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Registro Completo |
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Biblioteca(s): |
Embrapa Instrumentação. |
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Data corrente: |
19/10/2021 |
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Data da última atualização: |
09/06/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MERCANTE, L. A.; ANDRE, R. S.; FACURE, M. H. M.; FUGIKAWA-SANTOS, L.; CORREA, D. S. |
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Afiliação: |
DANIEL SOUZA CORREA, CNPDIA. |
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Título: |
Design of a bioelectronic tongue for glucose monitoring using zinc oxide nanofibers and graphene derivatives. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Sensors and Actuators Reports, v. 3, 100050, 2021. |
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ISSN: |
2666-0539 |
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DOI: |
https://doi.org/10.1016/j.snr.2021.100050 |
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Idioma: |
Inglês |
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Conteúdo: |
Monitoring glucose levels is critical for diabetes management and might be a key step in the development of individualized treatment strategies. In this scenario, tracking salivary glucose has been recognized as a promising strategy due to its merits of ease sampling and non-invasive nature. In this paper, we report on the development of an electrical impedance-based biosensor array to distinguish glucose at different concentrations in saliva. The enzymatic biosensors were made of gold interdigitated electrodes coated with pristine electrospun zinc oxide nanofibers (NFZ) and NFZ combined with graphene-based nanomaterials (i.e., reduced graphene oxide - rGO and graphene quantum dots - GQDs), on which a layer of glucose oxidase (GOx) enzyme was adsorbed. Electrical impedance measurements indicate that the NFZ-GQDs@GOx and NFZ-rGO@GOx platforms presented good linear relationship with glucose concentration in the range of 0.1 to 6 mM. The highest sensitivity was reached for NFZ-rGO@GOx with a detection limit (LOD) of 14 μM, while the LOD was 32 μM for NFZ-GQDs@GOx. Both biosensors were also capable of detecting glucose in artificial saliva using aliquots of 10 μL, with recovery between 87.3 and 106.8%. Furthermore, the three sensing units (NFZ@GOx, NFZ-rGO@GOx and NFZ-GQDs@GOx) were employed to build a bioelectronic tongue. Using Principal Component Analysis (PCA) technique to project the electrical impedance data of all sensing units allowed the discrimination of the different glucose concentrations and interferents. This study reveals the applicability of the developed bioelectronic tongue as noninvasive glucose sensors, which approach could also be pottentially adapted to detect other disease biomarkers present in saliva MenosMonitoring glucose levels is critical for diabetes management and might be a key step in the development of individualized treatment strategies. In this scenario, tracking salivary glucose has been recognized as a promising strategy due to its merits of ease sampling and non-invasive nature. In this paper, we report on the development of an electrical impedance-based biosensor array to distinguish glucose at different concentrations in saliva. The enzymatic biosensors were made of gold interdigitated electrodes coated with pristine electrospun zinc oxide nanofibers (NFZ) and NFZ combined with graphene-based nanomaterials (i.e., reduced graphene oxide - rGO and graphene quantum dots - GQDs), on which a layer of glucose oxidase (GOx) enzyme was adsorbed. Electrical impedance measurements indicate that the NFZ-GQDs@GOx and NFZ-rGO@GOx platforms presented good linear relationship with glucose concentration in the range of 0.1 to 6 mM. The highest sensitivity was reached for NFZ-rGO@GOx with a detection limit (LOD) of 14 μM, while the LOD was 32 μM for NFZ-GQDs@GOx. Both biosensors were also capable of detecting glucose in artificial saliva using aliquots of 10 μL, with recovery between 87.3 and 106.8%. Furthermore, the three sensing units (NFZ@GOx, NFZ-rGO@GOx and NFZ-GQDs@GOx) were employed to build a bioelectronic tongue. Using Principal Component Analysis (PCA) technique to project the electrical impedance data of all sensing units allowed the discrimination of the different ... Mostrar Tudo |
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Palavras-Chave: |
Electrospinning; Electrospinning Electrospun nanofibers; Reduced graphene oxide. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02503naa a2200229 a 4500
001 2135416
005 2022-06-09
008 2021 bl uuuu u00u1 u #d
022 $a2666-0539
024 7 $ahttps://doi.org/10.1016/j.snr.2021.100050$2DOI
100 1 $aMERCANTE, L. A.
245 $aDesign of a bioelectronic tongue for glucose monitoring using zinc oxide nanofibers and graphene derivatives.$h[electronic resource]
260 $c2021
520 $aMonitoring glucose levels is critical for diabetes management and might be a key step in the development of individualized treatment strategies. In this scenario, tracking salivary glucose has been recognized as a promising strategy due to its merits of ease sampling and non-invasive nature. In this paper, we report on the development of an electrical impedance-based biosensor array to distinguish glucose at different concentrations in saliva. The enzymatic biosensors were made of gold interdigitated electrodes coated with pristine electrospun zinc oxide nanofibers (NFZ) and NFZ combined with graphene-based nanomaterials (i.e., reduced graphene oxide - rGO and graphene quantum dots - GQDs), on which a layer of glucose oxidase (GOx) enzyme was adsorbed. Electrical impedance measurements indicate that the NFZ-GQDs@GOx and NFZ-rGO@GOx platforms presented good linear relationship with glucose concentration in the range of 0.1 to 6 mM. The highest sensitivity was reached for NFZ-rGO@GOx with a detection limit (LOD) of 14 μM, while the LOD was 32 μM for NFZ-GQDs@GOx. Both biosensors were also capable of detecting glucose in artificial saliva using aliquots of 10 μL, with recovery between 87.3 and 106.8%. Furthermore, the three sensing units (NFZ@GOx, NFZ-rGO@GOx and NFZ-GQDs@GOx) were employed to build a bioelectronic tongue. Using Principal Component Analysis (PCA) technique to project the electrical impedance data of all sensing units allowed the discrimination of the different glucose concentrations and interferents. This study reveals the applicability of the developed bioelectronic tongue as noninvasive glucose sensors, which approach could also be pottentially adapted to detect other disease biomarkers present in saliva
653 $aElectrospinning
653 $aElectrospinning Electrospun nanofibers
653 $aReduced graphene oxide
700 1 $aANDRE, R. S.
