Thin Film Protected Flexible Nanoparticle Strain Sensors: Experiments and Modeling

Vibration Sensors on Flexible Substrates Based on Nanoparticle Films Grown by Physical Vapor Deposition

Flexible electronics have gained a lot of attention in recent years due to their compatibility with soft robotics, artificial arms, and many other applications. Meanwhile, the detection of acoustic frequencies is a very useful tool for applications ranging from voice recognition to machine condition monitoring. In this work, the dynamic response of Pt nanoparticles (Pt NPs)-based strain sensors on flexible substrates is investigated. the nanoparticles were grown in a vacuum by magnetron-sputtering inert-gas condensation. Nanoparticle sensors made on cracked alumina deposited by atomic layer deposition on the flexible substrate and reference nanoparticle sensors, without the alumina layer, were first characterized by their response to strain. The sensors were then characterized by their dynamic response to acoustic frequency vibrations between 20 Hz and 6250 Hz. The results show that alumina sensors outperformed the reference sensors in terms of voltage amplitude. Sensors on the alumina layer could accurately detect frequencies up to 6250 Hz, compared with the reference sensors, which were sensitive to frequencies up to 4250 Hz, while they could distinguish between two neighboring frequencies with a difference of no more than 2 Hz.

High-Sensitivity Pressure Sensors Based on a Low Elastic Modulus Adhesive

With the rapid development of intelligent applications, the demand for high-sensitivity pressure sensor is increasing. However, the simple and efficient preparation of an industrial high-sensitivity sensor is still a challenge. In this study, adhesives with different elastic moduli are used to bond pressure-sensitive elements of double-sided sensitive grids to prepare a highly sensitive and fatigue-resistant pressure sensor. It was observed that the low elastic modulus adhesive effectively produced tensile and compressive strains on both sides of the sensitive grids to induce greater strain transfer efficiency in the pressure sensor, thus improving its sensitivity. The sensitivity of the sensor was simulated by finite element analysis to verify that the low elastic modulus adhesive could enhance the sensitivity of the sensor up to 12%. The preparation of high-precision and fatigue-resistant pressure sensors based on low elastic modulus, double-sided sensitive grids makes their application more flexible and convenient, which is urgently needed in the miniaturization and integration electronics field.

Plasmonic Strain Sensors Based on Au-TiO2 Thin Films on Flexible Substrates

This study aimed at introducing thin films exhibiting the localized surface plasmon resonance (LSPR) phenomenon with a reversible optical response to repeated uniaxial strain. The sensing platform was prepared by growing gold (Au) nanoparticles throughout a titanium dioxide dielectric matrix. The thin films were deposited on transparent polymeric substrates, using reactive magnetron sputtering, followed by a low temperature thermal treatment to grow the nanoparticles. The microstructural characterization of the thin films’ surface revealed Au nanoparticle with an average size of 15.9 nm, an aspect ratio of 1.29 and an average nearest neighbor nanoparticle at 16.3 nm distance. The plasmonic response of the flexible nanoplasmonic transducers was characterized with custom-made mechanical testing equipment using simultaneous optical transmittance measurements. The higher sensitivity that was obtained at a maximum strain of 6.7%, reached the values of 420 nm/ε and 110 pp/ε when measured at the wavelength or transmittance coordinates of the transmittance-LSPR band minimum, respectively. The higher transmittance gauge factor of 4.5 was obtained for a strain of 10.1%. Optical modelling, using discrete dipole approximation, seems to correlate the optical response of the strained thin film sensor to a reduction in the refractive index of the matrix surrounding the gold nanoparticles when uniaxial strain is applied.

Simulation tool for predicting and optimizing the performance of nanoparticle based strain sensors

In this work a Monte-Carlo tool simulating platinum nanoparticle (NP) based strain-sensors, on flexible substrates, is presented. The tool begins by randomly placing the NPs on the simulation area, with the ability to tune the NP surface coverage. After the calculation of the conductive paths that were generated in the previous step, the whole system is represented with an equivalent circuit; the NPs and the NP clusters act as nodes and the inter-particle gaps as resistances. The effective resistance is then calculated with the use of a Laplacian Matrix, which has proven extremely effective in significantly reducing the overall computational time. The simulation results are then benchmarked with experimental measurements from actual strain-sensing devices. The software is capable of predicting the strain-sensitivity for different NP sizes as well as surface coverages, emerging as a powerful computational tool for design-optimization of NP based devices in polymeric substrates, while it could well be extended to other nanocomposite materials used in flexible or stretchable electronic applications.

Resistive crack-based nanoparticle strain sensors with extreme sensitivity and adjustable gauge factor, made on flexible substrates

In this paper, we report the demonstration of highly sensitive flexible strain sensors formed by a network of metallic nanoparticles (NPs) grown under vacuum on top of a cracked thin alumina film which has been deposited by atomic layer deposition. It is shown that the sensor sensitivity depends on the surface density of NPs as well as on the thickness of alumina thin films that can both be well controlled via the deposition techniques. This method allows reaching a record strain sensitivity value of 2.6 × 108 at 7.2% strain, while exhibiting high sensitivity in a large strain range from 0.1% to 7.2%. The demonstration is followed by a discussion enlightening the physical understanding of sensor operation, which enables the tuning of its performance according to the above process parameters.