Modern agriculture requires rapid, affordable tools to monitor crops and soils directly in the field. Cellulose, the structural polymer of plants, is emerging as a versatile foundation for lightweight, biodegradable sensors that measure nutrients, moisture, stress, and disease without laboratory infrastructure. Spanning simple paper assays to flexible wearables, these platforms enable distributed, real-time insight into plant and soil health. As materials engineering converges with digital connectivity, cellulose-based sensors could accelerate the transition toward more data-driven, adaptive, and sustainable agricultural systems.

Blood sampling, despite its historical significance in clinical diagnostics, poses challenges, such as invasiveness, infection risks, and limited temporal fidelity for continuous monitoring. In contrast, exhaled breath offers a noninvasive, pain-free, and continuous sampling method, carrying biochemical information through volatile compounds like ammonia (NH3). NH3 in exhaled breath, influenced by kidney function, emerges as a promising biomarker for renal health assessment, particularly in resource-limited settings lacking extensive healthcare infrastructure. Current analytical methods for breath NH3, though effective, often face practical limitations. In this work, we introduce a low-cost, internet-connected, paper-based wearable device for measuring exhaled NH3, designed for early detection of kidney dysfunction at the point of need. The device, which attaches to disposable face masks, utilizes an array of disposable paper-based sensors to detect NH3 with the readout being changes in electrical impedance that correlate with the concentration of NH3. The sensor array is housed in a biodegradable plastic enclosure to mitigate high relative humidity issues in breath analysis. We validated our technology using a laboratory setup and human subjects who consumed ammonium chloride-containing candy to simulate elevated breath NH3. Our wearable sensor offers a promising solution for rapid, point-of-need kidney dysfunction screening, particularly valuable in resource-limited settings. This approach has potential applications beyond kidney health monitoring, including chemical industry safety and environmental sensing, paving the way for accessible, continuous health monitoring.

Blood sampling, despite its historical significance in clinical diagnostics, poses challenges such as invasiveness, infection risks, and limited temporal fidelity for continuous monitoring. In contrast, exhaled breath offers a non-invasive, pain-free, and continuous sampling method, carrying biochemical information through volatile compounds like ammonia (NH3). NH3 in exhaled breath, influenced by kidney function, emerges as a promising biomarker for renal health assessment, particularly in resource-limited settings lacking extensive healthcare infrastructure. Current analytical methods for breath ammonia, though effective, often face practical limitations. In this work, we introduce a low-cost, internet-connected, paper-based wearable device for measuring exhaled ammonia, designed for early detection of kidney dysfunction at the point-of-need. The device, which attaches to disposable facemasks, utilizes a disposable paper-based sensor array housed in a biodegradable plastic enclosure to mitigate high relative humidity (RH) issues in breath analysis. We validated our technology using a laboratory setup and human subjects who consumed ammonium chloride-containing candy to simulate elevated breath ammonia. Our wearable sensor offers a promising solution for rapid, point-of-need kidney dysfunction screening, particularly valuable in resource-limited settings. This approach has potential applications beyond kidney health monitoring, including chemical industry safety and environmental sensing, paving the way for accessible, continuous health monitoring. ### Competing Interest Statement The authors have declared no competing interest.

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 NH 4 + and NO 3 − ) is not performed regularly. Here we demonstrate that point-of-use measurements of NH 4 + , combined with soil conductivity, pH, easily accessible weather and timing data, allow instantaneous prediction of levels of NO 3 − in soil ( R 2 = 0.70) using a machine learning model. A long short-term memory recurrent neural network model can also be used to predict levels of NH 4 + and NO 3 − up to 12 days into the future from a single measurement at day one, with R^2_NH_4^+ = 0.60 and R^2_NO_3^- = 0.70 , for unseen weather conditions. Our machine-learning-based approach eliminates the need for dedicated instruments to determine the levels of NO 3 − 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.

Rapid screening and low-cost diagnosis play a crucial role in choosing the correct course of intervention e.g., drug therapy, quarantine, no action etc. 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 (the pathogen causing COVID-19), and shows no or similar symptoms to other common infections. We report a silicon-based integrated Point-of-Need (PoN) transducer (TriSilix) that can chemically-amplify and detect pathogen-specific sequences of nucleic acids (NA) quantitatively in real-time. Unlike other silicon-based technologies, TriSilix can be produced at wafer-scale in a standard laboratory; we have developed a series of methodologies based on metal-assisted chemical (wet) etching, electroplating, thermal bonding and laser-cutting to enable a cleanroom-free low-cost fabrication that does not require processing in an advanced semiconductor foundry. TriSilix is, therefore, resilient to disruptions in the global supply chain as the devices can be produced anywhere in the world. To create an ultra-low-cost device, the architecture proposed exploits the intrinsic properties of silicon and integrates three modes of operation in a single chip: i) electrical (Joule) heater, ii) temperature sensor ( i.e. thermistor) with a negative temperature coefficient that can provide the precise temperature of the sample solution during reaction and iii) electrochemical sensor for detecting target NA. Using TriSilix, the sample solution can be maintained at a single, specific temperature (needed for isothermal amplification of NA such as Recombinase Polymerase Amplification (RPA) or cycled between different temperatures (with a precision of ±1.3°C) for Polymerase Chain Reaction (PCR) while the exact concentration of amplicons is measured quantitatively and in real-time electrochemically. A single 4-inch Si wafer yields 37 TriSilix chips of 10×10×0.65 mm in size and can be produced in 7 hours, costing ~US $0.35 per device. The system is operated digitally, portable and low power – capable of running up to 35 tests with a 4000 mAh battery (a typical battery capacity of a modern smartphone). We were able to quantitatively detect a 563-bp fragment (Insertion Sequence IS 900 ) of the genomic DNA of M. avium subsp. paratuberculosis (extracted from cultured field samples) through PCR in real-time with a Limit-of-Detection of 20 fg, equivalent to a single bacterium, at the 30th cycle. Using TriSilix, we also detected the cDNA from SARS-CoV-2 (1 pg), through PCR, with high specificity against SARS-CoV (2003).

