The accurate measurement of wall zeta potentials and solute–surface interaction length scales for electrolyte and non-electrolyte solutes, respectively, is critical to the design of many biomedical and microfluidic applications. We present a novel microfluidic approach using diffusioosmosis for measuring either the zeta potentials or the characteristic interaction length scales for surfaces exposed to, respectively, electrolyte or non-electrolyte solutes. When flows containing different solute concentrations merge in a junction, local solute concentration gradients can drive diffusioosmotic flow due to electrokinetic, steric, and other interactions between the solute molecules and solid surfaces. We demonstrate a microfluidic system consisting of a long, narrow pore connecting two large side channels in which solute concentration gradients drive diffusioosmosis within the pore, resulting in predictable fluid velocity/pressure and solute profiles. Furthermore, we present analytical results and a methodology to determine the zeta potential or interaction length scale for the pore surfaces based on the solute concentrations in the main side channels, the flow rate in the pore, and the pressure drop across the pore. We apply this method to the experimental data of Lee et al. to predict the zeta potentials of their system, and we use 3D numerical simulations to validate the theory and showmore »
This content will become publicly available on June 17, 2023
GradTac: Spatio-Temporal Gradient Based Tactile Sensing
Tactile sensing for robotics is achieved through a variety of mechanisms, including magnetic, optical-tactile, and conductive fluid. Currently, the fluid-based sensors have struck the right balance of anthropomorphic sizes and shapes and accuracy of tactile response measurement. However, this design is plagued by a low Signal to Noise Ratio (SNR) due to the fluid based sensing mechanism “damping” the measurement values that are hard to model. To this end, we present a spatio-temporal gradient representation on the data obtained from fluid-based tactile sensors, which is inspired from neuromorphic principles of event based sensing. We present a novel algorithm (GradTac) that converts discrete data points from spatial tactile sensors into spatio-temporal surfaces and tracks tactile contours across these surfaces. Processing the tactile data using the proposed spatio-temporal domain is robust, makes it less susceptible to the inherent noise from the fluid based sensors, and allows accurate tracking of regions of touch as compared to using the raw data. We successfully evaluate and demonstrate the efficacy of GradTac on many real-world experiments performed using the Shadow Dexterous Hand, equipped with the BioTac SP sensors. Specifically, we use it for tracking tactile input across the sensor’s surface, measuring relative forces, detecting linear and more »
- Publication Date:
- NSF-PAR ID:
- 10376845
- Journal Name:
- Frontiers in Robotics and AI
- Volume:
- 9
- ISSN:
- 2296-9144
- Sponsoring Org:
- National Science Foundation
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