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SmartGas

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A Universal Acoustic MEMS Gas Sensor with Machine Learning

I am proposing a novel acoustic MEMS gas sensor with machine learning for the first time that can revolutionize the gas sensing field. Gas sensors targeting only a specific type of gas are developed due to the nature of the existi... ver más

30/04/2023

BILKENTUNIVERSITY...

157K€

Presupuesto del proyecto: 157K€

Líder del proyecto

BILKENT UNIVERSITESI VAKIF No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 2 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2023-04-30

WF-03-2020: Widening Fellowships

I+D

Cerrada hace 4 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

2 Participantes

BILKENTUNIVERSITY BILIM KENT...

157.36K€ | Lider

AERNNOVA ANDALUCIA ESTRUCTUR...

790.53K€ | Participante

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Duración del proyecto: 24 meses Fecha Inicio: 2021-04-15
Fecha Fin: 2023-04-30

Líder del proyecto

BILKENT UNIVERSITESI VAKIF No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 157K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto I am proposing a novel acoustic MEMS gas sensor with machine learning for the first time that can revolutionize the gas sensing field. Gas sensors targeting only a specific type of gas are developed due to the nature of the existing sensing technologies. Current gas sensors either rely on the detection of the electrical property changes upon the reaction of an active material with the gas or infrared (IR) transmission/absorption characteristics of the gases. I am proposing to couple the acoustic resonance in a cavity with a MEMS resonator and use the coupled resonance and damping as a gas sensor. Acoustic frequency and the damping are a function of the gas inside the cavity, and together can be used to detect any gas with machine learning. Since the frequency and damping change with multiple gases in the proposed approach, machine learning algorithms can extract the gas changes in a smart way. Proposed sensor solves the problems of the current gas sensors. It can be manufactured with standard MEMS fabrication flows, can be used with any gas, does not saturate, and has immediate response time. The sensors will be fabricated in the fully equipped clean room UNAM in Bilkent University. I will apply the sensor on human health by working with a pulmonologist to measure CO in human breath and indoor air quality. My supervisor Prof. Hilmi Volkan Demir will guide me throughout the project and help me to improve my technical and soft skills. I will also be able to improve my network and knowledge during my secondment in Fraunhofer EMFT, Germany. Collaborating with Analog Devices and Fraunhofer will be the key for further product technology development. In summary, this fellowship will establish me as a recognized European leader by demonstrating a proof of concept with interdisciplinary research (MEMS, machine learning, and medical application), by patenting and publishing my innovation, and by enabling the path for low cost universal gas sensor productization.

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