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AI-Powered Sensors Revolutionise Water Safety

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Conventional water contamination sensors have historically encountered problems with reliability and the ability to identify malfunctioning devices. A recent collaborative effort between researchers at Argonne National Laboratory, the School of Molecular Engineering at Chicago, and the University of Wisconsin, Milwaukee could hold promise for bolstering public health safeguards through an early alert mechanism for water contamination.

The partnership has yielded a large-scale sensor production method capable of concurrently detecting lead, mercury, and E. coli in flowing tap water. According to Haihui Pu, an Argonne scientist with a joint appointment at Chicago’s Molecular Engineering School, enhancing these sensors could prevent health emergencies.

These sensors utilise a layer of carbon and oxygen atoms, known as graphene, which is only one nanometer thick and is applied onto a silicon base. This graphene material serves a similar purpose to the semiconductors used in computer chips. Subsequently, gold electrodes are deposited onto the graphene surface, followed by a thin insulating layer made of aluminium oxide. Each sensor is specifically designed to detect one of the three contaminants: lead, mercury, or E. coli.

Evaluating the quality of these sensors on a large scale has been a significant challenge. The thin insulating layer can develop tiny areas of unintended porousness. This porosity allows electrons from the lower graphene layer to leak into the upper insulating layer. This leakage undermines its role as an insulator, leading to inconsistent responses from the sensors.

The team’s recent publication in Nature Communications outlines a method to identify defective devices before mass production. This method involves assessing the electrical behaviour of the insulating layer while the sensor is immersed in water. Significantly, this screening procedure doesn’t harm the sensor. Through this approach, the team pinpointed structural flaws in the insulating layers. Subsequently, they established criteria to recognise malfunctioning devices easily.

To showcase the effectiveness of their technique, the researchers tested a set of three sensors capable of concurrently detecting lead, mercury, and E. coli in running tap water. Leveraging machine learning algorithms to analyse the outcomes, they accurately determined toxin levels at a precision of parts per billion, even when dealing with interfering substances.

“The unique aspect of these sensors lies in their applicability to various water types, not limited to just tap water,” stated Junhong Chen, the Leading Water Strategist at Argonne.

He added, “Moreover, you have the flexibility to utilise three, thirty, or even three hundred sensors, each specifically designed to detect distinct components.” These components encompass heavy metals and bacteria, pharmaceuticals, pesticides, coronaviruses, and a prevalent water pollutant known as per- and polyfluoroalkyl substances. Additionally, they could contain valuable resources such as cobalt for batteries and essential nutrients like nitrogen and phosphorus for plants and animals.

After identifying and removing problematic or valuable substances, these sensors can be employed to assess the purity of treated water. The results can guide safe water repurposing, potable use, agricultural and irrigation purposes, groundwater replenishment, and industrial procedures.

Chen expressed his aspirations for commercialising this technology through his established startup company. However, he emphasised that water contamination is a global health concern requiring collaborative efforts.

Chen said the team’s screening technique provides a versatile tool for monitoring water quality and optimising its safe re-utilisation, generating a healthier and more sustainable future.


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