In this work, we report the design, fabrication, and characterization of novel biochemical sensors consisting of nanoscale grooves and slits milled in a metal film to form two-arm, three-beam, planar plasmonic interferometers. By integrating thousands of plasmonic interferometers per square millimeter with a microfluidic system, we demonstrate a sensor able to detect physiological concentrations of glucose in water over a broad wavelength range (400-800 nm). A wavelength sensitivity between 370 and 630 nm/RIU (RIU, refractive index units), a relative intensity change between ~10(3) and 10(6) %/RIU, and a resolution of ~3 × 10(-7) in refractive index change were experimentally measured using typical sensing volumes as low as 20 fL. These results show that multispectral plasmonic interferometry is a promising approach for the development of high-throughput, real-time, and extremely compact biochemical sensors.

The recently discovered bactericidal properties of nanostructures on wings of insects such as cicadas and dragonflies have inspired the development of similar nanostructured surfaces for antibacterial applications. Since most antibacterial applications require nanostructures covering a considerable amount of area, a practical fabrication method needs to be cost-effective and scalable. However, most reported nanofabrication methods require either expensive equipment or a high temperature process, limiting cost efficiency and scalability. Here, we report a simple, fast, low-cost, and scalable antibacterial surface nanofabrication methodology. Our method is based on metal-assisted chemical etching that only requires etching a single crystal silicon substrate in a mixture of silver nitrate and hydrofluoric acid for several minutes. We experimentally studied the effects of etching time on the morphology of the silicon nanospikes and the bactericidal properties of the resulting surface. We discovered that 6 minutes of etching results in a surface containing silicon nanospikes with optimal geometry. The bactericidal properties of the silicon nanospikes were supported by bacterial plating results, fluorescence images, and scanning electron microscopy images.

A non-invasive method for the detection of glucose is sought by millions of diabetic patients to improve personal management of blood glucose over a lifetime. In this work, the synergistic advantage of combining plasmonic interferometry with an enzyme-driven dye assay yields an optical sensor capable of detecting glucose in saliva with high sensitivity and selectivity. The sensor, coined a "plasmonic cuvette," is built around a nano-scale groove-slit-groove (GSG) plasmonic interferometer coupled to an Amplex-red/Glucose-oxidase/Glucose (AR/GOx/Glucose) assay. The proposed device is highly sensitive, with a measured intensity change of 1.7 × 10 5 %/m (i.e., one order of magnitude more sensitive than without assay) and highly specific for glucose sensing in picoliter volumes, across the physiological range of glucose concentrations found in human saliva (20-240 μm). Real-time glucose monitoring in saliva is achieved by performing a detailed study of the underlying enzyme-driven reactions to determine and tune the effective rate constants in order to reduce the overall assay reaction time to ∼2 min. The results reported suggest that by opportunely choosing the appropriate dye chemistry, a plasmonic cuvette can be turned into a general, realtime sensing scheme for detection of any molecular target, with high sensitivity and selectivity, within extremely low volumes of biological fluid (down to femtoliters). Hereby, we present the results on glucose detection in artificial saliva as a notable and clinically relevant case study.

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This paper highlights the design philosophy and architecture of the Health Guardian, a platform developed by the IBM Digital Health team to accelerate discoveries of new digital biomarkers and development of digital health technologies. The Health Guardian allows for rapid translation of artificial intelligence (AI) research into cloud-based microservices that can be tested with data from clinical cohorts to understand disease and enable early prevention. The platform can be connected to mobile applications, wearables, or Internet of things (IoT) devices to collect health-related data into a secure database. When the analytics are created, the researchers can containerize and deploy their code on the cloud using pre-defined templates, and validate the models using the data collected from one or more sensing devices. The Health Guardian platform currently supports timeseries, text, audio, and video inputs with 70+ analytic capabilities and is used for non-commercial scientific research. We provide an example of the Alzheimer's disease (AD) assessment microservice which uses AI methods to extract linguistic features from audio recordings to evaluate an individual's mini-mental state, the likelihood of having AD, and to predict the onset of AD before turning the age of 85. Today, IBM research teams across the globe use the Health Guardian internally as a test bed for early-stage research ideas, and externally with collaborators to support and enhance AI model development and clinical study efforts.

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