.png){kind=icon}
.png){kind=icon}
Return
Shape-Encoded Hydrogel Sensor Particles Enable Multiplex Odorant Detection Through Deep-learning Classification
.png){kind=icon}
OA
AI
DOI：10.1002/smll.202507903.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Simultaneous detection of multiple odorants is a major challenge in the development of portable, cell-based biohybrid sensors, primarily due to the difficulty of distinguishing between different sensor cell types. Here, a strategy that encodes odorant sensor cell types using the shape of hydrogel particles, enabling shape-based identification through deep learning is reported. Each particle shape corresponds to a unique sensor cell type expressing a distinct odorant receptor (OR). A convolutional neural network is trained to classify these shapes with high accuracy, and the resulting shape identification scheme is applied to time-lapse fluorescence images of mixed particles exposed to single odorants. This enabled reliable assignment of particle identity and extraction of shape-specific fluorescence signals. Distinct odorant-dependent responses are observed, consistent with the known ligand specificities of the corresponding ORs. While this study focuses on individual odorants, the shape-based approach provides a position-independent, scalable method for multiplexed odorant detection. This framework supports the development of compact, high-throughput biohybrid sensors for safety, environmental monitoring, and diagnostic applications.
Keywords:
convolutional neural network
hydrogel encoding
hydrogel particles
odorant sensor cells
shape classification
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
12.1
Papers:
3.0W
Citations:
16.4W