Alternating-current electro-osmosis, a phenomenon of fluid transport due to the interaction between an electrical double layer and a tangential electric field, has been used both for inducing fluid movement and for the concentration of particles suspended in the fluid. This offers many advantages over other phenomena used to trap particles, such as placing particles at an electrode centre rather than an edge; benefits of scale, where electrodes hundreds of micrometers across can trap particles from the molecules to cells at the same rate; and a trapping volume limited by the vortex height, a phenomenon thus far unstudied. In this paper, the collection of particles due to alternating-current electro-osmosis driven collection is examined for a range of particle concentrations, inter-electrode gap widths, chamber heights and media viscosity and density. A model of collection behaviour is described where particle collection over time is governed by two processes, one driven by the vortices and the other by sedimentation, allowing the determination of the maximum height of vortex-driven collection, but also indicates how trapping is limited by high particle concentrations and fluid velocities. The results also indicate that viscosity, rather than density, is a significant governing factor in determining the trapping behaviour of particles.
The assembly of carbon nanotubes (CNTs) across planner electrodes using dielectrophoresis (DEP) is one of the standard methods used to fabricate CNT-based devices such as sensors. The medium drag velocity caused by electrokinetic phenomena such as electrothermal and electroosmotic might drive CNTs away from the deposition area. This problem becomes critical at large-scale electrode structures due to the high attenuation of the DEP force. Herein, we simulated and experimentally validated a novel DEP setup that uses a top glass cover to minimize the medium drag velocity. The simulation results showed that the drag velocity can be reduced by 2-3 orders of magnitude compared with the basic DEP setup. The simulation also showed that the optimum channel height to result in a significant drag velocity reduction was between 100 μm and 240 μm. We experimentally report, for the first time, the assembly and alignment of CNT bridges across indium tin oxide (ITO) electrodes with spacing up to 125 μm. We also derived an equation to optimize the CNT's concentration in suspensions based on the electrode gap width and channel height. The deposition of long CNTs across ITO electrodes has potential use in transparent electronics and microfluidic systems.
Surfactants such as sodium dodecyl sulfate (SDS) are used to improve the dispersity of carbon nanotubes (CNTs) in aqueous solutions. The surfactant concentration in CNT solutions is a critical factor in the dielectrophoretic (DEP) manipulation of CNTs. A high surfactant concentration causes a rapid increase in the solution conductivity, while a low concentration results in undesirably large CNT bundles within the solution. The increase in the solution conductivity causes drag velocity that obstructs the CNT manipulation process due to the electrothermal forces induced by the electric field. The presence of large CNT bundles is undesirable since they degrade the device performance. In this work, mathematical modeling and experimental work were used to optimize the concentration of the SDS surfactant in multiwalled carbon nanotube (MWCNT) solutions. The solutions were characterized using dynamic light scattering (DLS) and ultraviolet-visible spectroscopy (UV-Vis) analysis. We found that the optimum SDS concentration in MWCNT solutions for the successful DEP manipulation of MWCNTs was between 0.1 and 0.01 wt %. A novel DEP configuration was then used to assemble MWCNTs across transparent electrodes. The configuration was based on ceiling deposition, where the electrodes were on top of a droplet. The newly proposed configuration reduced the drag velocity and prevented the assembly of large MWCNT bundles. MWCNTs were successfully assembled and aligned across interdigitated electrodes (IDEs). The assembly of MWCNTs from aqueous solutions across transparent electrodes has potential use in future transparent electronics and sensor devices.
Poly(methyl methacrylate) (PMMA) is a lightweight insulating polymer that possesses good mechanical stability. On the other hand, polyaniline (PANi) is one of the most favorable conducting materials to be used, as it is easily synthesized, cost-effective, and has good conductivity. However, most organic solvents have restricted potential applications due to poor mechanical properties and dispersibility. Compared to PANi, PMMA has more outstanding physical and chemical properties, such as good dimensional stability and better molecular interactions between the monomers. To date, many research studies have focused on incorporating PANi into PMMA. In this review, the properties and suitability of PANi as a conducting material are briefly reviewed. The major parts of this paper reviewed different approaches to incorporating PANi into PMMA, as well as evaluating the modifications to improve its conductivity. Finally, the polymerization condition to prepare PMMA/PANi copolymer to improve its conductivity is also discussed.
Memristors have revolutionized the path forward for brain-inspired computing. However, the instability of the nucleation process of conductive filaments based on active metal electrodes leads to the discrete distribution of switching parameters, which hinders the realization of high-performance and low-power devices for neuromorphic computing. In response, a carbon conductive filament-induced robust memristor is demonstrated with variation coefficients as low as 3.9%/-1.18%, a threshold power as low as 10-9 W, and 3 × 106 s retention and 107 cycle endurance behaviors can be maintained. The recognition accuracy for Modified National Institute of Standards and Technology (MNIST) handwriting is as high as 96.87%, attributed to the high linearity of the iterative updating of synaptic weights. The demodulation and storage functions of the American Standard Code for Information Interchange (ASCII) are demonstrated by programmable pulse modulation. Notably, the transmission electron microscopy (TEM) images allow the observation of carbon conductive filament paths formed in the low resistance state. First-principles calculations analyze the energetics of defects involved in the diffusion of carbon atoms into MoTe2 films. This work presents a novel guideline for studying memristor-based neuromorphic computing.
As an emerging carbon-based material, carbon quantum dots (CQDs) have shown unstoppable prospects in the field of bionic electronics with their outstanding optoelectronic properties and unique biocompatible characteristics. In this study, a novel CQD-based memristor is proposed for neuromorphic computing. Unlike the models that rely on the formation and rupturing of conductive filaments, it is speculated that the resistance switching mechanism of CQD-based memristors is due to the conductive path caused by the hybridization state transition of the sp2 carbon domain and sp3 carbon domain induced by the reversible electric field. This avoids the drawback of uncontrollable nucleation sites leading to the random formation of conductive filaments in resistive switching. Importantly, it illustrates that the coefficient of variation (CV) of the threshold voltage can be as low as -1.551% and 0.083%, which confirms the remarkable uniform switching characteristics. Interestingly, the Pavlov's dog reflection as an important biological behavior can be demonstrated by the samples. Finally, the accuracy recognition rate of MNIST handwriting can reach up to 96.7%, which is very close to the ideal number (97.8%). A carbon-based memristor based on a new mechanism presented provides new possibilities for the improvement of brain-like computing.