Grand Challenges in Biosensors and Biomolecular Electronics

AI-Based Metamaterial Design

The use of metamaterials in various devices has revolutionized applications in optics, healthcare, acoustics, and power systems. Advancements in these fields demand novel or superior metamaterials that can demonstrate targeted control of electromagnetic, mechanical, and thermal properties of matter. Traditional design systems and methods often require manual manipulations which is time-consuming and resource intensive. The integration of artificial intelligence (AI) in optimizing metamaterial design can be employed to explore variant disciplines and address bottlenecks in design. AI-based metamaterial design can also enable the development of novel metamaterials by optimizing design parameters that cannot be achieved using traditional methods. The application of AI can be leveraged to accelerate the analysis of vast data sets as well as to better utilize limited data sets via generative models. This review covers the transformative impact of AI and AI-based metamaterial design for optics, acoustics, healthcare, and power systems. The current challenges, emerging fields, future directions, and bottlenecks within each domain are discussed.

Development of epistatic YES and AND protein logic gates and their assembly into signalling cascades

The construction and assembly of artificial allosteric protein switches into information and energy processing networks connected to both biological and non-biological systems is a central goal of synthetic biology and bionanotechnology. However, designing protein switches with the desired input, output and performance parameters is challenging. Here we use a range of reporter proteins to demonstrate that their chimeras with duplicated receptor domains produce YES gate protein switches with large (up to 9,000-fold) dynamic ranges and fast (minutes) response rates. In such switches, the epistatic interactions between largely independent synthetic allosteric sites result in an OFF state with minimal background noise. We used YES gate protein switches based on β-lactamase to develop quantitative biosensors of therapeutic drugs and protein biomarkers. Furthermore, we demonstrated the reconfiguration of YES gate switches into AND gate switches controlled by two different inputs, and their assembly into signalling networks regulated at multiple nodes.

Digital Quantification Method for Sensitive Point-of-Care Detection of Salivary Uric Acid Using Smartphone-Assisted μPADs

Uric acid (UA) is an important biomarker for many diseases. A sensitive point-of-care (POC) testing platform is designed for the digital quantification of salivary UA based on a colorimetric reaction on an easy-to-build smartphone-assisted microfluidic paper-based analytical device (SμPAD). UA levels are quantified according to the color intensity of Prussian blue on the SμPAD with the aid of a MATLAB code or a smartphone APP. A color correction method is specifically applied to exclude the light effect. Together with the engineering design of SμPADs, the background calibration function with the APP increases the UA sensitivity by 100-fold to reach 0.1 ppm with a linear range of 0.1-200 ppm. The assay time is less than 10 min. SμPADs demonstrate a correlation of 0.97 with a commercial UA kit for the detection of salivary UA in clinical samples. SμPADs provide a sensitive, fast, affordable, and reliable tool for the noninvasive POC quantification of salivary UA for early diagnosis of abnormal UA level-associated health conditions.