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.

No abstract

No abstract

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.