The monitoring of toxic inorganic gases and volatile organic compounds has brought the development of field-deployable, sensitive, and scalable sensors into focus. Here, we attempted to meet these requirements by using concurrently microhole-structured meshes as (i) a membrane for the gas diffusion extraction of an analyte from a donor sample and (ii) an electrode for the sensitive electrochemical determination of this target with the receptor electrolyte at rest. We used two types of meshes with complementary benefits, i.e., Ni mesh fabricated by robust, scalable, and well-established methods for manufacturing specific designs and stainless steel wire mesh (SSWM), which is commercially available at a low cost. The diffusion of gas (from a donor) was conducted in headspace mode, thus minimizing issues related to mesh fouling. When compared with the conventional polytetrafluoroethylene (PTFE) membrane, both the meshes (40 μm hole diameter) led to a higher amount of vapor collected into the electrolyte for subsequent detection. This inedited fashion produced a kind of reverse diffusion of the analyte dissolved into the electrolyte (receptor), i.e., from the electrode to bulk, which further enabled highly sensitive analyses. Using Ni mesh coated with Ni­(OH)2 nanoparticles, the limit of detection reached for ethanol was 24-fold lower than the data attained by a platform with a PTFE membrane and placement of the electrode into electrolyte bulk. This system was applied in the determination of ethanol in complex samples related to the production of ethanol biofuel. It is noteworthy that a simple equation fitted by machine learning was able to provide accurate assays (accuracies from 97 to 102%) by overcoming matrix effect-related interferences on detection performance. Furthermore, preliminary measurements demonstrated the successful coating of the meshes with gold films as an alternative raw electrode material and the monitoring of HCl utilizing Au-coated SSWMs. These strategies extend the applicability of the platform that may help to develop valuable volatile sensing solutions.

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.

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