700 1 $aFACURE, M. H. M.
700 1 $aFUGIKAWA-SANTOS, L.
700 1 $aCORREA, D. S.
773 $tSensors and Actuators Reports$gv. 3, 100050, 2021.
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Registro original: |
Embrapa Instrumentação (CNPDIA) |
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Biblioteca |
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Registro |
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Registro Completo
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Biblioteca(s): |
Embrapa Trigo. |
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Data corrente: |
11/02/2021 |
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Data da última atualização: |
11/02/2021 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
RODIGHERI, G.; FONTANA, D. C.; SCHAPARINI, L. P.; DALMAGO, G. A.; SCHIRMBECK, J. |
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Afiliação: |
GRAQZIELI RODIGHERI, CEPSRM, Post-Graduation Program of Remote Sensing, 91501970, Rio Grande do Sul, Brasil – grazielirodigheri@gmail.com; D. C. FONTANA, Dept. of Agronomy, Post-Graduation Program in Phytotechnics, 91540000, Rio Grande do Sul, Brasil – dfontana@ufrgs.br; laura_pigatto@yahoo.com.br; LAURA PIGATTO SCHAPARINI, Dept. of Agronomy, Post-Graduation Program in Phytotechnics, 91540000, Rio Grande do Sul, Brasil – dfontana@ufrgs.br; laura_pigatto@yahoo.com.br; GENEI ANTONIO DALMAGO, CNPT; J. SCHIRMBECK, UNIVATES, 95914014, Rio Grande do Sul, Brasil – schirmbeck.j@gmail.com. |
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Título: |
Net primary productivity and dry matter in soybean cultivation utilizing datas of ndvi multi-sensors. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W12-2020, IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS 2020), 22?26 March 2020, Santiago, Chile, 2020. |
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Idioma: |
Inglês |
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Conteúdo: |
Net Primary Productivity (NPP) is an important indicator of vegetation growth status and ecosystems health. NPP can be estimated through remote sensing data, using vegetation indices such as NDVI. However, this index may show systematic differences when using several orbital sensors. Therefore, the objective of this paper was to compare the NDVI data obtained from different sensors and evaluate the impact over the soybean biomass and NPP estimates. NDVI data were recorded from 4 sensors, one on the field and others 3 orbitals sensors (Landsat 8/OLI, Sentinel 2/MSI and Terra/MODIS). Measured data on the field, Photosynthetically Active Radiation (PAR) and Dry Matter (DM), were used to modeling the total DM and also NPP. The NDVI data from different sensors showed differences throughout the cycle, but compared to the reference data there was a correlation greater than 0.84. The DM presented a correlation of 0.91 with the field measured MS data while the NPP presented differences of up to 240 gC/m²/month from in relation to the reference data. Therefore, NDVI obtained from multiple sensors can be used to estimate NPP for surface analysis. However, for more consistent evaluations, a function of adjustment between the NDVI sensor data and NDVI reference data is required, so that the NPP estimation be better correlated to the actual data. |
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Palavras-Chave: |
Google Earth Engine; PAR. |
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Thesaurus NAL: |
Agriculture; Remote sensing. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/221162/1/Rodigheri-2020-p22.pdf
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Marc: |
LEADER 02164nam a2200205 a 4500
001 2129996
005 2021-02-11
008 2020 bl uuuu u00u1 u #d
100 1 $aRODIGHERI, G.
245 $aNet primary productivity and dry matter in soybean cultivation utilizing datas of ndvi multi-sensors.$h[electronic resource]
260 $aIn: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W12-2020, IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS 2020), 22?26 March 2020, Santiago, Chile$c2020
520 $aNet Primary Productivity (NPP) is an important indicator of vegetation growth status and ecosystems health. NPP can be estimated through remote sensing data, using vegetation indices such as NDVI. However, this index may show systematic differences when using several orbital sensors. Therefore, the objective of this paper was to compare the NDVI data obtained from different sensors and evaluate the impact over the soybean biomass and NPP estimates. NDVI data were recorded from 4 sensors, one on the field and others 3 orbitals sensors (Landsat 8/OLI, Sentinel 2/MSI and Terra/MODIS). Measured data on the field, Photosynthetically Active Radiation (PAR) and Dry Matter (DM), were used to modeling the total DM and also NPP. The NDVI data from different sensors showed differences throughout the cycle, but compared to the reference data there was a correlation greater than 0.84. The DM presented a correlation of 0.91 with the field measured MS data while the NPP presented differences of up to 240 gC/m²/month from in relation to the reference data. Therefore, NDVI obtained from multiple sensors can be used to estimate NPP for surface analysis. However, for more consistent evaluations, a function of adjustment between the NDVI sensor data and NDVI reference data is required, so that the NPP estimation be better correlated to the actual data.
650 $aAgriculture
650 $aRemote sensing
653 $aGoogle Earth Engine
653 $aPAR
700 1 $aFONTANA, D. C.
700 1 $aSCHAPARINI, L. P.
700 1 $aDALMAGO, G. A.
700 1 $aSCHIRMBECK, J.
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