journal article·2022-05-25

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

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

ACS Applied Materials & Interfaces· Volume 14· Issue 20
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Abstract

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.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Analytical Chemistry and Sensors · Electrochemical sensors and biosensors · Electrowetting and Microfluidic Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil

São Carlos Institute of Chemistry, University of São Paulo, São Carlos, São Paulo 09210-580, Brazil

Faculty of Chemical Engineering, University of Campinas, Campinas, São Paulo 13083-970, Brazil

Institute of Chemistry, University of Campinas, Campinas, São Paulo 13083-970, Brazil

John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States

Federal University of ABC, Santo André, São Paulo 09210-580, Brazil

Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil

Funding

Fundação de Amparo à Pesquisa do Estado de São Paulo

References (51)

Show source-provided references
1. 10.1038/s41587-019-0045-y10.1038/s41587-019-0045-y ↗
2. 10.1021/acs.chemrev.8b0057310.1021/acs.chemrev.8b00573 ↗
3. 10.1021/acssensors.1c0113310.1021/acssensors.1c01133 ↗
4. 10.1002/adma.20200776410.1002/adma.202007764 ↗
5. 10.3389/fsufs.2018.0008710.3389/fsufs.2018.00087 ↗
6. 10.1002/admt.20170022310.1002/admt.201700223 ↗
7. 10.1038/s41528-018-0039-810.1038/s41528-018-0039-8 ↗
8. 10.1021/acsomega.9b0103510.1021/acsomega.9b01035 ↗
9. 10.1126/sciadv.aaw046310.1126/sciadv.aaw0463 ↗
10. 10.1126/sciadv.abc329610.1126/sciadv.abc3296 ↗
11. Dubey, S. K.; Pandey, A.; Sangwan, R. S. Current Developments in Biotechnology and Bioengineering. Crop Modification, Nutrition, and Food Production: Abiotic Stress in the Production of Food Grains and Methods to Alleviate the Impact of Stress, 1st ed. Elsevier: Amsterdam, The Netherlands, 2017; pp 215–240.10.1016/B978-0-444-63661-4.00010-4 ↗
12. Taiz L. · 2002
13. 10.1016/j.jmb.2021.16711810.1016/j.jmb.2021.167118 ↗
14. Lima R. S. · 2021
15. 10.1039/D1GC00843A10.1039/D1GC00843A ↗
16. 10.1021/acs.chemrev.0c0133510.1021/acs.chemrev.0c01335 ↗
17. 10.1021/acssensors.1c0130210.1021/acssensors.1c01302 ↗
18. Ustinova, E. Oilseeds and Products Annual. https://apps.fas.usda.gov/newgainapi/api/Report/DownloadReportByFileName?fileName=Oilseeds%20and%20Products%20Annual_Brasilia_Brazil_04-01-2021 (accessed 10-09-2021).
19. 10.2135/cropsci1971.0011183X001100060051x10.2135/cropsci1971.0011183X001100060051x ↗
20. 10.2134/agronj1981.00021962007300030034x10.2134/agronj1981.00021962007300030034x ↗
21. 10.1088/2515-7639/ab077b10.1088/2515-7639/ab077b ↗
22. 10.1021/acsami.9b1453010.1021/acsami.9b14530 ↗
23. 10.1088/2515-7639/ab084b10.1088/2515-7639/ab084b ↗
24. 10.1016/j.snb.2019.12748210.1016/j.snb.2019.127482 ↗
25. 10.1021/acssensors.0c0059910.1021/acssensors.0c00599 ↗
26. 10.1016/j.fuel.2020.11907210.1016/j.fuel.2020.119072 ↗
27. 10.1021/acsami.1c0787410.1021/acsami.1c07874 ↗
28. 10.1073/pnas.202039811810.1073/pnas.2020398118 ↗
29. 10.1002/aelm.20190082610.1002/aelm.201900826 ↗
30. 10.1002/bip.2347210.1002/bip.23472 ↗
31. 10.1117/12.259649610.1117/12.2596496 ↗
32. 10.1038/s41598-021-02928-210.1038/s41598-021-02928-2 ↗
33. 10.1002/smll.20110263510.1002/smll.201102635 ↗
34. 10.1016/j.jclepro.2020.12162410.1016/j.jclepro.2020.121624 ↗
35. 10.1002/aps3.1137510.1002/aps3.11375 ↗
36. Bard A. L. · 2001
37. 10.5194/jsss-6-303-201710.5194/jsss-6-303-2017 ↗
38. 10.1021/acsami.1c1877810.1021/acsami.1c18778 ↗
39. 10.1126/sciadv.150113610.1126/sciadv.1501136 ↗
40. 10.1145/1073204.107325110.1145/1073204.1073251 ↗
41. 10.1108/SR-08-2013-71910.1108/SR-08-2013-719 ↗
42. 10.1016/j.bios.2017.03.01610.1016/j.bios.2017.03.016 ↗
43. 10.1007/s10570-018-1893-110.1007/s10570-018-1893-1 ↗
44. 10.1021/acsami.8b1616710.1021/acsami.8b16167 ↗
45. 10.1016/j.jiec.2020.04.01310.1016/j.jiec.2020.04.013 ↗
46. 10.1016/j.jelechem.2021.11524410.1016/j.jelechem.2021.115244 ↗
47. 10.1021/acssuschemeng.6b0078310.1021/acssuschemeng.6b00783 ↗
48. 10.1021/acssuschemeng.7b0254010.1021/acssuschemeng.7b02540 ↗
49. 10.1038/s41477-020-00808-710.1038/s41477-020-00808-7 ↗
50. 10.1016/j.aca.2021.33840310.1016/j.aca.2021.338403 ↗
51. Gonçalves S. P. C. · 2019 · 26510.1016/B978-0-12-814829-7.00007-0 ↗