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.

Disposable silicon-based all-in-one micro-qPCR for rapid on-site detection of pathogens.

Rapid screening and low-cost diagnosis play a crucial role in choosing the correct course of intervention when dealing with highly infectious pathogens. This is especially important if the disease-causing agent has no effective treatment, such as the novel coronavirus SARS-CoV-2, and shows no or similar symptoms to other common infections. Here, we report a disposable silicon-based integrated Point-of-Need transducer (TriSilix) for real-time quantitative detection of pathogen-specific sequences of nucleic acids. TriSilix can be produced at wafer-scale in a standard laboratory (37 chips of 10 × 10 × 0.65 mm in size can be produced in 7 h, costing ~0.35 USD per device). We are able to quantitatively detect a 563 bp fragment of genomic DNA of Mycobacterium avium subspecies paratuberculosis through real-time PCR with a limit-of-detection of 20 fg, equivalent to a single bacterium, at the 35th cycle. Using TriSilix, we also detect the cDNA from SARS-CoV-2 (1 pg) with high specificity against SARS-CoV (2003).

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.