Stochastic Time Response and Ultimate Noise Performance of Adsorption-Based Microfluidic Biosensors

In order to improve the interpretation of measurement results and to achieve the optimal performance of microfluidic biosensors, advanced mathematical models of their time response and noise are needed. The random nature of adsorption–desorption and mass transfer (MT) processes that generate the sen...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ivana Jokić [verfasserIn] Zoran Djurić [verfasserIn] Katarina Radulović [verfasserIn] Miloš Frantlović [verfasserIn] Gradimir V. Milovanović [verfasserIn] Predrag M. Krstajić [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	microfluidic adsorption-based sensor stochastic model adsorption mass transfer ultimate noise performance detection limit

Übergeordnetes Werk:	In: Biosensors - MDPI AG, 2012, 11(2021), 6, p 194
Übergeordnetes Werk:	volume:11 ; year:2021 ; number:6, p 194

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/bios11060194

Katalog-ID:	DOAJ051123827

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ051123827
003	DE-627
005	20240412174427.0
007	cr uuu---uuuuu
008	230227s2021 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.3390/bios11060194 \|2 doi
035			\|a (DE-627)DOAJ051123827
035			\|a (DE-599)DOAJ872febaa3df746229705a1459d4ddcf5
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a TP248.13-248.65
100	0		\|a Ivana Jokić \|e verfasserin \|4 aut
245	1	0	\|a Stochastic Time Response and Ultimate Noise Performance of Adsorption-Based Microfluidic Biosensors
264		1	\|c 2021
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a In order to improve the interpretation of measurement results and to achieve the optimal performance of microfluidic biosensors, advanced mathematical models of their time response and noise are needed. The random nature of adsorption–desorption and mass transfer (MT) processes that generate the sensor response makes the sensor output signal inherently stochastic and necessitates the use of a stochastic approach in sensor response analysis. We present a stochastic model of the sensor time response, which takes into account the coupling of adsorption–desorption and MT processes. It is used for the analysis of response kinetics and ultimate noise performance of protein biosensors. We show that slow MT not only decelerates the response kinetics, but also increases the noise and decreases the sensor’s maximal achievable signal-to-noise ratio, thus degrading the ultimate sensor performance, including the minimal detectable/quantifiable analyte concentration. The results illustrate the significance of the presented model for the correct interpretation of measurement data, for the estimation of sensors’ noise performance metrics important for reliable analyte detection/quantification, as well as for sensor optimization in terms of the lower detection/quantification limit. They are also incentives for the further investigation of the MT influence in nanoscale sensors, as a possible cause of false-negative results in analyte detection experiments.
650		4	\|a microfluidic adsorption-based sensor
650		4	\|a stochastic model
650		4	\|a adsorption
650		4	\|a mass transfer
650		4	\|a ultimate noise performance
650		4	\|a detection limit
653		0	\|a Biotechnology
700	0		\|a Zoran Djurić \|e verfasserin \|4 aut
700	0		\|a Katarina Radulović \|e verfasserin \|4 aut
700	0		\|a Miloš Frantlović \|e verfasserin \|4 aut
700	0		\|a Gradimir V. Milovanović \|e verfasserin \|4 aut
700	0		\|a Predrag M. Krstajić \|e verfasserin \|4 aut
773	0	8	\|i In \|t Biosensors \|d MDPI AG, 2012 \|g 11(2021), 6, p 194 \|w (DE-627)718626451 \|w (DE-600)2662125-3 \|x 20796374 \|7 nnns
773	1	8	\|g volume:11 \|g year:2021 \|g number:6, p 194
856	4	0	\|u https://doi.org/10.3390/bios11060194 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/872febaa3df746229705a1459d4ddcf5 \|z kostenfrei
856	4	0	\|u https://www.mdpi.com/2079-6374/11/6/194 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2079-6374 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 11 \|j 2021 \|e 6, p 194

Indexfelder

author_variant	i j ij z d zd k r kr m f mf g v m gvm p m k pmk
matchkey_str	article:20796374:2021----::tcatcieepnenutmtnieefracoasrtobsd
hierarchy_sort_str	2021
callnumber-subject-code	TP
publishDate	2021
