Fluorescent Probe for Picric Acid Detection via Deep Learning
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Solution Overview
Problem
Current fluorescence sensing technologies for detecting nitro explosives are limited by the need for sophisticated optical devices and trained technicians, leading to inefficiencies in detection speed, accuracy, and portability.
Innovation Solution
A small-molecule probe based on fluorescence sensing, specifically designed to detect picric acid (PA) in explosives, combined with a portable detection platform and deep learning algorithms for image processing, enabling rapid and accurate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fluorescence sensing technologies are used to detect nitro explosives, then detection accuracy and visualization are improved, but device complexity and operational difficulty increase due to sophisticated optical devices and trained technicians being required
Solution Approach 1:
The patent employs disposable fluorescent probe strips that can be easily discarded after use, eliminating the need for expensive, complex optical devices. The probes are designed as single-use consumables that provide sufficient detection capability without requiring sophisticated instrumentation, thereby reducing device complexity while maintaining detection accuracy.
Solution Approach 2:
The patent replaces complex optical detection systems with a fluorescence-based chemical sensing approach. Instead of using sophisticated optical devices for detection, the system utilizes fluorescent probes that undergo chemical reactions with nitro explosives, producing detectable fluorescence signals that can be captured by simple imaging devices rather than complex optical instrumentation.
2Reliability
If fluorescence sensing technologies are used for explosive detection, then detection capability is improved, but detection time increases due to complex spectral information processing
Solution Approach 1:
The patent extracts and focuses on a specific wavelength range (blue light region) for fluorescence detection, filtering out unnecessary spectral information. By concentrating the detection on the most informative wavelength range where nitro explosives produce characteristic fluorescence signals, the system reduces the complexity of spectral analysis and accelerates detection time while maintaining reliability.
Solution Approach 2:
The patent changes the detection parameter from comprehensive spectral analysis to specific wavelength intensity measurement. Instead of processing complex spectral information across multiple wavelengths, the system measures fluorescence intensity at specific wavelengths where nitro explosives exhibit characteristic signals, thereby simplifying the detection process and reducing analysis time.
3Ease of operation
If traditional RGB value reading method is used for image processing, then ease of operation is maintained, but processing efficiency and accuracy decrease
Solution Approach 1:
The patent introduces deep learning algorithms as an intermediary between the captured images and the detection results. The deep learning model automatically processes the fluorescence images, extracts relevant features, and identifies nitro explosives without requiring manual intervention or complex processing steps. This intermediary system maintains ease of operation while dramatically improving processing efficiency and accuracy compared to traditional RGB value reading methods.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves rapid and highly-sensitive detection of PA in explosives, with a detection limit of 1 mg/mL, and facilitates efficient real-time monitoring, improving detection speed, accuracy, and portability.
Implementation Method 1
The small-molecule probe based on fluorescence sensing... achieves rapid and highly-sensitive detection of PA in explosives
Data Source
AI summary
The intelligent real-time monitoring system for nitro explosives in the present disclosure adopts a PP-YOLO algorithm, and can capture. Combining fluorescence and colorimetric images with the assistance of an optical camera. The fluorescent probe TPE-J is designed and synthesized for the dosage-sensitive and visual detection of nitro explosives. The electron transfer between picric acid (PA) and the probe causes a specific response that the original blue fluorescence is rapidly quenched to non-luminescence within 5 s, with a detection limit as low as 1 mg/mL. A color change can be integrated into an optical camera for capture and quantization, and the resulting image data is automatically processed by a deep learning algorithm platform. The sensing system facilitates the efficient real-time monitoring and highly-sensitive detection of PA in various scenarios. The fluorescence sensing-based detection platform with deep learning provides a new perspective for the efficient portable detection of explosives.