{"type": "FeatureCollection", "features": [{"id": "10.2139/ssrn.5084742", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:13Z", "type": "Journal Article", "created": "2025-05-25", "title": "ZnO-nanostructured electrochemical sensor for efficient detection of glyphosate in water", "description": "Glyphosate is a widely used broad-spectrum herbicide for controlling grassy weeds, despite having potential health hazards. Herein, we report on a solid-state electrochemical sensor based on ZnO nanoparticles (ZnO NPs) for on-site detection of glyphosate. Accordingly, ZnO NPs was drop-cast on the surface of a disposable screen-printed carbon electrode. Eco-friendly ZnO NPs of only 7 nm crystallite sizes were obtained by green sol-gel synthesis using lemon (Citrus limon) waste aqueous extract as the green reducing and capping/stabilizing agent and Zn nitrate precursor as evidenced by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction and diffuse reflectance. SEM confirmed successful electrode functionalization with the synthesized nanoparticles. Under laboratory conditions in acetate buffer (pH 5), the sensor demonstrated excellent selectivity and sensitivity, with a detection limit of 0.648 \u00b5M, a wide linear detection range (0.5 \u00b5M to 7.5 mM), and a rapid detection time of 30 min. When tested in river water, the sensor achieved a detection limit of 0.96 \u00b5M using differential pulse voltammetry. It also exceptionally tolerated interference from similar organophosphorus compounds and ions commonly found in river water. The excellent detection performance of the sensor was attributed to the strong coordination interactions between Zn atoms and phosphonate/carboxylate groups that are enhanced by a hydrogen bond at acidic pH, as determined by chemical calculations. This disposable sensor offers a cost-effective, efficient, and environmentally friendly solution for monitoring glyphosate in water systems.", "keywords": ["QD71-142", "Environmental water", "Eco-friendly ZnO nanoparticles", "Computational modeling", "Pesticides", "Eco-friendly ZnO nanoparticles;", "[SDV.MP] Life Sciences [q-bio]/Microbiology and Parasitology", "Analytical chemistry", "Sensor"]}, "links": [{"href": "https://doi.org/10.2139/ssrn.5084742"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Talanta%20Open", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.2139/ssrn.5084742", "name": "item", "description": "10.2139/ssrn.5084742", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.2139/ssrn.5084742"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-01T00:00:00Z"}}, {"id": "10.1109/lgrs.2021.3073484", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:18:21Z", "type": "Journal Article", "created": "2021-06-10", "title": "Sentinel-1 Backscatter Assimilation Using Support Vector Regression or the Water Cloud Model at European Soil Moisture Sites", "description": "Sentinel-1 backscatter observations were assimilated into the Global Land Evaporation Amsterdam Model (GLEAM) using an ensemble Kalman filter. As a forward operator, which is required to simulate backscatter from soil moisture and leaf area index (LAI), we evaluated both the traditional water cloud model (WCM) and the support vector regression (SVR). With SVR, a closer fit between backscatter observations and simulations was achieved. The impact on the correlation between modeled and in situ soil moisture measurements was similar when assimilating the Sentinel data using WCM (\u0394 R = +0.037) or SVR (\u0394 R = +0.025).", "keywords": ["Vegetation mapping", "support vector regression (SVR)", "Technology and Engineering", "Data models", "0211 other engineering and technologies", "Computational modeling", "02 engineering and technology", "15. 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Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. This may especially be the case for mechanistic mathematical modeling, and we spend some extra time discussing this. Through a more thorough appreciation and awareness of the power of different kinds of modeling in plant biology, we hope to facilitate interdisciplinary, transformative research.</p>", "keywords": ["Other Physical Sciences and Mathematics", "computational modeling", "0301 basic medicine", "2. 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Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. 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This may especially be the case for mechanistic mathematical modeling, and we spend some extra time discussing this. Through a more thorough appreciation and awareness of the power of different kinds of modeling in plant biology, we hope to facilitate interdisciplinary, transformative research.</p>", "keywords": ["Other Physical Sciences and Mathematics", "computational modeling", "0301 basic medicine", "2. 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