allfields	10.3390/bios11060194 doi (DE-627)DOAJ051123827 (DE-599)DOAJ872febaa3df746229705a1459d4ddcf5 DE-627 ger DE-627 rakwb eng TP248.13-248.65 Ivana Jokić verfasserin aut Stochastic Time Response and Ultimate Noise Performance of Adsorption-Based Microfluidic Biosensors 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to improve the interpretation of measurement results and to achieve the optimal performance of microfluidic biosensors, advanced mathematical models of their time response and noise are needed. The random nature of adsorption–desorption and mass transfer (MT) processes that generate the sensor response makes the sensor output signal inherently stochastic and necessitates the use of a stochastic approach in sensor response analysis. We present a stochastic model of the sensor time response, which takes into account the coupling of adsorption–desorption and MT processes. It is used for the analysis of response kinetics and ultimate noise performance of protein biosensors. We show that slow MT not only decelerates the response kinetics, but also increases the noise and decreases the sensor’s maximal achievable signal-to-noise ratio, thus degrading the ultimate sensor performance, including the minimal detectable/quantifiable analyte concentration. The results illustrate the significance of the presented model for the correct interpretation of measurement data, for the estimation of sensors’ noise performance metrics important for reliable analyte detection/quantification, as well as for sensor optimization in terms of the lower detection/quantification limit. They are also incentives for the further investigation of the MT influence in nanoscale sensors, as a possible cause of false-negative results in analyte detection experiments. microfluidic adsorption-based sensor stochastic model adsorption mass transfer ultimate noise performance detection limit Biotechnology Zoran Djurić verfasserin aut Katarina Radulović verfasserin aut Miloš Frantlović verfasserin aut Gradimir V. Milovanović verfasserin aut Predrag M. Krstajić verfasserin aut In Biosensors MDPI AG, 2012 11(2021), 6, p 194 (DE-627)718626451 (DE-600)2662125-3 20796374 nnns volume:11 year:2021 number:6, p 194 https://doi.org/10.3390/bios11060194 kostenfrei https://doaj.org/article/872febaa3df746229705a1459d4ddcf5 kostenfrei https://www.mdpi.com/2079-6374/11/6/194 kostenfrei https://doaj.org/toc/2079-6374 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 6, p 194
spelling	10.3390/bios11060194 doi (DE-627)DOAJ051123827 (DE-599)DOAJ872febaa3df746229705a1459d4ddcf5 DE-627 ger DE-627 rakwb eng TP248.13-248.65 Ivana Jokić verfasserin aut Stochastic Time Response and Ultimate Noise Performance of Adsorption-Based Microfluidic Biosensors 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to improve the interpretation of measurement results and to achieve the optimal performance of microfluidic biosensors, advanced mathematical models of their time response and noise are needed. The random nature of adsorption–desorption and mass transfer (MT) processes that generate the sensor response makes the sensor output signal inherently stochastic and necessitates the use of a stochastic approach in sensor response analysis. We present a stochastic model of the sensor time response, which takes into account the coupling of adsorption–desorption and MT processes. It is used for the analysis of response kinetics and ultimate noise performance of protein biosensors. We show that slow MT not only decelerates the response kinetics, but also increases the noise and decreases the sensor’s maximal achievable signal-to-noise ratio, thus degrading the ultimate sensor performance, including the minimal detectable/quantifiable analyte concentration. The results illustrate the significance of the presented model for the correct interpretation of measurement data, for the estimation of sensors’ noise performance metrics important for reliable analyte detection/quantification, as well as for sensor optimization in terms of the lower detection/quantification limit. They are also incentives for the further investigation of the MT influence in nanoscale sensors, as a possible cause of false-negative results in analyte detection experiments. microfluidic adsorption-based sensor stochastic model adsorption mass transfer ultimate noise performance detection limit Biotechnology Zoran Djurić verfasserin aut Katarina Radulović verfasserin aut Miloš Frantlović verfasserin aut Gradimir V. Milovanović verfasserin aut Predrag M. Krstajić verfasserin aut In Biosensors MDPI AG, 2012 11(2021), 6, p 194 (DE-627)718626451 (DE-600)2662125-3 20796374 nnns volume:11 year:2021 number:6, p 194 https://doi.org/10.3390/bios11060194 kostenfrei https://doaj.org/article/872febaa3df746229705a1459d4ddcf5 kostenfrei https://www.mdpi.com/2079-6374/11/6/194 kostenfrei https://doaj.org/toc/2079-6374 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 6, p 194
