Machine Vision Based Measurement of Dynamic Contact Angles in Microchannel Flows

When characterizing flows in miniaturized channels, the determination of the dynamic contact angle is important. By measuring the dynamic contact angle, the flow properties of the flowing liquid and the effect of material properties on the flow can be characterized. A machine vision based system to measure the contact angle of front or rear menisci of a moving liquid plug is described in this article. In this research, transparent flow channels fabricated on thermoplastic polymer and sealed with an adhesive tape are used. The transparency of the channels enables image based monitoring and measurement of flow variables, including the dynamic contact angle. It is shown that the dynamic angle can be measured from a liquid flow in a channel using the image based measurement system. An image processing algorithm has been developed in a MATLAB environment. Images are taken using a CCD camera and the channels are illuminated using a custom made ring light. Two fitting methods, a circle and two parabolas, are experimented and the results are compared in the measurement of the dynamic contact angles.

An in vitro demonstration of a passive,acoustic metamaterial as a temperature sensor with mK resolution for implantable applications

Wireless medical sensors typically utilize electromagnetic coupling or ultrasound for energy transfer and sensor interrogation.Energy transfer and management is a complex aspect that often limits the applicability of implantable sensor systems.In this work,we report a new passive temperature sensing scheme based on an acoustic metamaterial made of silicon embedded in a polydimethylsiloxane matrix.Compared to other approaches,this concept is implemented without additional electrical components in situ or the need for a customized receiving unit.A standard ultrasonic transducer is used for this demonstration to directly excite and collect the reflected signal.The metamaterial resonates at a frequency close to a typical medical value(5 MHz)and exhibits a high-quality factor.Combining the design features of the metamaterial with the high-temperature sensitivity of the polydimethylsiloxane matrix,we achieve a temperature resolution of 30 mK.This value is below the current standard resolution required in infrared thermometry for monitoring postoperative complications(0.1 K).We fabricated,simulated,in vitro tested,and compared three acoustic sensor designs in the 29-43℃(~302-316 K)temperature range.With this concept,we demonstrate how our passive metamaterial sensor can open the way toward new zero-power smart medical implant concepts based on acoustic interrogation.

Electrophoretic deposition of graphene-based materials:A review of materials and their applications

Recently,graphene-based materials have been successfully fabricated by the electrophoretic deposition(EPD)technique and exhibited various extraordinary properties.Here,research progress of the field of graphene-based materials prepared by the EPD process in recent 5 years is reviewed,including graphene films,graphene/non-metal composites,graphene/metal-based nanoparticles composites,graphene/polymer composites.We also summarize the experimental deposition conditions and the applications of the deposited graphene-based materials that have been reported.It can be concluded that EPD is a simple and reliable manipulation technique and promises a bright future for the production of graphenebased materials in the field of advanced nanocomposite materials.Finally the current issues and outlook of the development direction of EPD in future are also proposed.

Reaction injection molding of hydrophilicin-hydrophobic femtolitre-well arrays

Patterning of micro-and nanoscale topologies and surface properties of polymer devices is of particular importance for a broad range of life science applications,including cell-adhesion assays and highly sensitive bioassays.The manufacturing of such devices necessitates cumbersome multiple-step fabrication procedures and results in surface properties which degrade over time.This critically hinders their wide-spread dissemination.Here,we simultaneously mold and surface energy pattern microstructures in off-stoichiometric thiol-ene by area-selective monomer selfassembly in a rapid micro-reaction injection molding cycle.We replicated arrays of 1,843,650 hydrophilic-inhydrophobic femtolitre-wells with long-term stable surface properties and magnetically trapped beads with 75%and 87.2%efficiency in single-and multiple-seeding events,respectively.These results form the basis for ultrasensitive digital biosensors,specifically,and for the fabrication of medical devices and life science research tools,generally.

High-speed identification of suspended carbon nanotubes using Raman spectroscopy and deep learning

The identification of nanomaterials with the properties required for energy-efficient electronic systems is usually a tedious human task.A workflow to rapidly localize and characterize nanomaterials at the various stages of their integration into large-scale fabrication processes is essential for quality control and,ultimately,their industrial adoption.In this work,we develop a high-throughput approach to rapidly identify suspended carbon nanotubes(CNTs)by using high-speed Raman imaging and deep learning analysis.Even for Raman spectra with extremely low signal-to-noise ratios(SNRs)of 0.9,we achieve a classification accuracy that exceeds 90%,while it reaches 98%for an SNR of 2.2.By applying a threshold on the output of the softmax layer of an optimized convolutional neural network(CNN),we further increase the accuracy of the classification.Moreover,we propose an optimized Raman scanning strategy to minimize the acquisition time while simultaneously identifying the position,amount,and metallicity of CNTs on each sample.Our approach can readily be extended to other types of nanomaterials and has the potential to be integrated into a production line to monitor the quality and properties of nanomaterials during fabrication.

A customizable,low-power,wireless,embedded sensing platform for resistive nanoscale sensors

Customizable,portable,battery-operated,wireless platforms for interfacing high-sensitivity nanoscale sensors are a means to improve spatiotemporal measurement coverage of physical parameters.Such a platform can enable the expansion of IoT for environmental and lifestyle applications.Here we report a platform capable of acquiring currents ranging from 1.5 nA to 7.2μA full-scale with 20-bit resolution and variable sampling rates of up to 3.125 kSPS.In addition,it features a bipolar voltage programmable in the range of-10 V to+5 V with a 3.65 mV resolution.A Finite State Machine steers the system by executing a set of embedded functions.The FSM allows for dynamic,customized adjustments of the nanosensor bias,including elevated bias schemes for self-heating,measurement range,bandwidth,sampling rate,and measurement time intervals.Furthermore,it enables data logging on external memory(SD card)and data transmission over a Bluetooth low energy connection.The average power consumption of the platform is 64.5 mW for a measurement protocol of three samples per second,including a BLE advertisement of a 0 dBm transmission power.A state-of-the-art(SoA)application of the platform performance using a CNT nanosensor,exposed to NO_(2) gas concentrations from 200 ppb down to 1 ppb,has been demonstrated.Although sensor signals are measured for NO_(2) concentrations of 1 ppb,the 3σlimit of detection(LOD)of 23 ppb is determined(1σ:7 ppb)in slope detection mode,including the sensor signal variations in repeated measurements.The platform’s wide current range and high versatility make it suitable for signal acquisition from resistive nanosensors such as silicon nanowires,carbon nanotubes,graphene,and other 2D materials.Along with its overall low power consumption,the proposed platform is highly suitable for various sensing applications within the context of IoT.