Origami Bioinspired Sensor Array Using Nanocluster−Carbon Dot Hybrids for Multiplexed Smell- and Taste-Mimetic Analysis of Blood Plasma in Cancer Detection

Inspired by the complementary integration of smell and taste in mammals, we developed a miniaturized, paper-based sensor that combines optical nose (ON) and optical tongue (OT) functionalities for the rapid and minimally invasive detection of breast and head-and-neck cancers. The device features an origami-inspired three-dimensional architecture that vertically integrates liquid-phase and vapor-phase sensing modules, enabling simultaneous analysis of nonvolatile metabolites and volatile organic compounds from small plasma volumes. Each sensing layer incorporates eight fluorescent elements based on green- and blue-emissive carbon dots and their hybrids with protein-stabilized bimetallic nanoclusters (Au-Ag, Au-Cd, and Au-Ni), providing a chemically diverse and cross-reactive sensing array. Fluorescence modulation arises from complementary mechanisms, including polarity-driven surface-state perturbation in carbon dots and adsorption-mediated electron transfer and coordination interactions in nanoclusters, generating distinct optical fingerprints. Plasma samples from 120 individuals were analyzed. Both OT and ON modules produced characteristic response patterns, while their integration enhanced discrimination performance by expanding the effective sensing dimensionality. Linear discriminant analysis achieved cross-validation accuracy of 0.998 ± 0.014 (95% CI: 0.995-1.000), and support vector machine classification yielded near-perfect performance with ROC-AUC values approaching 1.00 following hyperparameter optimization. This compact and cost-effective platform requires only microliter-scale plasma volumes and offers strong potential for rapid metabolic fingerprinting and noninvasive cancer screening, although further validation on larger clinical cohorts will be necessary.

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Publication Details

Journal
ACS Sensors
Published
2026-06-25
DOI
https://doi.org/10.1021/acssensors.6c00629
Primary Topic
Advanced Chemical Sensor Technologies
Type
article
Field-Weighted Citation Impact
0.00

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article

Origami Bioinspired Sensor Array Using Nanocluster−Carbon Dot Hybrids for Multiplexed Smell- and Taste-Mimetic Analysis of Blood Plasma in Cancer Detection

Mohammad Javad Fattahi, Zahra Shojaeifard, Oldouz Heidary, Abbas Ghaderi et al.
ACS Sensors
Advanced Chemical Sensor Technologies
article

Origami Bioinspired Sensor Array Using Nanocluster−Carbon Dot Hybrids for Multiplexed Smell- and Taste-Mimetic Analysis of Blood Plasma in Cancer Detection

Mohammad Javad Fattahi, Zahra Shojaeifard, Oldouz Heidary, Abbas Ghaderi, Bahram Hemmateenejad, Omidreza Firuzi, Morteza Akhond
article en

Abstract

Inspired by the complementary integration of smell and taste in mammals, we developed a miniaturized, paper-based sensor that combines optical nose (ON) and optical tongue (OT) functionalities for the rapid and minimally invasive detection of breast and head-and-neck cancers. The device features an origami-inspired three-dimensional architecture that vertically integrates liquid-phase and vapor-phase sensing modules, enabling simultaneous analysis of nonvolatile metabolites and volatile organic compounds from small plasma volumes. Each sensing layer incorporates eight fluorescent elements based on green- and blue-emissive carbon dots and their hybrids with protein-stabilized bimetallic nanoclusters (Au-Ag, Au-Cd, and Au-Ni), providing a chemically diverse and cross-reactive sensing array. Fluorescence modulation arises from complementary mechanisms, including polarity-driven surface-state perturbation in carbon dots and adsorption-mediated electron transfer and coordination interactions in nanoclusters, generating distinct optical fingerprints. Plasma samples from 120 individuals were analyzed. Both OT and ON modules produced characteristic response patterns, while their integration enhanced discrimination performance by expanding the effective sensing dimensionality. Linear discriminant analysis achieved cross-validation accuracy of 0.998 ± 0.014 (95% CI: 0.995-1.000), and support vector machine classification yielded near-perfect performance with ROC-AUC values approaching 1.00 following hyperparameter optimization. This compact and cost-effective platform requires only microliter-scale plasma volumes and offers strong potential for rapid metabolic fingerprinting and noninvasive cancer screening, although further validation on larger clinical cohorts will be necessary.

ACS Sensors
Shiraz University (IR)
Shiraz University
Reduced inequalities
Openalex Percentile: Top 12%
Advanced Chemical Sensor Technologies
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