The dependence of charge carrier mobility on semiconductor channel thickness in field-effect transistors is a universal phenomenon that has been studied extensively for various families of materials. Surprisingly, analogous studies involving metal oxide semiconductors are relatively scarce. Here, spray-deposited In(2)O(3)layers are employed as the model semiconductor system to study the impact of layer thickness on quantum confinement and electron transport along the transistor channel. The results reveal an exponential increase of the in-plane electron mobility (mu(e)) with increasing In(2)O(3)thickness up to approximate to 10 nm, beyond which it plateaus at a maximum value of approximate to 35 cm(2)V(-1)s(-1). Optical spectroscopy measurements performed on In(2)O(3)layers reveal the emergence of quantum confinement for thickness <10 nm, which coincides with the thickness that mu(e)starts deteriorating. By combining two- and four-probe field-effect mobility measurements with high-resolution atomic force microscopy, it is shown that the reduction in mu(e)is attributed primarily to surface scattering. The study provides important guidelines for the design of next generation metal oxide thin-film transistors.

A Correction to this paper has been published: https://doi.org/10.1038/s41467-020-20396-6

Overfertilization with nitrogen fertilizers has damaged the environment and health of soil; yields are declining, while the population continues to rise. Soil is a complex, living organism which is constantly evolving, physically, chemically and biologically. Standard laboratory testing of soil to determine the levels of nitrogen (mainly NH 4 + and NO 3 − ) is infrequent as it is expensive and slow and levels of nitrogen vary on short timescales. Current testing practices, therefore, are not useful to guide fertilization. We demonstrate that Point-of-Use (PoU) measurements of NH 4 + , when combined with soil conductivity, pH, easily accessible weather (in this study, we simulated weather in the laboratory) and timing data (i.e. days passed since fertilization), allow instantaneous prediction of levels of NO 3 − in soil with of R 2 =0.70 using a machine learning (ML) model (the use of higher-precision laboratory measurements instead of PoU measurements increase R 2 to 0.87 for the same model). We also show that a long short-term memory recurrent neural network model can be used to predict levels of NH 4 + and NO 3 − up to 12 days into the future from a single measurement at day one, with R 2 NH4+ = 0.64 and R 2 NO3- = 0.70, for unseen weather conditions. To measure NH 4 + in soil at the PoU easily and inexpensively, we also developed a new sensor that uses chemically functionalized near ‘zero-cost’ paper-based electrical gas sensors. This new technology can detect the concentration of NH 4 + in soil down to 3±1ppm (R 2 =0.85). Gas-phase sensing provides a robust method of sensing NH 4 + due to the reduced complexity of the gas-phase sample. Our machine learning-based approach eliminates the need of using dedicated, expensive sensing instruments to determine the levels of NO 3 − in soil which is difficult to measure reliably with inexpensive technologies; furthermore, crucial nitrogenous soil nutrients can be determined and predicted with enough accuracy to forecast the impact of climate on fertilization planning, and tune timing for crop requirements, reducing overfertilization while improving crop yields.

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.

In article number 1804798, Max Grell, Firat Guder, and co-workers describe a method for depositing a wide range of metals inside fabrics which they call Si ink-enabled autocatalytic metallization. Without requiring surface modification, the method provides access to devices as diverse as antennas, batteries, biosensors, or Raman spectroscopy substrates. Future research might bring many more applications for this highly adaptable method.

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 which 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.

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.

The growth mechanism of indium oxide (In2O3) layers processed via spray pyrolysis of an aqueous precursor solution in the temperature range of 100–300 °C and the impact on their electron transporting properties are studied. Analysis of the droplet impingement sites on the substrate's surface as a function of its temperature reveals that Leidenfrost effect dominated boiling plays a crucial role in the growth of smooth, continuous, and highly crystalline In2O3 layers via a vapor phase‐like process. By careful optimization of the precursor formulation, deposition conditions, and choice of substrate, this effect is exploited and ultrathin and exceptionally smooth layers of In2O3 are grown over large area substrates at temperatures as low as 252 °C. Thin‐film transistors (TFTs) fabricated using these optimized In2O3 layers exhibit superior electron transport characteristics with the electron mobility reaching up to 40 cm2 V−1 s−1, a value amongst the highest reported to date for solution‐processed In2O3 TFTs. The present work contributes enormously to the basic understanding of spray pyrolysis and highlights its tremendous potential for large‐volume manufacturing of high‐performance metal oxide thin‐film transistor electronics.