Deep Learning-Assisted Sensor Array Based on Host–Guest Chemistry for Accurate Fluorescent Visual Identification of Multiple Explosives DOI

Wenxing Gao,

Zhibin Wang, Qiang Li

et al.

Analytical Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: May 17, 2025

Accurate and rapid discrimination of multiple explosives with high precision is paramount importance for national security, ecological protection, human health yet remains a significant challenge conventional analytical techniques. Herein, we present an innovative deep learning-assisted artificial vision platform based on cyclodextrin-protected multicolor fluorescent gold nanoclusters (CD-AuNCs) four distinct emission wavelengths, enabling the highly accurate seven explosives. The sensor array leverages host-guest interactions between cyclodextrin ligands AuNCs' surface target explosives, generating unique fluorescence fingerprint patterns. Mechanistic studies reveal that enhancement CD-AuNCs attributed to ligand rigidification, while quenching primarily caused by photoinduced electron transfer responses are captured using smartphone, corresponding RGB values simultaneously extracted. To enhance recognition accuracy, dense convolutional network (DenseNet) algorithm advanced image capability integrated array. This achieves remarkable 100% accuracy at concentration 200 μM, precise visual classification proposed strategy not only provides powerful tool on-site explosive monitoring but also offers versatile intelligent detection diverse analytes, demonstrating potential real-world applications in environmental security monitoring.

Language: Английский

Gold nanocluster hybrid nanomaterials: Alloying and nanocomposite for fluorescence detection in food, environment, and healthcare DOI
Yi Zhao, Yang Song, Hengyi Xu

et al.

Chemical Engineering Journal, Journal Year: 2025, Volume and Issue: unknown, P. 161331 - 161331

Published: March 1, 2025

Language: Английский

Citations

0
Advanced anticounterfeiting polymer inks for high-level encryption and authentication technologies DOI

Reza Khalilzadeh,

Milad Babazadeh‐Mamaqani,

Moein Mohammadi‐Jorjafki

et al.

Progress in Materials Science, Journal Year: 2025, Volume and Issue: unknown, P. 101487 - 101487

Published: April 1, 2025

Language: Английский

Citations

0
A Self-Reportable Fluorescence Lighting-up Nanoherbicide with Minimized Off-Target Hazards DOI Creative Commons
Yuwei Jin, Yuan Xue,

Yijun Lu

et al.

Advanced Agrochem, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0
Using a Functional Wool Keratin Photoresist to Build Iridescent and Fluorescent 3D Micro‐Pattern for Dual‐Mode Optical Anti‐Counterfeiting DOI
Shuang Xia,

Qinghong Lu,

Chaoyu Fan

et al.

Small, Journal Year: 2025, Volume and Issue: unknown

Published: May 7, 2025

Abstract The construction of bio‐nanostructures plays a critical role in the advancement applications across bioelectronics, bio‐optical devices, and biomedicine. Among various fabrication techniques, soft lithography emerges as an efficient scalable method for producing high‐quality intricate surface micropatterns. Herein, mild aqueous approach is developed to endow biocompatible wool keratin (WK) with photoresponsiveness; utilize gold nanoclusters (AuNCs) incorporated functional bio‐photoresist build iridescent fluorescent micrometer‐scale patterns dual‐mode optical anti‐counterfeiting. Specifically, chemical modification WK achieved by using glycidyl methacrylate under conditions. And then, modified can function green bio‐photoresist, which be cross‐linked via UV light‐initiated radical polymerization. By combining lithography, both positive negative 3D micro‐patterns stability, biocompatibility, controlled degradability facilely fabricated. Notably, obtained periodic microstructures exhibit typical behavior excellent diffraction efficiency. Interestingly, reductant stabilizer, AuNCs resist significant fluorescence response situ generated. More importantly, through skillful combination fluorescence, WK/AuNCs‐based hybrid further used anti‐counterfeiting, significantly enhance information storage encryption security.

Language: Английский

Citations

0
Angle‐Dependent Dynamic Fluorescent Anti‐Counterfeiting of Multilayer Flexible Structure for High‐Capacity 3D Luminescent Barcodes DOI

Lixue Yang,

Xingyue Liu, Min Huang

et al.

Advanced Optical Materials, Journal Year: 2025, Volume and Issue: unknown

Published: May 13, 2025

Abstract Dynamic fluorescent anticounterfeiting, which converts external stimuli into intuitive color changes, faces challenges due to low encryption levels and complex stimulation requirements. Here, a multilayer flexible structure combining dielectric microsphere cavity arrays (MCA), quantum dot polymer composites films (QDs), polydimethylsiloxane (PDMS) enhance anticounterfeiting performance is presented. The MCA/QDs/PDMS/QDs (MQPQ) structure, fabricated by screen printing, exhibits angle‐dependent variations in both brightness under ultraviolet excitation. Four distinct MQPQ structures with different emissions realize widely tunable hue conversion. Experimental theoretical investigations reveal that the directional antenna effect significantly contributes optically variable mechanism. extended 3D dynamic barcodes, where stripe provide enhanced information storage. Up five barcode patterns can be read from angles, improving storage capacity security. This approach, requiring only single excitation light, offers simplicity, universality, advanced potential.

Language: Английский

Citations

0
Deep Learning-Assisted Sensor Array Based on Host–Guest Chemistry for Accurate Fluorescent Visual Identification of Multiple Explosives DOI

Wenxing Gao,

Zhibin Wang, Qiang Li

et al.

Analytical Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: May 17, 2025

Accurate and rapid discrimination of multiple explosives with high precision is paramount importance for national security, ecological protection, human health yet remains a significant challenge conventional analytical techniques. Herein, we present an innovative deep learning-assisted artificial vision platform based on cyclodextrin-protected multicolor fluorescent gold nanoclusters (CD-AuNCs) four distinct emission wavelengths, enabling the highly accurate seven explosives. The sensor array leverages host-guest interactions between cyclodextrin ligands AuNCs' surface target explosives, generating unique fluorescence fingerprint patterns. Mechanistic studies reveal that enhancement CD-AuNCs attributed to ligand rigidification, while quenching primarily caused by photoinduced electron transfer responses are captured using smartphone, corresponding RGB values simultaneously extracted. To enhance recognition accuracy, dense convolutional network (DenseNet) algorithm advanced image capability integrated array. This achieves remarkable 100% accuracy at concentration 200 μM, precise visual classification proposed strategy not only provides powerful tool on-site explosive monitoring but also offers versatile intelligent detection diverse analytes, demonstrating potential real-world applications in environmental security monitoring.

Language: Английский

Citations

0