The Steady March toward Biomimetic Nanoelectronics

Controlling the Morphology in Electrostatic Self-Assembly via Light

Electrostatic self-assembly of macroions is an emerging area with great potential in the development of nanoscale functional objects, where photo-irradiation responsiveness can either elevate or suppress the self-assembly. The ability to control the size and shape of macroion assemblies would greatly facilitate the fabrication of desired nano-objects that can be harnessed in various applications such as catalysis, drug delivery, bio-sensors, and actuators. Here, we demonstrate that a polyelectrolyte with a size of 5 nm and multivalent counterions with a size of 1 nm can produce well-defined nanostructures ranging in size from 10-1000 nm in an aqueous environment by utilizing the concept of electrostatic self-assembly and other intermolecular non-covalent interactions including dipole-dipole interactions. The pH- and photoresponsiveness of polyelectrolytes and azo dyes provide diverse parameters to tune the nanostructures. Our findings demonstrate a facile approach to fabricating and manipulating self-assembled nanoparticles using light and neutron scattering techniques.

Artificial Intelligence Algorithms Enable Automated Characterization of the Positive and Negative Dielectrophoretic Ranges of Applied Frequency

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.