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May 20, 2025

Color-changing sensor detects alcohol with a smartphone snap

The sensor visually detects ethanol concentration, offering a simple, sensitive, and effective way to measure alcohol levels without complex equipment. Credit: Osaka Metropolitan University
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The sensor visually detects ethanol concentration, offering a simple, sensitive, and effective way to measure alcohol levels without complex equipment. Credit: Osaka Metropolitan University

Determining how strong your drink is doesn't need to be either guesswork or lab work. New research has made it as simple as checking your messages鈥攁nd more colorful, too. The study is in the journal Small Science.

Osaka Metropolitan University researchers have developed a smartphone-compatible alcohol sensor that can visually detect a full range of ethanol concentrations, without the need for complex electronics or lab tools. Their technology allows for a broad array of potential applications in , health care, , and alcohol breath analysis.

Ethanol is used widely in food, pharmaceuticals, and fuel. It is also the intoxicating ingredient in many . Accurate detection of ethanol concentration, particularly in products containing both ethanol and water, is crucial for product hygiene management and quality maintenance.

"Conventional sensors typically require power sources and complex electronics, limiting their accessibility for everyday use," said Kenji Okada, an associate professor at Osaka Metropolitan University's Graduate School of Engineering and lead author of this study.

Seeking both selectivity and practicality, the team fabricated a portable and highly sensitive ethanol sensor built from a copper-based metal鈥搊rganic framework (MOF) thin film called Cu-MOF-74.

a鈥揷) SEM images of the thin films; (a) Cu(OH)2 nanobelts, (b) Cu-MOF-74 thin film synthesized in MeOH, and (c) Cu-MOF-74 thin film synthesized in DMF:MeOH鈥=鈥3:7. d) Peak area of the band from MOF (1556鈥塩m鈭1. Credit: Small Science (2025). DOI: 10.1002/smsc.202400634
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a鈥揷) SEM images of the thin films; (a) Cu(OH)2 nanobelts, (b) Cu-MOF-74 thin film synthesized in MeOH, and (c) Cu-MOF-74 thin film synthesized in DMF:MeOH鈥=鈥3:7. d) Peak area of the band from MOF (1556鈥塩m鈭1. Credit: Small Science (2025). DOI: 10.1002/smsc.202400634

These MOFs contain nanometer-sized pores that absorb ethanol molecules and respond with a visible color change鈥攁 phenomenon known as solvato/vapochromism. Thanks to its low light-scattering properties and high transparency, the Cu-MOF-74 film enables precise optical measurements without the need for complex lab equipment.

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"Our sensor changes color in response to varying ethanol levels across the full concentration range, even at low concentrations," Okada said.

What truly sets this technology apart is its integration with a smartphone app. Users can simply snap a photo of the film to measure concentration, making it a portable and accessible tool for use in the field, factories, or settings.

The researchers' findings offer a smarter, simpler, and more reliable approach to alcohol sensing. From the quality of your drink to the potential future of portable breath tests, this new sensor technology brings us a colorful step closer to real-time alcohol monitoring in everyday life.

"We hope our study could open up a wide range of applications, from the food and beverage industry to environmental monitoring, industrial exhaust gas detection and alcohol breath analysis," Okada said.

More information: Yuto Toki et al, Solvato/Vapochromism鈥怋ased Alcohol Sensing through Metal鈥揙rganic Framework Thin Films with Coordinatively Unsaturated Metal Sites, Small Science (2025).

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A copper-based metal鈥搊rganic framework (Cu-MOF-74) thin film sensor enables visual detection of ethanol concentrations through a color change, covering the full range of ethanol levels, including low concentrations. The sensor鈥檚 response can be quantified using a smartphone app, offering a portable, electronics-free method suitable for diverse applications in quality control and monitoring.

This summary was automatically generated using LLM.