Harnessing highly efficient triboelectric sensors and machine learning for self-powered intelligent security applications

In the contemporary epoch, distinguished by a transition from the internet-of-things (IoT) to the artificial intelligence of things (AIoT), individual electronic appliances necessitate inherent power-generation, independence from internet connectivity, and an imbued degree of intellect. Devices governed by pressure or strain sensors particularly demand such attributes. Responding to this technological imperative, our study endeavored to conceive an intelligent door security apparatus grounded on the universally adopted numerical input system. Despite the commercialization of identification systems such as fingerprint, iris, or facial recognition, these mechanisms suffer from susceptibility to a variety of functional aberrations. Consequently, our investigation concentrated on a security system predicated on numerical input. This necessitated the formulation of a swift, self-powered pressure sensor characterized by sensitivity to minute pressure changes. As such, we engineered a triboelectric pressure sensor incorporating a composite of Ti₃C₂-based MXene and polydimethylsiloxane (PDMS) as the electronegative stratum, and Nylon functioning as the electropositive layer. Addressing the sensor's intrinsic deficiency in sensitivity to pressure, we augmented the MXene-PDMS composite's surface with an out-of-plane wavy structure, and utilized a Nylon stratum composed of nanofibers, thereby amplifying the contact area under pressurized conditions. This meticulously developed sensor displayed a sensitivity metric of 0.604 kPa⁻¹ at 15 kPa, and notably, the swiftest response times recorded amongst triboelectric pressure sensors to date. Post attachment of the sensor to a numeric keypad (ranging from 0 to 9), we meticulously measured the signal alterations contingent on each key press, resulting in a comprehensive dataset. Employing a multitude of machine learning algorithms, we realized an exemplary degree of precision in both training and testing phases. The pragmatic implications of this work are noteworthy. Not only does our technology facilitate the unlocking of a door by entering the correct numerical code, but it is capable of recognizing distinct triboelectric signal patterns, corresponding to the specific manner of key entry by an authorized user, offering an additional dimension of security.