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July 11, 2025

Graphene-based artificial tongue achieves near-human-like sense of taste

Schemes of the biological and graphene-oxide ionic sensory memristive device (GO- ISMD),-based gustatory systems. In the biological gustatory system, chemical stimuli first trigger the electrical responses through various taste receptor cells distributed on the tongue. These electrical potentials are subsequently encoded and transmitted via nerves to the cerebral cortical neurons for processing and taste recognition. In the GO- ISMD- based system, the sensing signals generated by feeding different saline flavors are first encoded and then imported to a dynamic GO- ISMD- based reservoir computing system for processing and perceiving. Credit: Proceedings of the National Academy of Sciences (2025). DOI: 10.1073/pnas.2413060122
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Schemes of the biological and graphene-oxide ionic sensory memristive device (GO- ISMD),-based gustatory systems. In the biological gustatory system, chemical stimuli first trigger the electrical responses through various taste receptor cells distributed on the tongue. These electrical potentials are subsequently encoded and transmitted via nerves to the cerebral cortical neurons for processing and taste recognition. In the GO- ISMD- based system, the sensing signals generated by feeding different saline flavors are first encoded and then imported to a dynamic GO- ISMD- based reservoir computing system for processing and perceiving. Credit: Proceedings of the National Academy of Sciences (2025). DOI: 10.1073/pnas.2413060122

A team of researchers report in the on a new graphene-based sensor design that, through machine learning, was able to develop a near-human sense of taste. This device is the first of its kind to operate in a moist environment, better approximating the conditions inside the human mouth.

The sensor described in the was made of multiple layers of graphene oxide, a material well known for its tunable electrical properties and high chemical reactivity, enclosed in a nanofluidic device.

Pure graphene—which was first isolated by Andre Geim and Kosta Novoslov, earning them the Nobel Prize in ÌÇÐÄÊÓÆµics in 2010—is a material made of a single layer of carbon atoms bound together in a lattice-like structure, endowing it with a range of mechanical, electrical, and .

Like graphene, changes its when exposed to different chemicals. The researchers used this property to measure electrical variations in the sensor when it was exposed to a sampling of 160 chemicals, each associated with a unique flavor profile. Using these data, a was able to create a 'memory' of flavors.

This learning process is analogous to the way the human brain interprets signals from our taste buds when they react to chemicals in our foods. It was long held that humans could detect five distinct tastes: sweet, salty, bitter, sour, and umami. In 2023, researchers isolated a , ammonia chloride.

Schematic illustration of the device configuration. The size of the Graphene-oxide membrane is 4 mm × 4 mm. The scale bar in the SEM image is 500 nm. Credit: Proceedings of the National Academy of Sciences (2025). DOI: 10.1073/pnas.2413060122
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Schematic illustration of the device configuration. The size of the Graphene-oxide membrane is 4 mm × 4 mm. The scale bar in the SEM image is 500 nm. Credit: Proceedings of the National Academy of Sciences (2025). DOI: 10.1073/pnas.2413060122

During testing, the new artificial tasting system's algorithm, which was trained to classify four basic tastes (sweet, salty, bitter, sour), could readily identify tastes it had already experienced with an accuracy of around 98.5%.

It was also able to categorize flavors of 40 samples it hadn't previously encountered, with an accuracy ranging from 75% to 90%. The researchers also trained the algorithm to identify the more complex tastes of coffee and cola.

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Addressing one of the limitations of previous artificial gustatory systems (the technical term for similar artificial tongues), the new design integrated the sensing and computing functions of taste perception into a single nanofluidic device.

According to the authors, this system has the potential to one day restore taste perception to people who have lost that ability due to stroke, viral infection, or a range of neurodegenerative conditions. There are a number of technical hurdles to overcome before that time, however.

The complete system, which was designed as a proof-of-concept experiment, is relatively bulky with concurrently large energy demands. The researchers note that further miniaturization and integration are needed for practical applications.

More information: Yuchun Zhang et al, Confinement of ions within graphene oxide membranes enables neuromorphic artificial gustation, Proceedings of the National Academy of Sciences (2025).

Journal information: Proceedings of the National Academy of Sciences

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A graphene oxide-based artificial tongue, integrated with machine learning, can identify four basic tastes with 98.5% accuracy and classify unfamiliar flavors with 75–90% accuracy. The device operates in moist conditions, mimicking the human mouth, and combines sensing and processing in a single nanofluidic system, suggesting potential for future taste restoration applications.

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