allfields_unstemmed	10.3390/bios11060194 doi (DE-627)DOAJ051123827 (DE-599)DOAJ872febaa3df746229705a1459d4ddcf5 DE-627 ger DE-627 rakwb eng TP248.13-248.65 Ivana Jokić verfasserin aut Stochastic Time Response and Ultimate Noise Performance of Adsorption-Based Microfluidic Biosensors 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to improve the interpretation of measurement results and to achieve the optimal performance of microfluidic biosensors, advanced mathematical models of their time response and noise are needed. The random nature of adsorption–desorption and mass transfer (MT) processes that generate the sensor response makes the sensor output signal inherently stochastic and necessitates the use of a stochastic approach in sensor response analysis. We present a stochastic model of the sensor time response, which takes into account the coupling of adsorption–desorption and MT processes. It is used for the analysis of response kinetics and ultimate noise performance of protein biosensors. We show that slow MT not only decelerates the response kinetics, but also increases the noise and decreases the sensor’s maximal achievable signal-to-noise ratio, thus degrading the ultimate sensor performance, including the minimal detectable/quantifiable analyte concentration. The results illustrate the significance of the presented model for the correct interpretation of measurement data, for the estimation of sensors’ noise performance metrics important for reliable analyte detection/quantification, as well as for sensor optimization in terms of the lower detection/quantification limit. They are also incentives for the further investigation of the MT influence in nanoscale sensors, as a possible cause of false-negative results in analyte detection experiments. microfluidic adsorption-based sensor stochastic model adsorption mass transfer ultimate noise performance detection limit Biotechnology Zoran Djurić verfasserin aut Katarina Radulović verfasserin aut Miloš Frantlović verfasserin aut Gradimir V. Milovanović verfasserin aut Predrag M. Krstajić verfasserin aut In Biosensors MDPI AG, 2012 11(2021), 6, p 194 (DE-627)718626451 (DE-600)2662125-3 20796374 nnns volume:11 year:2021 number:6, p 194 https://doi.org/10.3390/bios11060194 kostenfrei https://doaj.org/article/872febaa3df746229705a1459d4ddcf5 kostenfrei https://www.mdpi.com/2079-6374/11/6/194 kostenfrei https://doaj.org/toc/2079-6374 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 6, p 194
allfieldsGer	10.3390/bios11060194 doi (DE-627)DOAJ051123827 (DE-599)DOAJ872febaa3df746229705a1459d4ddcf5 DE-627 ger DE-627 rakwb eng TP248.13-248.65 Ivana Jokić verfasserin aut Stochastic Time Response and Ultimate Noise Performance of Adsorption-Based Microfluidic Biosensors 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to improve the interpretation of measurement results and to achieve the optimal performance of microfluidic biosensors, advanced mathematical models of their time response and noise are needed. The random nature of adsorption–desorption and mass transfer (MT) processes that generate the sensor response makes the sensor output signal inherently stochastic and necessitates the use of a stochastic approach in sensor response analysis. We present a stochastic model of the sensor time response, which takes into account the coupling of adsorption–desorption and MT processes. It is used for the analysis of response kinetics and ultimate noise performance of protein biosensors. We show that slow MT not only decelerates the response kinetics, but also increases the noise and decreases the sensor’s maximal achievable signal-to-noise ratio, thus degrading the ultimate sensor performance, including the minimal detectable/quantifiable analyte concentration. The results illustrate the significance of the presented model for the correct interpretation of measurement data, for the estimation of sensors’ noise performance metrics important for reliable analyte detection/quantification, as well as for sensor optimization in terms of the lower detection/quantification limit. They are also