Heteroatom-Tuned Carbon Dots for Multimode High-Resolution Fluorescence Imaging of Latent Imprints
Bibliographic record
Abstract
Fingerprints are recognized as one of the most reliable biometric features in forensic science. High-quality fingerprint imaging facilitates the extraction of essential personal information during examination and analysis. Here, we utilized the transfer mechanism between precursors and carbon dots (CDs) to synthesize wavelength-tunable fluorescent nanoparticles for developing latent fingerprints (LFPs). Experimental and theoretical results revealed that the precise design of the heteroatom-doped structure of carbon sources enabled the sequential introduction of heteroatoms to CDs, from oxygen alone, to bromine, chlorine, and finally nitrogen, which induced a gradual reduction of energy level gaps, thereby realizing adjustable fluorescence from green to red (538-612 nm). By combining CDs with diatomite, the resulting composite powders, available as multicolor fluorescence and daylight developers, demonstrated high sensitivity and selectivity for detecting both fresh and aging LFPs. Using a typical powdering method with CDs/diatomite stored for 30 days, level 1-3 fingerprint details were visualized with high contrast, and diversified latent imprints deposited on various substrates, including knuckle prints, palmprints, and footprints, were clearly developed. Furthermore, the developed multicolor enhancement technique ensured high-resolution details and overcame the challenges of poorly developed fingerprints. Thus, the proposed multimode framework establishes a viable platform for practical imprint imaging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".