphorescence Effects and LSTM RNN