incentives for the further investigation of the MT influence in nanoscale sensors, as a possible cause of false-negative results in analyte detection experiments. microfluidic adsorption-based sensor stochastic model adsorption mass transfer ultimate noise performance detection limit Biotechnology Zoran Djurić verfasserin aut Katarina Radulović verfasserin aut Miloš Frantlović verfasserin aut Gradimir V. Milovanović verfasserin aut Predrag M. Krstajić verfasserin aut In Biosensors MDPI AG, 2012 11(2021), 6, p 194 (DE-627)718626451 (DE-600)2662125-3 20796374 nnns volume:11 year:2021 number:6, p 194 https://doi.org/10.3390/bios11060194 kostenfrei https://doaj.org/article/872febaa3df746229705a1459d4ddcf5 kostenfrei https://www.mdpi.com/2079-6374/11/6/194 kostenfrei https://doaj.org/toc/2079-6374 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 6, p 194
allfieldsSound	10.3390/bios11060194 doi (DE-627)DOAJ051123827 (DE-599)DOAJ872febaa3df746229705a1459d4ddcf5 DE-627 ger DE-627 rakwb eng TP248.13-248.65 Ivana Jokić verfasserin aut Stochastic Time Response and Ultimate Noise Performance of Adsorption-Based Microfluidic Biosensors 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to improve the interpretation of measurement results and to achieve the optimal performance of microfluidic biosensors, advanced mathematical models of their time response and noise are needed. The random nature of adsorption–desorption and mass transfer (MT) processes that generate the sensor response makes the sensor output signal inherently stochastic and necessitates the use of a stochastic approach in sensor response analysis. We present a stochastic model of the sensor time response, which takes into account the coupling of adsorption–desorption and MT processes. It is used for the analysis of response kinetics and ultimate noise performance of protein biosensors. We show that slow MT not only decelerates the response kinetics, but also increases the noise and decreases the sensor’s maximal achievable signal-to-noise ratio, thus degrading the ultimate sensor performance, including the minimal detectable/quantifiable analyte concentration. The results illustrate the significance of the presented model for the correct interpretation of measurement data, for the estimation of sensors’ noise performance metrics important for reliable analyte detection/quantification, as well as for sensor optimization in terms of the lower detection/quantification limit. They are also incentives for the further investigation of the MT influence in nanoscale sensors, as a possible cause of false-negative results in analyte detection experiments. microfluidic adsorption-based sensor stochastic model adsorption mass transfer ultimate noise performance detection limit Biotechnology Zoran Djurić verfasserin aut Katarina Radulović verfasserin aut Miloš Frantlović verfasserin aut Gradimir V. Milovanović verfasserin aut Predrag M. Krstajić verfasserin aut In Biosensors MDPI AG, 2012 11(2021), 6, p 194 (DE-627)718626451 (DE-600)2662125-3 20796374 nnns volume:11 year:2021 number:6, p 194 https://doi.org/10.3390/bios11060194 kostenfrei https://doaj.org/article/872febaa3df746229705a1459d4ddcf5 kostenfrei https://www.mdpi.com/2079-6374/11/6/194 kostenfrei https://doaj.org/toc/2079-6374 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 6, p 194
language	English
source	In Biosensors 11(2021), 6, p 194 volume:11 year:2021 number:6, p 194
sourceStr	In Biosensors 11(2021), 6, p 194 volume:11 year:2021 number:6, p 194
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	microfluidic adsorption-based sensor stochastic model adsorption mass transfer ultimate noise performance detection limit Biotechnology
isfreeaccess_bool	true
container_title	Biosensors
authorswithroles_txt_mv	Ivana Jokić @@aut@@ Zoran Djurić @@aut@@ Katarina Radulović @@aut@@ Miloš Frantlović @@aut@@ Gradimir V. Milovanović @@aut@@ Predrag M. Krstajić @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	718626451
id	DOAJ051123827
