An Integrated Nanocomposite Proximity Sensor: Machine Learning-Based Optimization, Simulation, and Experiment.

This paper utilizes multi-objective optimization for efficient fabrication of a novel Carbon Nanotube (CNT) based nanocomposite proximity sensor. A previously developed model is utilized to generate a large data set required for optimization which included dimensions of the film sensor, applied excitation frequency, medium permittivity, and resistivity of sensor dielectric, to maximize sensor sensitivity and minimize the cost of the material used. To decrease the runtime of the original model, an artificial neural network (ANN) is implemented by generating a one-thousand samples data set to create and train a black-box model. This model is used as the fitness function of a genetic algorithm (GA) model for dual-objective optimization. We also represented the 2D Pareto Frontier of optimum solutions and scatters of distribution. A parametric study is also performed to discern the effects of the various device parameters. The results provide a wide range of geometrical data leading to the maximum sensitivity at the minimum cost of conductive nanoparticles. The innovative contribution of this research is the combination of GA and ANN, which results in a fast and accurate optimization scheme.

Thermoplastic polyurethane flexible capacitive proximity sensor reinforced by CNTs for applications in the creative industries.

Wearable sensing platforms have been rapidly advanced over recent years, thanks to numerous achievements in a variety of sensor fabrication techniques. However, the development of a flexible proximity sensor that can perform in a large range of object mobility remains a challenge. Here, a polymer-based sensor that utilizes a nanostructure composite as the sensing element has been presented for forthcoming usage in healthcare and automotive applications. Thermoplastic Polyurethane (TPU)/Carbon Nanotubes (CNTs) composites are capable of detecting presence of an external object in a wide range of distance. The proximity sensor exhibits an unprecedented detection distance of 120 mm with a resolution of 0.3%/mm. The architecture and manufacturing procedures of TPU/CNTs sensor are straightforward and performance of the proximity sensor shows robustness to reproducibility as well as excellent electrical and mechanical flexibility under different bending radii and over hundreds of bending cycles with variation of 4.7% and 4.2%, respectively. Tunneling and fringing effects are addressed as the sensing mechanism to explain significant capacitance changes. Percolation threshold analysis of different TPU/CNT contents indicated that nanocomposites having 2 wt% carbon nanotubes are exhibiting excellent sensing capabilities to achieve maximum detection accuracy and least noise among others. Fringing capacitance effect of the structure has been systematically analyzed by ANSYS Maxwell (Ansoft) simulation, as the experiments precisely supports the sensitivity trend in simulation. Our results introduce a new mainstream platform to realize an ultrasensitive perception of objects, presenting a promising prototype for application in wearable proximity sensors for motion analysis and artificial electronic skin.