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Biolayer interferometry (BLI) is a well-established laboratory technique for studying biomolecular interactions important for applications such as drug development. Currently, there are interesting opportunities for expanding the use of BLI in other fields, including the development of rapid diagnostic tools. To date, there are no detailed frameworks for implementing BLI in target-recognition studies that are pivotal for developing point-of-need biosensors. Here, we attempt to bridge these domains by providing a framework that connects output(s) of molecular interaction studies with key performance indicators used in the development of point-of-need biosensors. First, we briefly review the governing theory for protein-ligand interactions, and we then summarize the approach for real-time kinetic quantification using various techniques. The 2020 PRISMA guideline was used for all governing theory reviews and meta-analyses. Using the information from the meta-analysis, we introduce an experimental framework for connecting outcomes from BLI experiments (KD, kon, koff) with electrochemical (capacitive) biosensor design. As a first step in the development of a larger framework, we specifically focus on mapping BLI outcomes to five biosensor key performance indicators (sensitivity, selectivity, response time, hysteresis, operating range). The applicability of our framework was demonstrated in a study of case based on published literature related to SARS-CoV-2 spike protein to show the development of a capacitive biosensor based on truncated angiotensin-converting enzyme 2 (ACE2) as the receptor. The case study focuses on non-specific binding and selectivity as research goals. The proposed framework proved to be an important first step toward modeling/simulation efforts that map molecular interactions to sensor design.

Foodborne pathogens are a major concern for public health. We demonstrate for the first time a partially automated sensing system for rapid (~17 min), label-free impedimetric detection of Escherichia coli spp. in food samples (vegetable broth) and hydroponic media (aeroponic lettuce system) based on temperature-responsive poly(N-isopropylacrylamide) (PNIPAAm) nanobrushes. This proof of concept (PoC) for the Sense-Analyze-Respond-Actuate (SARA) paradigm uses a biomimetic nanostructure that is analyzed and actuated with a smartphone. The bio-inspired soft material and sensing mechanism is inspired by binary symbiotic systems found in nature, where low concentrations of bacteria are captured from complex matrices by brush actuation driven by concentration gradients at the tissue surface. To mimic this natural actuation system, carbon-metal nanohybrid sensors were fabricated as the transducer layer, and coated with PNIPAAm nanobrushes. The most effective coating and actuation protocol for E. coli detection at various temperatures above/below the critical solution temperature of PNIPAAm was determined using a series of electrochemical experiments. After analyzing nanobrush actuation in stagnant media, we developed a flow through system using a series of pumps that are triggered by electrochemical events at the surface of the biosensor. SARA PoC may be viewed as a cyber-physical system that actuates nanomaterials using smartphone-based electroanalytical testing of samples. This study demonstrates thermal actuation of polymer nanobrushes to detect (sense) bacteria using a cyber-physical systems (CPS) approach. This PoC may catalyze the development of smart sensors capable of actuation at the nanoscale (stimulus-response polymer) and macroscale (non-microfluidic pumping).

In this manuscript, we discuss relevant socioeconomic factors for developing and implementing Sensor aNAlytic Point Solutions (SNAPS) as point-of-care tools to serve impoverished communities. The distinct economic, environmental, cultural, and ethical paradigms that affect economically disadvantaged users add complexity to the process of technology development and deployment beyond the science and engineering issues. We begin by contextualizing the environmental burden of disease in select low-income regions around the world, including environmental hazards at work, home, and the broader community environment, where SNAPS may be helpful in the prevention and mitigation of human exposure to harmful biological vectors and chemical agents. We offer examples of SNAPS designed for economically disadvantaged users, specifically for supporting decision-making in cases of tuberculosis (TB) infection and mercury exposure. We follow-up by discussing the economic challenges that are involved in the phased implementation of diagnostic tools in low-income markets and describe a micropayment-based systems-as-a-service approach (pay-a-penny-per-use--PAPPU), which may be catalytic for the adoption of low-end, low-margin, low-research, and the development SNAPS. Finally, we provide some insights into the social and ethical considerations for the assimilation of SNAPS to improve health outcomes in marginalized communities. Keywords: sensor analytic point solutions (SNAPS); environmental health; poverty; pay-a-penny-per-use (PAPPU); public health, USDA National Institute of Food and Agriculture (grant no. 2018-67016-27578)

In this review, we discuss the role of sensor analytics point solutions (SNAPS), a reduced complexity machine-assisted decision support tool. We summarize the approaches used for mobile phone-based chemical/biological sensors, including general hardware and software requirements for signal transduction and acquisition. We introduce SNAPS, part of a platform approach to converge sensor data and analytics. The platform is designed to consist of a portfolio of modular tools which may lend itself to dynamic composability by enabling context-specific selection of relevant units, resulting in case-based working modules. SNAPS is an element of this platform where data analytics, statistical characterization and algorithms may be delivered to the data either via embedded systems in devices, or sourced, in near real-time, from mist, fog or cloud computing resources. Convergence of the physical systems with the cyber components paves the path for SNAPS to progress to higher levels of artificial reasoning tools (ART) and emerge as data-informed decision support, as a service for general societal needs. Proof of concept examples of SNAPS are demonstrated both for quantitative data and qualitative data, each operated using a mobile device (smartphone or tablet) for data acquisition and analytics. We discuss the challenges and opportunities for SNAPS, centered around the value to users/stakeholders and the key performance indicators users may find helpful, for these types of machine-assisted tools. Keywords: sensor; smart systems; data analytics; cyber-physical systems; artificial reasoning tools; ART; drag and drop analytics; DADA; sensor-analytics point solutions; SNAPS; sense-analyze-respond-actuate; SARA; machine-assisted tools; MAT; machine-assisted platform; MAP; knowledge graphs; trans-disciplinary convergence, Agriculture and Food Research Initiative Competitive Grant (grant no. 2018-67016-27578), NSF (project no. 1805512), NSF (project no. 1511953)