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ051123827</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240412174427.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/bios11060194</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ051123827</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ872febaa3df746229705a1459d4ddcf5</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TP248.13-248.65</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Ivana Jokić</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Stochastic Time Response and Ultimate Noise Performance of Adsorption-Based Microfluidic Biosensors</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In order to improve the interpretation of measurement results and to achieve the optimal performance of microfluidic biosensors, advanced mathematical models of their time response and noise are needed. The random nature of adsorption–desorption and mass transfer (MT) processes that generate the sensor response makes the sensor output signal inherently stochastic and necessitates the use of a stochastic approach in sensor response analysis. We present a stochastic model of the sensor time response, which takes into account the coupling of adsorption–desorption and MT processes. It is used for the analysis of response kinetics and ultimate noise performance of protein biosensors. We show that slow MT not only decelerates the response kinetics, but also increases the noise and decreases the sensor’s maximal achievable signal-to-noise ratio, thus degrading the ultimate sensor performance, including the minimal detectable/quantifiable analyte concentration. The results illustrate the significance of the presented model for the correct interpretation of measurement data, for the estimation of sensors’ noise performance metrics important for reliable analyte detection/quantification, as well as for sensor optimization in terms of the lower detection/quantification limit. They are also incentives for the further investigation of the MT influence in nanoscale sensors, as a possible cause of false-negative results in analyte detection experiments.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">microfluidic adsorption-based sensor</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">stochastic model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">adsorption</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">mass transfer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ultimate noise performance</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">detection limit</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Biotechnology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zoran Djurić</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Katarina Radulović</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Miloš Frantlović</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gradimir V. Milovanović</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Predrag M. Krstajić</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Biosensors</subfield><subfield code="d">MDPI AG, 2012</subfield><subfield code="g">11(2021), 6, p 194</subfield><subfield code="w">(DE-627)718626451</subfield><subfield code="w">(DE-600)2662125-3</subfield><subfield code="x">20796374</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:6, p 194</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/bios11060194</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/872febaa3df746229705a1459d4ddcf5</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2079-6374/11/6/194</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2079-6374</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">11</subfield><subfield code="j">2021</subfield><subfield code="e">6, p 194</subfield></datafield></record></collection>
callnumber-first	T - Technology
author	Ivana Jokić
spellingShingle	Ivana Jokić misc TP248.13-248.65 misc microfluidic adsorption-based sensor misc stochastic model misc adsorption misc mass transfer misc ultimate noise performance misc detection limit misc Biotechnology Stochastic Time Response and Ultimate Noise Performance of Adsorption-Based Microfluidic Biosensors
authorStr	Ivana Jokić
ppnlink_with_tag_str_mv	@@773@@(DE-627)718626451
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	TP248
illustrated	Not Illustrated
issn	20796374
topic_title	TP248.13-248.65 Stochastic Time Response and Ultimate Noise Performance of Adsorption-Based Microfluidic Biosensors microfluidic adsorption-based sensor stochastic model adsorption mass transfer ultimate noise performance detection limit
topic	misc TP248.13-248.65 misc microfluidic adsorption-based sensor misc stochastic model misc adsorption misc mass transfer misc ultimate noise performance misc detection limit misc Biotechnology
