Rapid ultrasensitive and specific BNP biosensor with LED readout.

Biosensing for diagnostics has risen rapidly in popularity over the past decades. With the discovery of new nanomaterials and morphologies, sensitivity is being constantly improved enough for reliable detection of trace biomarkers in human samples, like serum or sweat. This precision has enabled detailed research on the efficacy of biosensors. However, current biosensors suffer from reduced speed of operation. To make better use of this sensitivity, the development of a conductometric biosensor with in-situ use of an Laser Emitting Device (LED) display can provide rapid determination of sample results, steadily pushing biosensors toward more clinical, point-of-care (POC) applications. In this research, a simple LED was used for facile optical determination and visual output of an ultrasensitive bio-signal amplification circuit was made to interface with a B-type Natriuretic Peptide (BNP) biosensor. Tuning circuit gain enables an elegant method for adjustable separation of concentrations into 3 discrete categories: sub-threshold, analog, and saturation regions. These regions corresponded to 0 < [C] < 500 pg/mL (25, 100, 250 pg/mL, LED off), 500 < [C] < 1000 pg/mL (LED varying intensity), and 1000 pg/mL < [C] (LED full intensity). System efficacy was tested using human blood serum samples from University of Pittsburgh Medical Center patients, which were able to be accurately detected and sorted for rapid low cost and power. determination without need for complex digital elements. Additional specificity testing suggests insignificant impact of non-target biomarkers.

Induced bioresistance via BNP detection for machine learning-based risk assessment.

Machine Learning (ML) is a powerful tool for big data analysis that shows substantial potential in the field of healthcare. Individual patient data can be inundative, but its value can be extracted by ML's predictive power and ability to find trends. A great area of interest is early diagnosis and disease management strategies for cardiovascular disease (CVD), the leading cause of death in the world. Treatment is often inhibited by analysis delays, but rapid testing and determination can help improve frequency for real time monitoring. In this research, an ML algorithm was developed in conjunction with a flexible BNP sensor to create a quick diagnostic tool. The sensor was fabricated as an ion-selective field effect transistor (ISFET) in order to be able to quickly gather large amounts of electrical data from a sample. Artifical samples were tested to characterize the sensors using linear sweep voltammetry, and the resulting data was utilized as the initial training set for the ML algorithm, an implementation of quadratic discriminant analysis (QDA) written in MATLAB. Human blood serum samples from 30 University of Pittsburgh Medical Center (UPMC) patients were tested to evaluate the effective sorting power of the algorithm, yielding 95% power in addition to ultra fast data collection and determination.

Effects of Surface Modifications to Single and Multilayer Graphene Temperature Coefficient of Resistance.

Interfacial effects on single-layer graphene (SLG) or multilayer graphene (MLG) properties greatly affect device performance. Thus, the effect of the interface on the temperature coefficient of resistance (TCR) on SLG and MLG due to surface-deposited core-shell metallic nanoparticles (MNPs) and various substrates was experimentally investigated. Observed substrates included glass, SiO2, and Si3N4. We show that these modifications can be used to strongly influence SLG interface effects, thus increasing the TCR up to a 0.456% per K resistance change when in contact with the SiO2 substrate at the bottom surface and MNPs on the top surface. However, these surface interactions are muted in MLG due to the screening effect of nonsuperficial layers, only achieving a -0.0998% per K resistance change in contact with the bottom Si3N4 substrate and the top MNPs. We also demonstrate contrary thermal sensitivity responses between SLG and MLG after the addition of MNP to the surface.