Low‐cost biosensors that can rapidly and widely monitor plant nutritional levels will be critical for better understanding plant health and improving precision agriculture decision making. In this work, fully printed ion‐selective organic electrochemical transistors (OECTs) that can detect macronutrient concentrations in whole plant sap are described. Potassium, the most concentrated cation in the majority of plants, is selected as the target analyte as it plays a critical role in plant growth and development. The ion sensors demonstrate high current (170 µA dec−1) and voltage (99 mV dec−1) sensitivity, and a low limit of detection (10 × 10−6 m). These OECT biosensors can be used to determine potassium concentration in raw sap and sap‐like aqueous environments demonstrating a log‐linear response within the expected physiological range of cations in plants. The performance of these printed devices enables their use in high‐throughput plant health monitoring in agricultural and ecological applications.

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We use a random forest (RF) model to predict the critical cooling rate (R C) for glass formation of various alloys from features of their constituent elements. The RF model was trained on a database that integrates multiple sources of direct and indirect R C data for metallic glasses to expand the directly measured R C database of less than 100 values to a training set of over 2000 values. The model error on 5-fold cross-validation (CV) is 0.66 orders of magnitude in K/s. The error on leave-out-one-group CV on alloy system groups is 0.59 log units in K/s when the target alloy constituents appear more than 500 times in training data. Using this model, we make predictions for the set of compositions with melt-spun glasses in the database and for the full set of quaternary alloys that have constituents which appear more than 500 times in training data. These predictions identify a number of potential new bulk metallic glass systems for future study, but the model is most useful for the identification of alloy systems likely to contain good glass formers rather than detailed discovery of bulk glass composition regions within known glassy systems.

In this work, we present a novel method for integrating printed electronic materials with biocompatible cryogels to form stable, implantable hydrogel-based bioelectronic devices that show stable long-term operation inside plant tissue. The gels can be customized to provide various electronic functionalities, including electrodes and organic electrochemical transistors (OECT). These inkjet printed cryogel-based devices exhibit high electrical conductivity for embedded conductive polymer traces (up to 350 S/cm), high transconductance for OECTs (in the mS range), and high capacitance in capacitive structures (up to 4.2 mF.g-1). These devices also show high stretchability (up to 330% strain), and self-healing properties. The biocompatible functionalized gel-based electrodes and transistors were successfully implanted in plant tissue. Ionic activity in tomato plants was collected for over two months with minimal scar tissue formation observed over this time, making these cryogel-based electronic devices excellent candidates for continuous, in-situ monitoring of plant and environmental status and health.