topic_unstemmed	misc TP248.13-248.65 misc microfluidic adsorption-based sensor misc stochastic model misc adsorption misc mass transfer misc ultimate noise performance misc detection limit misc Biotechnology
topic_browse	misc TP248.13-248.65 misc microfluidic adsorption-based sensor misc stochastic model misc adsorption misc mass transfer misc ultimate noise performance misc detection limit misc Biotechnology
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Biosensors
hierarchy_parent_id	718626451
hierarchy_top_title	Biosensors
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)718626451 (DE-600)2662125-3
title	Stochastic Time Response and Ultimate Noise Performance of Adsorption-Based Microfluidic Biosensors
ctrlnum	(DE-627)DOAJ051123827 (DE-599)DOAJ872febaa3df746229705a1459d4ddcf5
title_full	Stochastic Time Response and Ultimate Noise Performance of Adsorption-Based Microfluidic Biosensors
author_sort	Ivana Jokić
journal	Biosensors
journalStr	Biosensors
callnumber-first-code	T
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2021
contenttype_str_mv	txt
author_browse	Ivana Jokić Zoran Djurić Katarina Radulović Miloš Frantlović Gradimir V. Milovanović Predrag M. Krstajić
container_volume	11
class	TP248.13-248.65
format_se	Elektronische Aufsätze
author-letter	Ivana Jokić
doi_str_mv	10.3390/bios11060194
author2-role	verfasserin
title_sort	stochastic time response and ultimate noise performance of adsorption-based microfluidic biosensors
callnumber	TP248.13-248.65
title_auth	Stochastic Time Response and Ultimate Noise Performance of Adsorption-Based Microfluidic Biosensors
abstract	In order to improve the interpretation of measurement results and to achieve the optimal performance of microfluidic biosensors, advanced mathematical models of their time response and noise are needed. The random nature of adsorption–desorption and mass transfer (MT) processes that generate the sensor response makes the sensor output signal inherently stochastic and necessitates the use of a stochastic approach in sensor response analysis. We present a stochastic model of the sensor time response, which takes into account the coupling of adsorption–desorption and MT processes. It is used for the analysis of response kinetics and ultimate noise performance of protein biosensors. We show that slow MT not only decelerates the response kinetics, but also increases the noise and decreases the sensor’s maximal achievable signal-to-noise ratio, thus degrading the ultimate sensor performance, including the minimal detectable/quantifiable analyte concentration. The results illustrate the significance of the presented model for the correct interpretation of measurement data, for the estimation of sensors’ noise performance metrics important for reliable analyte detection/quantification, as well as for sensor optimization in terms of the lower detection/quantification limit. They are also incentives for the further investigation of the MT influence in nanoscale sensors, as a possible cause of false-negative results in analyte detection experiments.
abstractGer	In order to improve the interpretation of measurement results and to achieve the optimal performance of microfluidic biosensors, advanced mathematical models of their time response and noise are needed. The random nature of adsorption–desorption and mass transfer (MT) processes that generate the sensor response makes the sensor output signal inherently stochastic and necessitates the use of a stochastic approach in sensor response analysis. We present a stochastic model of the sensor time response, which takes into account the coupling of adsorption–desorption and MT processes. It is used for the analysis of response kinetics and ultimate noise performance of protein biosensors. We show that slow MT not only decelerates the response kinetics, but also increases the noise and decreases the sensor’s maximal achievable signal-to-noise ratio, thus degrading the ultimate sensor performance, including the minimal detectable/quantifiable analyte concentration. The results illustrate the significance of the presented model for the correct interpretation of measurement data, for the estimation of sensors’ noise performance metrics important for reliable analyte detection/quantification, as well as for sensor optimization in terms of the lower detection/quantification limit. They are also incentives for the further investigation of the MT influence in nanoscale sensors, as a possible cause of false-negative results in analyte detection experiments.
