Latent fingerprints are invisible impressions that need to be developed before being used for individual identification. To advance fingerprint powdering, which is the most common method of developing latent fingerprints on nonporous substrates, two types of green-light-excited carbon dots (CDs) were synthesized hydrothermally using rhodamine 6G (R6G) and rhodamine B (RB) as the precursors. Under the physical effects of capillary attraction and coffee-ring effect, they were dispersed into hierarchical porous micron-sized diatomite (DE) in a facile, cost-effective, and eco-friendly way. The evolution of fluorescence characteristics from water-dispersed CDs to solid CDs/DE composite powders is clarified from the perspectives of physical properties and microstructures of the CDs and diatomite matrix. Optimized green fluorescent R6G-CDs/DE and yellow fluorescent RB-CDs/DE can be excited by visible light (green region) and show very high performance in fingerprint development under the luminescence modes, which has low phototoxicity and helps to preserve chemical information in fingerprint residues. Furthermore, image enhancement protocols, called “digital filters”, established based on qualitative and quantitative analyses of red, green, and blue (RGB) channel images of fluorescent fingerprints developed with the CDs/DE composite powders are very efficient in eliminating background interferences of challenging surfaces such as multicolored or patterned substrates. The as-prepared new materials for fingerprint development and the created methods for image enhancement show great promise in practical application scenarios.
Jiujiang Wang,Dawu Li,Jianghua Zhang,Zijian Gao,Jinke Han
Latent fingerprints (LFPs) are invisible impressions that need to be developed before being used for criminal investigation; however, existing fingerprint visualization techniques face challenges, such as complex preparation and poor contrast. To advance practical fingerprint detection, green-emissive micron-sized curcumin/kaolin composites were synthesized via a facile and cost-effective one-step physical cross-linking method, which exhibited unprecedented performance in developing diversified marks, including LFPs, knuckle prints, palm prints, and footprints, with clear three-level details on various substrates. Notably, the powders successfully developed LFPs that were aged for 30 days and even up to 100 days, meeting the stringent requirements for comprehensive forensic application. Afterward, a novel method, termed Fingerprint Fluorescence Intensity Ratio (FFIR), was developed to quantify the contrast between fingerprint signals and background noise and to compare the efficacy of full-color developing agents. Compared with the existing grayscale conversion strategy, the proposed FFIR method achieved tunable multi-color fingerprint image enhancement for the first time, which helped to eliminate background fluorescence interference and improved visual perception. The feasibility of FFIR and its sensitivity in tracking image capture parameters were demonstrated by the established mathematical model. Hence, the newly synthesized modified composites and the mathematical model-validated method demonstrate profound practical significance in comprehensive fingerprint imaging.
Point-of-care testing (POCT) is currently the predominant method of in vitro diagnostics, with lateral flow immunoassay(LFIA) being commonly utilized in POCT. The performance of the probe in LFIA will directly affect the results of the detection. Therefore, the selection of probes is particularly important. However, the current commercially produced probes generally suffer from short luminescence lifetime, high detection cost, and poor anti-interference capability. Meanwhile, the traditional detection system has difficulties in detecting weakly positive samples. These ultimately lead to the problem of poor detection sensitivity. In order to address the above issues, we have synthesized an ultralong organic phosphorescence nanoprobe (UOPN). The UOPNs exhibit a long afterglow phosphorescence signal of around 300 ms after being excited, which can be used to eliminate endogenous interference. Meanwhile, focusing on the problem of poor detection of weakly positive samples in conventional detection systems, and the weak inherent signal of UOPNs, resulting in a low signal-to-noise ratio, we have developed a detection system that combines image processing and neural network-driven analysis for long afterglow image analysis. Through the analysis of the long afterglow images of serum amyloid A (SAA) in human blood, a detection limit of 0.01 μ g / mL and a linear range of 0.1–251 μ g / mL were achieved. The whole analysis was completed within 5 minutes. After pre-processing with the denoising algorithm, the system demonstrated an impressive classification accuracy of 99.24 %, validating the reliability of the detection system. This approach has further enhanced the sensitivity of immunoassay, contributing to the advancement of point-of-care testing.
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