Metal-Organic Framework Field-Effect Transistor Arrays for Chemical Sensing

Project Information

MOFFET

Grant agreement ID: 101069401

DOI

10.3030/101069401

Project closed

EC signature date 24 March 2022

Start date 1 April 2022

End date 30 September 2023

Funded under

• European Research Council (ERC)

Total cost

No data

EU contribution

€ 150 000,00

Coordinated by

KATHOLIEKE UNIVERSITEIT LEUVEN
Belgium

Periodic Reporting for period 1 - MOFFET (Metal-Organic Framework Field-Effect Transistor Arrays for Chemical Sensing)

Reporting period: 2022-04-01 to 2023-09-30

Summary of the context and overall objectives of the project

Detecting and monitoring volatile organic compounds (VOCs) at ppm concentrations presents significant challenges but is vital across various sectors, including healthcare for disease diagnosis and environmental monitoring for air quality assessment. Conventional methods, primarily involving semiconducting metal oxide (SMOx) sensors, are widely used due to their cost-effectiveness, high sensitivity, and rapid response rates. These sensors function by exhibiting changes in resistivity when exposed to VOCs. Despite their advantages, SMOx sensors have notable limitations, such as reduced lifespan, low selectivity, requirement of high operational temperatures (typically 200-300 °C), and issues like baseline drifts that compromise data reliability.

To address these selectivity challenges, the concept of an electronic nose (E-nose) has been developed, which involves an array of SMOx sensors, each with partial selectivity to different VOCs and background interferences. The idea is that by combining the multivariate responses from these sensors, specific VOCs can be identified even in complex environments. However, the effectiveness of current E-nose designs is hindered by the high correlation in responses among the sensor elements, leading to two main issues: either the array is too small to cover the required chemical diversity, or it becomes overly large, leading to increased costs and power consumption.

In response to these limitations, there is a growing demand for innovative VOC sensing technologies.The MOFFET project proposes a departure from traditional SMOx sensors, focusing instead on metal-organic frameworks (MOFs). MOFs are unique for their microporous, crystalline structures formed from metal ions linked by organic ligands. Their sub-nanometer pores and customizable pore walls make MOFs exceptionally capable of capturing VOCs, even at trace levels. While achieving perfect selectivity for a specific VOC in a complex background may be overly optimistic, an array of MOFs, each with different adsorption characteristics, can function effectively as an E-nose.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

A prototype MOFFET sensor was developed and tested. This achievement required the development of new fabrication flows for (1) the thin-film transistors that are at the heart of the MOFFET sensor and (2) the integration of the highly porous sensing layer based on a metal-organic framework (MOF) coating. Various configurations of Thin-film Transistors (TFTs) with different channel widths and lengths were explored. A significant focus was on the gate dimensions to facilitate gas analyte access. However, the first generation of devices encountered issues such as poor electrical characteristics, highlighting the need for design adjustments. This led to the exploration of the impact of gate width on the device's electrical performance, resulting in improved outcomes.

Subsequent iterations of MOFFETs aimed to address these initial drawbacks by resizing the transistors and optimizing the gate and interface designs. Despite these modifications, challenges such as device shorting persisted. A novel method involving the integration of a specific compound into the devices was developed, requiring careful calibration of reaction conditions to avoid damage while ensuring quality. This process led to modifications in the devices' electrical characteristics, with notable changes in threshold voltage and subthreshold slope. Compared with analog capacitor-based devices, MOFFETs arise as a promising technology as they demonstrate a sensitivity that is 50 times higher.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The project successfully met its targeted objectives, leading to the development of prototype sensors that demonstrated promising performance in controlled tests. These prototypes exhibited one of the largest responses ever measured for sensors of this type when detecting selected volatile organic components (VOCs). The current focus is on refining these prototypes for VOCs present in human breath, with the ultimate aim of creating sensors suitable for breath-based disease diagnosis.

The potential impact of this advancement is significant both economically and socially. If successful, these sensors could revolutionize the field of medical diagnostics, offering a non-invasive, efficient, and potentially cost-effective method for early disease detection. This could lead to earlier interventions, improved patient outcomes, and reduced healthcare costs. Moreover, the environmental monitoring capabilities of these sensors could have far-reaching implications for public health and safety.

To ensure further uptake and success of this technology, several key needs must be addressed. In the first place, these are related to (i) further research and development: further improvement and testing of sensor sensitivity, selectivity, and durability are essential. Research into integrating other MOF materials and refining detection algorithms will enhance the performance and reliability of the sensors'; (ii) demonstration and validation: extensive testing under real-world conditions is necessary to demonstrate the effectiveness and reliability of the sensors. Pilot studies in clinical settings would provide valuable data on their efficacy in disease diagnosis.