abstract_unstemmed	In order to improve the interpretation of measurement results and to achieve the optimal performance of microfluidic biosensors, advanced mathematical models of their time response and noise are needed. The random nature of adsorption–desorption and mass transfer (MT) processes that generate the sensor response makes the sensor output signal inherently stochastic and necessitates the use of a stochastic approach in sensor response analysis. We present a stochastic model of the sensor time response, which takes into account the coupling of adsorption–desorption and MT processes. It is used for the analysis of response kinetics and ultimate noise performance of protein biosensors. We show that slow MT not only decelerates the response kinetics, but also increases the noise and decreases the sensor’s maximal achievable signal-to-noise ratio, thus degrading the ultimate sensor performance, including the minimal detectable/quantifiable analyte concentration. The results illustrate the significance of the presented model for the correct interpretation of measurement data, for the estimation of sensors’ noise performance metrics important for reliable analyte detection/quantification, as well as for sensor optimization in terms of the lower detection/quantification limit. They are also incentives for the further investigation of the MT influence in nanoscale sensors, as a possible cause of false-negative results in analyte detection experiments.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	6, p 194
title_short	Stochastic Time Response and Ultimate Noise Performance of Adsorption-Based Microfluidic Biosensors
url	https://doi.org/10.3390/bios11060194 https://doaj.org/article/872febaa3df746229705a1459d4ddcf5 https://www.mdpi.com/2079-6374/11/6/194 https://doaj.org/toc/2079-6374
remote_bool	true
author2	Zoran Djurić Katarina Radulović Miloš Frantlović Gradimir V. Milovanović Predrag M. Krstajić
author2Str	Zoran Djurić Katarina Radulović Miloš Frantlović Gradimir V. Milovanović Predrag M. Krstajić
ppnlink	718626451
callnumber-subject	TP - Chemical Technology
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.3390/bios11060194
callnumber-a	TP248.13-248.65
up_date	2024-07-03T18:32:59.302Z
_version_	1803583833299222529
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ051123827</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240412174427.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/bios11060194</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ051123827</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ872febaa3df746229705a1459d4ddcf5</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TP248.13-248.65</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Ivana Jokić</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Stochastic Time Response and Ultimate Noise Performance of Adsorption-Based Microfluidic Biosensors</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In order to improve the interpretation of measurement results and to achieve the optimal performance of microfluidic biosensors, advanced mathematical models of their time response and noise are needed. The random nature of adsorption–desorption and mass transfer (MT) processes that generate the sensor response makes the sensor output signal inherently stochastic and necessitates the use of a stochastic approach in sensor response analysis. We present a stochastic model of the sensor time response, which takes into account the coupling of adsorption–desorption and MT processes. It is used for the analysis of response kinetics and ultimate noise performance of protein biosensors. We show that slow MT not only decelerates the response kinetics, but also increases the noise and decreases the sensor’s maximal achievable signal-to-noise ratio, thus degrading the ultimate sensor performance, including the minimal detectable/quantifiable analyte concentration. The results illustrate the significance of the presented model for the correct interpretation of measurement data, for the estimation of sensors’ noise performance metrics important for reliable analyte detection/quantification, as well as for sensor optimization in terms of the lower detection/quantification limit. They are also incentives for the further investigation of the MT influence in nanoscale sensors, as a possible cause of false-negative results in analyte detection experiments.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">microfluidic adsorption-based sensor</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">stochastic model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">adsorption</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">mass transfer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ultimate noise performance</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">detection limit</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Biotechnology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zoran Djurić</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Katarina Radulović</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Miloš Frantlović</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gradimir V. Milovanović</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Predrag M. Krstajić</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Biosensors</subfield><subfield code="d">MDPI AG, 2012</subfield><subfield code="g">11(2021), 6, p 194</subfield><subfield code="w">(DE-627)718626451</subfield><subfield code="w">(DE-600)2662125-3</subfield><subfield code="x">20796374</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:6, p 194</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/bios11060194</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/872febaa3df746229705a1459d4ddcf5</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2079-6374/11/6/194</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2079-6374</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">11</subfield><subfield code="j">2021</subfield><subfield code="e">6, p 194</subfield></datafield></record></collection>
score	7.399599

Nicht das Richtige dabei?

Schreiben Sie uns!

Stochastic Time Response and Ultimate Noise Performance of Adsorption-Based Microfluidic Biosensors

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?