Newton Truong

We present a compact mobile phone platform for rapid, quantitative biomolecular detection. This system consists of an embedded circuit for signal processing and data analysis, and disposable microfluidic chips for fluidic handling and biosensing. Capillary flow is employed for sample loading, processing, and pumping to enhance operational portability and simplicity. Graphical step-by-step instructions displayed on the phone assists the operator through the detection process. After the completion of each measurement, the results are displayed on the screen for immediate assessment and the data is automatically saved to the phone's memory for future analysis and transmission. Validation of this device was carried out by detecting Plasmodium falciparum histidine-rich protein 2 (PfHRP2), an important biomarker for malaria, with a lower limit of detection of 16 ng mL(-1) in human serum. The simple detection process can be carried out with two loading steps and takes 15 min to complete each measurement. Due to its compact size and high performance, this device offers immense potential as a widely accessible, point-of-care diagnostic platform, especially in remote and rural areas. In addition to its impact on global healthcare, this technology is relevant to other important applications including food safety, environmental monitoring and biosecurity.

A compilation of our latest design, simulation, and evaluation efforts regarding the construction of a biomimetic electrostatic imaging platform capable of visualizing submerged objects in a salt-water environment. In our previous work, the position accuracy was no better than 10cm. The imager described here achieves 2.5cm accuracy on the center line and degrades to 5cm accuracy at the peripheral channels.

Electrolocation is a method of sensing and navigating around nearby objects by probing the environment with a series of electrical pulses and measuring the response. This method, found in several species of electric fish, has the potential for faster response times and reduced scanning overheads when compared to traditional underwater location methods such as sonar. This work describes a biology-inspired model and process method for emulating this sensing modality. Previous work in this area uses parametric models, requiring the learning of many time-varying physical parameters. This limits the usability and adaptability of these methods. Instead of relying on complex physical models, we propose in this paper, a dynamic non-parametric model for underwater electrolocation which can be identified using existing system identification techniques. We further describe ways in which results from adaptive filtering and machine learning can be used to process incoming sensory information for electrolocation. We demonstrate the performance of the proposed improvements using an experimental aquatic testbed. Our experiments shows a 3 × increase in the detection range.

We discuss the development and design of a Biomimetic Electrostatic Imaging (BEI) element for use in a future multichannel imaging instrument. Inspiration for electrostatic sensing is provided by the diverse species of fish that use passive and induced electric fields for hunting, defense, localization, and communication. Designing an electrostatic sensor is not without its challenges. Hardware filter and amplifier design are analyzed since the high impedance signals require high gain and steps must be taken to minimize noise, maximizing signal to noise ratio.

This paper explores a technique to exploit Biomimetic Electrostatic Imaging (BEI) for the purposes of short-range high-speed detection and tracking of submerged obstacles based on their conductivity deviation from the background ocean environment. BEI uses conductivity and Coulomb's law rather than electromagnetic or acoustic (SONAR) principles to provide more rapid imaging at substantially reduced output powers making the technique perfect for Uncrewed Underwater Vehicles (UUV) seeking to align for docking, avoid obstacles while traversing, perform relative station keeping (formation management), or track/follow a target object. It is demonstrated to work in real-time against the type of short-range targets that pose a collision threat.