A Real-Time Method for Improving Stability of Monolithic Quartz Crystal Microbalance Operating under Harsh Environmental Conditions

[EN] Monolithic quartz crystal microbalance (MQCM) has recently emerged as a very promising technology suitable for biosensing applications. These devices consist of an array of miniaturized QCM sensors integrated within the same quartz substrate capable of detecting multiple target analytes simulta...

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Detalles Bibliográficos
Autores: FERNÁNDEZ DÍAZ, ROMÁN|||0000-0002-8883-4653, Jiménez Jiménez, Yolanda|||0000-0003-4835-9007, Arnau Vives, Antonio|||0000-0002-5709-1690, Calero-Alcarria, María Del Señor
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/176054
Acceso en línea:https://riunet.upv.es/handle/10251/176054
Access Level:acceso abierto
Palabra clave:Monolithic quartz crystal microbalance
Biosensor
Discrete wavelet transform
TECNOLOGIA ELECTRONICA
Descripción
Sumario:[EN] Monolithic quartz crystal microbalance (MQCM) has recently emerged as a very promising technology suitable for biosensing applications. These devices consist of an array of miniaturized QCM sensors integrated within the same quartz substrate capable of detecting multiple target analytes simultaneously. Their relevant benefits include high throughput, low cost per sensor unit, low sample/reagent consumption and fast sensing response. Despite the great potential of MQCM, unwanted environmental factors (e.g., temperature, humidity, vibrations, or pressure) and perturbations intrinsic to the sensor setup (e.g., mechanical stress exerted by the measurement cell or electronic noise of the characterization system) can affect sensor stability, masking the signal of interest and degrading the limit of detection (LoD). Here, we present a method based on the discrete wavelet transform (DWT) to improve the stability of the resonance frequency and dissipation signals in real time. The method takes advantage of the similarity among the noise patterns of the resonators integrated in an MQCM device to mitigate disturbing factors that impact on sensor response. Performance of the method is validated by studying the adsorption of proteins (neutravidin and biotinylated albumin) under external controlled factors (temperature and pressure/flow rate) that simulate unwanted disturbances.