NFC-enabled potentiostat and nitrocellulose-based metal electrodes for electrochemical lateral flow assay.

Rapid detection of pathogens at the point-of-need is crucial for preventing the spread of human, animal and plant diseases which can have devastating consequences both on the lives and livelihood of billions of people. Colorimetric, lateral flow assays consisting of a nitrocellulose membrane, are the preferred format today for low-cost on-site detection of pathogens. This assay format has, however, historically suffered from poor analytical performance and is not compatible with digital technologies. In this work, we report the development of a new class of digital diagnostics platform for precision point-of-need testing. This new versatile platform consists of two important innovations: i) A wireless and batteryless, microcontroller-based, low-cost Near Field Communication (NFC)-enabled potentiostat that brings high performance electroanalytical techniques (cyclic voltammetry, chronoamperometry, square wave voltammetry) to the field. The NFC-potentiostat can be operated with a mobile app by minimally trained users; ii) A new approach for producing nitrocellulose membranes with integrated electrodes that facilitate high performance electrochemical detection at the point-of-need. We produced an integrated system housed in a 3D-printed phone case and demonstrated its use for the detection of Maize Mosaic Virus (MMV), a plant pathogen, as a proof-of-concept application.

Challenges and Opportunities for Printed Electrical Gas Sensors.

Printed electrical gas sensors are a low-cost, lightweight, low-power, and potentially disposable alternative to gas sensors manufactured using conventional methods such as photolithography, etching, and chemical vapor deposition. The growing interest in Internet-of-Things, smart homes, wearable devices, and point-of-need sensors has been the main driver fueling the development of new classes of printed electrical gas sensors. In this Perspective, we provide an insight into the current research related to printed electrical gas sensors including materials, methods of fabrication, and applications in monitoring food quality, air quality, diagnosis of diseases, and detection of hazardous gases. We further describe the challenges and future opportunities for this emerging technology.

Self-powered ultrasensitive and highly stretchable temperature-strain sensing composite yarns.

With the emergence of stretchable/wearable devices, functions, such as sensing, energy storage/harvesting, and electrical conduction, should ideally be carried out by a single material, while retaining its ability to withstand large elastic deformations, to create compact, functionally-integrated and autonomous systems. A new class of trimodal, stretchable yarn-based transducer formed by coating commercially available Lycra® yarns with PEDOT:PSS is presented. The material developed can sense strain (first mode), and temperature (second mode) and can power itself thermoelectrically (third mode), eliminating the need for an external power-supply. The yarns were extensively characterized and obtained an ultrahigh (gauge factor ∼3.6 × 105, at 10-20% strain) and tunable (up to about 2 orders of magnitude) strain sensitivity together with a very high strain-at-break point (up to ∼1000%). These PEDOT:PSS-Lycra yarns also exhibited stable thermoelectric behavior (Seebeck coefficient of 15 µV K-1), which was exploited both for temperature sensing and self-powering (∼0.5 µW, for a 10-couple module at ΔT ∼ 95 K). The produced material has potential to be interfaced with microcontroller-based systems to create internet-enabled, internet-of-things type devices in a variety of form factors.

Point-of-use sensors and machine learning enable low-cost determination of soil nitrogen.

Overfertilization with nitrogen fertilizers has damaged the environment and health of soil, but standard laboratory testing of soil to determine the levels of nitrogen (mainly NH4+ and NO3-) is not performed regularly. Here we demonstrate that point-of-use measurements of NH4+, combined with soil conductivity, pH, easily accessible weather and timing data, allow instantaneous prediction of levels of NO3- in soil (R2 = 0.70) using a machine learning model. A long short-term memory recurrent neural network model can also be used to predict levels of NH4+ and NO3- up to 12 days into the future from a single measurement at day one, with [Formula: see text] and [Formula: see text], for unseen weather conditions. Our machine-learning-based approach eliminates the need for dedicated instruments to determine the levels of NO3- in soil. Nitrogenous soil nutrients can be determined and predicted with enough accuracy to forecast the impact of climate on fertilization planning and to tune timing for crop requirements, reducing overfertilization while improving crop yields.

Monolithic Solder-On Nanoporous Si-Cu Contacts for Stretchable Silicone Composite Sensors.

We report a method of creating solderable, mechanically robust, electrical contacts to interface (soft) silicone-based strain sensors with conventional (hard) solid-state electronics using a nanoporous Si-Cu composite. The Si-based solder-on electrical contact consists of a copper-plated nanoporous Si top surface formed through metal-assisted chemical etching and electroplating and a smooth Si bottom surface that can be covalently bonded onto silicone-based strain sensors through plasma bonding. We investigated the mechanical and electrical properties of the contacts proposed under relevant ranges of mechanical stress for applications in physiological monitoring and rehabilitation. We also produced a series of proof-of-concept devices, including a wearable respiration monitor, leg band for exercise monitoring, and squeeze ball for monitoring rehabilitation of patients with hand injuries or neurological disorders to demonstrate the mechanical robustness and versatility of the technology developed in real-world applications.

Cellulose Fibers Enable Near-Zero-Cost Electrical Sensing of Water-Soluble Gases.

We report an entirely new class of printed electrical gas sensors that are produced at near "zero cost". This technology exploits the intrinsic hygroscopic properties of cellulose fibers within paper; although it feels and looks dry, paper contains substantial amount of moisture, adsorbed from the environment, enabling the use of wet chemical methods for sensing without manually adding water to the substrate. The sensors exhibit high sensitivity to water-soluble gases (e.g., lower limit of detection for NH3 < 200 parts-per-billion) with a fast and reversible response. The sensors show comparable or better performance (especially at high relative humidity) than most commercial ammonia sensors at a fraction of their price (<$0.02 per sensor). We demonstrate that the sensors proposed can be integrated into food packaging to monitor freshness (to reduce food waste and plastic pollution) or implemented into near-field-communication tags to function as wireless, battery-less gas sensors that can be interrogated with smartphones.

Autocatalytic Metallization of Fabrics Using Si Ink, for Biosensors, Batteries and Energy Harvesting.

Commercially available metal inks are mainly designed for planar substrates (for example, polyethylene terephthalate foils or ceramics), and they contain hydrophobic polymer binders that fill the pores in fabrics when printed, thus resulting in hydrophobic electrodes. Here, a low-cost binder-free method for the metallization of woven and nonwoven fabrics is presented that preserves the 3D structure and hydrophilicity of the substrate. Metals such as Au, Ag, and Pt are grown autocatalytically, using metal salts, inside the fibrous network of fabrics at room temperature in a two-step process, with a water-based silicon particle ink acting as precursor. Using this method, (patterned) metallized fabrics are being enabled to be produced with low electrical resistance (less than 3.5 Ω sq-1). In addition to fabrics, the method is also compatible with other 3D hydrophilic substrates such as nitrocellulose membranes. The versatility of this method is demonstrated by producing coil antennas for wireless energy harvesting, Ag-Zn batteries for energy storage, electrochemical biosensors for the detection of DNA/proteins, and as a substrate for optical sensing by surface enhanced Raman spectroscopy. In the future, this method of metallization may pave the way for new classes of high-performance devices using low-cost fabrics.