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

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Barbosa, Julia A. ; Freitas, Vitoria M. S. ; Vidotto, Lourenco H. B. ; Schleder, Gabriel R. ; de Oliveira, Ricardo A. G. ; da Rocha, Jaqueline F. ; Kubota, Lauro T. ; Vieira, Luis C. S. ; Tolentino, Helio C. N. ; Neckel, Itamar T. ; Gobbi, Angelo L. ; Santhiago, Murilo ; Lim, Renato S.

• Tipo de documento: Artigo Científico
• Fonte: ACS APPLIED MATERIALS & INTERFACES; v. 14, n. 20, p. 13-pg., 2022-03-21.

Resumo

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 k Omega %(-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 degrees 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. (AU)

• Processo FAPESP: 20/09102-4 - Capacitores vestíveis em folhas e machine learning para análise em tempo real de múltiplos parâmetros fisiológicos de plantas
• Beneficiário: Júlia Adorno Barbosa
• Modalidade de apoio: Bolsas no Brasil - Doutorado Direto