The subject of healing and repair of damaged microelectrodes has become of particular interest as the use of integrated circuits, energy storage technologies, and sensors within modern devices has increased. As the dimensions of the electrodes shrink together with miniaturization of all the elements in modern electronic devices, there is a greater risk of mechanical-, thermal-, or chemical-induced fracture of the electrodes. In this research, a novel method of electrode healing using electrokinetically assembled carbon nanotube (CNT) bridges is presented. Utilizing the previously described step-wise CNT deposition process, conductive bridges were assembled across ever-larger electrode gaps, with the width of electrode gaps ranging from 20 microns to well over 170 microns. This work represents a significant milestone since the longest electrically conductive CNT bridge previously reported had a length of 75 microns. To secure the created conductive CNT bridges, they are fixed with a layer of electrodeposited polypyrrole (a conductive polymer). The resistance of the resulting CNT bridges, and its dependence on the size of the electrode gap, is evaluated and explained. Connecting electrodes via conductive CNT bridges can find many applications from nanoelectronics to neuroscience and tissue engineering.

The present work describes the phenomenological approach to automatically determine the frequency range for positive and negative dielectrophoresis (DEP)-an electrokinetic force that can be used for massively parallel micro- and nano-assembly. An experimental setup consists of the microfabricated chip with gold microelectrode array connected to a function generator capable of digitally controlling an AC signal of 1 V (peak-to-peak) and of various frequencies in the range between 10 kHz and 1 MHz. The suspension of latex microbeads (3-μm diameter) is either attracted or repelled from the microelectrodes under the influence of DEP force as a function of the applied frequency. The video of the bead movement is captured via a digital camera attached to the microscope. The OpenCV software package is used to digitally analyze the images and identify the beads. Positions of the identified beads are compared for successive frames via Artificial Intelligence (AI) algorithm that determines the cloud behavior of the microbeads and algorithmically determines if the beads experience attraction or repulsion from the electrodes. Based on the determined behavior of the beads, algorithm will either increase or decrease the applied frequency and implement the digital command of the function generator that is controlled by the computer. Thus, the operation of the study platform is fully automated. The AI-guided platform has determined that positive DEP (pDEP) is active below 500 kHz frequency, negative DEP (nDEP) is evidenced above 1 MHz frequency and the crossover frequency is between 500 kHz and 1 MHz. These results are in line with previously published experimentally determined frequency-dependent DEP behavior of the latex microbeads. The phenomenological approach assisted by live AI-guided feedback loop described in the present study will assist the active manipulation of the system towards the desired phenomenological outcome such as, for example, collection of the particles at the electrodes, even if, due to the complexity and plurality of the interactive forces, model-based predictions are not available.