Scalable and green formation of graphitic nanolayers produces highly conductive pyrolyzed paper toward sensitive electrochemical sensors.

While pyrolyzed paper (PP) is a green and abundant material that can provide functionalized electrodes with wide detection windows for a plethora of targets, it poses long-standing challenges against sensing assays such as poor electrical conductivity, with resistivities generally higher than 200.0 mΩ cm (e.g., gold and silver show resistivities 1000-fold lower, ∼0.2 mΩ cm). In this regard, the fundamental hypothesis that drives this work is whether a scalable, cost-effective, and eco-friendly strategy is capable of significantly reducing the resistivity of PP electrodes toward the development of sensitive electrochemical sensors, whether faradaic or capacitive. We address this hypothesis by simply annealing PP under an isopropanol atmosphere for 1 h, reaching resistivities as low as 7 mΩ cm. Specifically, the annealing of PP at 800 or 1000 °C under isopropanol vapor leads to the formation of a highly graphitic nanolayer (∼15 nm) on the PP surface, boosting conductivity as the delocalization of π electrons stemming from carbon sp2 is favored. The reduction of carbonyl groups and the deposition of dehydrated isopropanol during the annealing process are hypothesized herein as the dominant PP graphitization mechanisms. Electrochemical analyses demonstrated the capability of the annealed PP to increase the charge-transfer kinetics, with the optimum heterogeneous standard rate constant being roughly 3.6 × 10-3 cm s-1. This value is larger than the constants reported for other carbon electrodes and indium tin oxide. Furthermore, freestanding fingers of the annealed PP were prototyped using a knife plotter to fabricate impedimetric on-leaf electrodes. These wearable sensors ensured the real-time and in situ monitoring of the loss of water content from soy leaves, outperforming non-annealed electrodes in terms of reproducibility and sensitivity. Such an application is of pivotal importance for precision agriculture and development of agricultural inputs. This work addresses the foundations for the achievement of conductive PP in a scalable, low-cost, simple, and eco-friendly way, i.e. without producing any liquid chemical waste, providing new opportunities to translate PP-based sensitive electrochemical devices into practical use.

Machine learning toward high-performance electrochemical sensors.

The so-coined fourth paradigm in science has reached the sensing area, with the use of machine learning (ML) toward data-driven improvements in sensitivity, reproducibility, and accuracy, along with the determination of multiple targets from a single measurement using multi-output regression models. Particularly, the use of supervised ML models trained on large data sets produced by electrical and electrochemical bio/sensors has emerged as an impacting trend in the literature by allowing accurate analyses even in the presence of usual issues such as electrode fouling, poor signal-to-noise ratio, chemical interferences, and matrix effects. In this trend article, apart from an outlook for the coming years, we present examples from the literature that demonstrate how helpful ML algorithms can be for dispensing the adoption of experimental methods to address the aforesaid interfering issues, ultimately contributing to translate testing technologies into on-site, practical, and daily applications.

Biocompatible Wearable Electrodes on Leaves toward the On-Site Monitoring of Water Loss from Plants.

Impedimetric wearable sensors are a promising strategy for determining the loss of water content (LWC) from leaves because they can afford on-site and nondestructive quantification of cellular water from a single measurement. Because the water content is a key marker of leaf health, monitoring of the LWC can lend key insights into daily practice in precision agriculture, toxicity studies, and the development of agricultural inputs. Ongoing challenges with this monitoring are the on-leaf adhesion, compatibility, scalability, and reproducibility of the electrodes, especially when subjected to long-term measurements. This paper introduces a set of sensing material, technological, and data processing solutions that overwhelm such obstacles. Mass-production-suitable electrodes consisting of stand-alone Ni films obtained by well-established microfabrication methods or ecofriendly pyrolyzed paper enabled reproducible determination of the LWC from soy leaves with optimized sensibilities of 27.0 (Ni) and 17.5 kΩ %-1 (paper). The freestanding design of the Ni electrodes was further key to delivering high on-leaf adhesion and long-term compatibility. Their impedances remained unchanged under the action of wind at velocities of up to 2.00 m s-1, whereas X-ray nanoprobe fluorescence assays allowed us to confirm the Ni sensor compatibility by the monitoring of the soy leaf health in an electrode-exposed area. Both electrodes operated through direct transfer of the conductive materials on hairy soy leaves using an ordinary adhesive tape. We used a hand-held and low-power potentiostat with wireless connection to a smartphone to determine the LWC over 24 h. Impressively, a machine-learning model was able to convert the sensing responses into a simple mathematical equation that gauged the impairments on the water content at two temperatures (30 and 20 °C) with reduced root-mean-square errors (0.1% up to 0.3%). These data suggest broad applicability of the platform by enabling direct determination of the LWC from leaves even at variable temperatures. Overall, our findings may help to pave the way for translating "sense-act" technologies into practice toward the on-site and remote investigation of plant drought stress. These platforms can provide key information for aiding efficient data-driven management and guiding decision-making steps.