Point-of-use sensors and machine learning enable low-cost determination of soil nitrogen

Grell, Max; Barandun, Giandrin; Asfour, Tarek; Kasimatis, Michael; Collins, Alex; Wang, Jieni; Güder, Firat

doi:10.1038/s43016-021-00416-4

Cited by 35 publications

(18 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The process to fabricate 3D metal electrodes on NC membranes comprised four steps: (i) Transfer of the 3-electrode design to the NC membranes, using a permanent fabric marker and laser-cut Cu mask; (ii) Drop-casting of the Si precursor ink; (iii) Metallization of the silicon patterns into Au, assisted by HF solution; (iv) Fabrication of hydrophobic barriers by wax printing to separate the electrode contacts from the detection area. 48,49 The Si precursor ink consisted of Si particles (1 g), carboxymethyl cellulose (CMC) and DI water (10 mL), based on previous ink formulations developed in our group. The Si-ink patterns deposited on the NC membranes were then autocatalytically metallized in a bath containing AuCl3 in a dilute aqueous solution of hydrofluoric acid (HF).…”

Section: Methodsmentioning

confidence: 99%

Facilitating Electrochemical Lateral Flow Assay using NFC-Enabled Potentiostat and Nitrocellulose-based Metal Electrodes

Gonzalez‐Macia

Liu

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

Rapid detection of pathogens at the point-of-need is crucial for preventing the spread of human, animal and plant diseases which can have devastating consequences both on the lives and livelihood of billions of people. Colorimetric, lateral flow assays consisting of a nitrocellulose membrane, are the preferred format today for low-cost on-site detection of pathogens. This assay format has, however, historically suffered from poor analytical performance and is not compatible with digital technologies. In this work, we report the development of a new class of digital diagnostics platform for precision point-of-need testing. This new versatile platform consists of two important innovations: i) A wireless and batteryless, microcontroller-based, low-cost Near Field Communication (NFC)-enabled potentiostat that brings high performance electroanalytical techniques (cyclic voltammetry, chronoamperometry, square wave voltammetry) to the field. The NFC-potentiostat can be operated with a mobile app by minimally trained users; ii) A new approach for producing nitrocellulose membranes with integrated electrodes that facilitate high performance electrochemical detection at the point-of-need. We produced an integrated system housed in a 3D-printed phone case and demonstrated its used for the detection of Maize Mosaic Virus (MMV), a plant pathogen, as a proof-of-concept application.

show abstract

Section: Methodsmentioning

confidence: 99%

Facilitating Electrochemical Lateral Flow Assay using NFC-Enabled Potentiostat and Nitrocellulose-based Metal Electrodes

Gonzalez‐Macia

Liu

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Agricultural activity is a major contributor to environmental pollution, especially the overuse of soil additives to support growth of crops: Grell and co-workers developed a paper-based printed gas sensor to measure soil ammonium (NH 4 + ) on a point-of-use basis. 179 Their sensing system comprised a disposable cartridge that contained a cellulose paper-based substrate with printed carbon electrodes based on the work by Barandun et al 55 The soil solution (sample) was added to the cartridge and the pH was increased to 14 by adding sodium hydroxide to the sample. Increasing the pH shifts the equilibrium from NH 4 + toward NH 3(aq) and eventually NH 3(g) , which is detected by the printed gas sensor.…”

Section: Environmentmentioning

confidence: 99%

Challenges and Opportunities for Printed Electrical Gas Sensors

et al. 2022

Self Cite

View full text Add to dashboard Cite

Printed electrical gas sensors are a low-cost, lightweight, low-power, and potentially disposable alternative to gas sensors manufactured using conventional methods such as photolithography, etching, and chemical vapor deposition. The growing interest in Internet-of-Things, smart homes, wearable devices, and point-of-need sensors has been the main driver fueling the development of new classes of printed electrical gas sensors. In this Perspective, we provide an insight into the current research related to printed electrical gas sensors including materials, methods of fabrication, and applications in monitoring food quality, air quality, diagnosis of diseases, and detection of hazardous gases. We further describe the challenges and future opportunities for this emerging technology.

show abstract

“…Так же химические анализы требуют много времени, средств и сложны в эксплуатации. Поэтому создание альтернативных методов быстрого определения содержание азота в почве перед посевом, имеет важное значение для сельскохозяйственных угодий [8].…”

Section: «агроэкоинфо» ==============================================...unclassified

Using Machine Learning Techniques to Predict Nitrate Nitrogen in Soi

Kizimova¹,

Riksen²,

Maksimovich³

et al. 2022

АЭИ

View full text Add to dashboard Cite

This paper presents multinomial logistic regression and neural network models that allow predicting and thereby quickly determining the content of nitrate nitrogen in the 0-40 cm soil layer before sowing. To train the models, we used the data of a long-term multifactorial field experiment of the Siberian Research Institute of Agriculture and Chemistry of the Siberian Federal Scientific Research Center of the Russian Academy of Sciences for 2009-2018, established in 1981 on leached chernozem in the OS "Elitnaya" - a branch of the Siberian Federal Scientific Research Center of the Russian Academy of Sciences. Taking into account the features of the statistical sample (observation data and analyses), the main predictors of the models that affect the content of nitrate nitrogen in the soil (target indicator) are determined, they are presented qualitative (predecessor, tillage) and quantitative (weather conditions and the content of productive moisture before sowing in the layer 0-100 cm) by factors with appropriate gradations. The models showed a fairly high reliability when verified on empirical data and can be used as a tool for forecasting. The quality of the developed multinomial logistic regression model was assessed using the coefficient of determination, which was 78% according to the Nagelkerke measure, and 72% according to the Coxsei Snell measure. To determine the predictive ability of the neural network, an ROC analysis was carried out, which showed that the area under the ROC curve for each category of the target indicator was close to 1, which indicates a high predictive power of this method. A comparative assessment of the predictive capabilities of the trained models was carried out. The overall proportion of correct predictions for multinomial logistic regression is 80.6%, in the neural network model 89.5%. Keywords: MULTINOMIAL LOGISTIC REGRESSION, NEURAL NETWORK, NITRATE NITROGEN, SOIL

show abstract

Point-of-use sensors and machine learning enable low-cost determination of soil nitrogen

Cited by 35 publications

References 41 publications

Facilitating Electrochemical Lateral Flow Assay using NFC-Enabled Potentiostat and Nitrocellulose-based Metal Electrodes

Facilitating Electrochemical Lateral Flow Assay using NFC-Enabled Potentiostat and Nitrocellulose-based Metal Electrodes

Challenges and Opportunities for Printed Electrical Gas Sensors

Using Machine Learning Techniques to Predict Nitrate Nitrogen in Soi

Contact Info

Product

Resources

About