Developing innovative approaches to water testing is crucial to tackling water pollution and contamination. In this article, we discuss how graphene sensors could be one path to securing a safe water supply.
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A recent study published in Nature Communications presents a novel approach to water testing and integrates wet transfer techniques, impedance, noise measurements and machine learning algorithms. This integration provides an efficient approach for the scalable fabrication process of sensor arrays based on graphene field-effect transistors (GFETs) and enhances the effective detection of faulty devices.
A Brief Overview of Graphene FET Sensors
Field-effect transistors (FETs) are an integral part of the domain of electrical sensors and operate by detecting variations in the conductance of a transducer material. 2D Field-Effect Transistor sensors (FET sensors) have received considerable interest, particularly in biomolecular sensing over water samples. This heightened interest is mainly because of their ultrahigh sensitivity, reduced costs, and ease of manufacturing on the nanoscale. Given their remarkable electron transport characteristics and ease of functionalization, materials based on graphene prove highly suitable as transducers in such sensor applications.
FET Sensors and the Challenges to Face
The commercialization of 2D FET sensors for real-time water sensing continues to pose significant challenges in terms of scaling up fabrication. This is primarily due to the inherent difficulties in maintaining consistent device quality control, resulting in variations in output characteristics, calibration, and reliability among different devices.
Current efforts to address these challenges have predominantly concentrated on the crucial initial stage of controlling the sensor's materials. Some examples are large-scale processes such as chemical vapor deposition growth for 2D nanomaterials, direct thin film printing, spin-coating, and self-assembly onto the substrate.
Nevertheless, identifying a singular nanomaterial sheet and the subsequent patterning of a single pair of electrodes in an FET sensor represent laborious and resource-intensive endeavors.
Furthermore, a comprehensive approach is currently unavailable to address device variations by directly linking faulty sensor devices to non-destructive measurements for isolation within large-scale manufacturing processes. Similarly, there is a lack of modeling capabilities for sensor responses in ideal-like devices using advanced data analysis to achieve exceptionally accurate predictions.
Graphene-Based Sensors for Water Testing
The choice of representative pollutants for testing included heavy metals such as lead and mercury, along with E. coli bacteria, as they are prominent contaminants commonly found in drinking water supplies.
The nanofabrication process, performed on a wafer scale, involved spin-coating the channel materials onto a wafer substrate in a solution phase. The results from the sensing experiments indicate that FET sensors do not consistently display a unidirectional exponential response, which would align with the ideal behavior proposed by Langmuir's theory of adsorption.
The research approach involved selecting the time-domain response output collected simultaneously from each device within the sensor array. These responses were then utilized in conjunction with machine learning techniques to classify and quantify various mixture conditions. By using the concurrent responses from multiple sensors after exposing the array to various combinations of toxins, principal component analysis (PCA) achieved successful classification and quantification of the target components.
It is worth noting that, in the low-concentration region (<2.5 ppb or cfu/mL), there was some overlap observed among the target species. However, it is crucial to highlight that these concentrations remained well below the corresponding thresholds established by the World Health Organization for tap water standards. With the assistance of machine learning, the identification and quantification of multiple toxins within real tap water flow were accomplished with a high degree of accuracy.
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The research findings revealed that bidirectional response was observed from FET sensors. A notable drawback identified in the recent research is the presence of a bidirectional response pattern, which poses challenges in accurately predicting the concentration of detected toxins. Consequently, it becomes imperative to proactively identify and segregate defective devices by establishing correlations with non-ideal responses before conducting tests with real water samples.
One method employed for this purpose involves optical microscopy, which aids in identifying visible structural defects arising from fabrication issues, such as unsuccessful lift-off and discontinuous coating of the SiO2 protection layer.
However, it is essential to acknowledge that despite its simplicity, this approach is time-consuming and, therefore, less efficient when applied to the task of isolating faulty devices during large-scale fabrication. Furthermore, it is crucial to note that invisible defects, such as tiny cavities, are commonplace and challenging to detect. These defects can have a detrimental impact on device performance, thus constraining the overall effectiveness of optical microscopy as a pre-screening tool.
Will This Novel Sensor Study Affect Other Industries?
Graphene FET sensor technology is not only essential for water treatment and sensing, but it is also revolutionizing other industries. NASA Goddard Space Flight Center utilizes a Graphene FET sensor technology featuring graphene placed on a silicon substrate.
The patterning of graphene involves a two-step process: initially, e-beam lithography is employed, followed by a reactive ion etch to achieve the desired length and width. This methodology leverages the graphene's sensitivity to changes in the local electric field, induced by the interaction of radiation with the underlying absorber substrate.
Consequently, this technology offers radiation sensors with significant advantages, including low power consumption and high sensitivity, making them particularly valuable for both the commercial space industry and government agencies.
Furthermore, this technology holds substantial promise for future helio-physics science missions. In such missions, the deployment of small, lightweight radiation sensors like GFET-RS is envisaged on arrays of CubeSats, demonstrating its potential significance in advancing scientific exploration and understanding in the field of aeronautics and astronautics.
The latest article published in Analytical and Bioanalytical Chemistry states that despite notable advancements in aligning graphene field-effect transistor biosensors with applications in life sciences and their integration into microelectronic platforms, several challenges still lie ahead. While these sensors offer fabrication advantages compared to devices using 1D materials, there are lingering questions concerning device structure, batch-to-batch variability, and the methods employed for anchoring bio-receptors.
The utilization of multi-step surface chemistry approaches continues to present a barrier to widespread implementation in most industrial processes. Therefore, innovations aimed at simplifying graphene modifications into a single-step process would represent a significant breakthrough. Such advancements could pave the way for introducing the first European graphene-FET-based sensor product in the market, marking a milestone in this field.
References and Further Reading
NASA, (2023). Graphene Field Effect Transistors for Radiation Detection (GFET-RS). [Online] Available at: https://technology.nasa.gov/patent/GSC-TOPS-143
Szunerits, S., et al. (2023). Graphene-based field-effect transistors for biosensing: where is the field heading to? Analytical and Bioanalytical Chemistry. Available at: doi.org/10.1007/s00216-023-04760-1
Laserna, et.al. (2020). Review—Graphene-Based Water Quality Sensors. Journal of the Electrochemical Society. 167(3), p. 037539. Available at: doi.org/10.1149/1945-7111/ab67a5
Maity, A., et al. (2023). Scalable graphene sensor array for real-time toxins monitoring in flowing water. Nature Communications. 14, p. 4184. Available at: doi.org/10.1038/s41467-023-39701-0