Bioinspired Rheological Sensing for Robotic Liquid Identification in Sealed Containers via Ultrafast Incipient Slip Detection

AI Summary

Bioinspired iontronic tactile sensor with interlocked protrusion-groove structure enables simultaneous static pressure and incipient slip detection within a single device.
Ultrahigh sensitivity (up to 1873.83 kPa^-1) and ultrafast slip response time (46 ms) enable detection of low-pressure and transient slip.
Robotic fingertip captures transient signals from shaking sealed liquids; Random Forest classifier achieves 99.04% ± 0.47% non-visual identification accuracy.

AI Summary

Its unique biomimetic structure allows for the detection of static pressure and incipient slip within a single device, with a peak sensitivity of up to 1873.83 kPa- 1 in the low-pressure regime and a slip response time as fast as 46 ms.

Furthermore, by extracting physical features from the signals and applying a Random Forest classifier, we developed a non-visual liquid identification system that achieves an accuracy of 99.04% ± 0.47% for different liquids.

Basic summary

Adv Mater. 2026 Jul 6:e73955. doi: 10.1002/adma.73955. Online ahead of print.

ABSTRACT

The rapid, non-invasive identification of sealed liquids is crucial for ensuring counter-terrorism security and improving operational efficiency in various scenarios such as airports and large-scale events. Existing technologies are limited by invasive procedures or reliance on vision. In this research, we developed a bioinspired iontronic tactile sensor with an interlocked “protrusion-groove” structure. Its unique biomimetic structure allows for the detection of static pressure and incipient slip within a single device, with a peak sensitivity of up to 1873.83 kPa^– ¹ in the low-pressure regime and a slip response time as fast as 46 ms. A robotic fingertip integrated with this sensor can effectively capture the transient signals generated by shaking liquids. Furthermore, by extracting physical features from the signals and applying a Random Forest classifier, we developed a non-visual liquid identification system that achieves an accuracy of 99.04% ± 0.47% for different liquids. This work provides a practical solution for non-visual liquid identification and demonstrates a robust approach for enabling robots to achieve multimodal tactile perception.

PMID:42411017 | DOI:10.1002/adma.73955

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