Surface-specific performance of metal and metal oxide nanoparticles in latent fingerprint visualisation
Bibliographic record
Abstract
Latent fingerprint (LFP) visualisation remains a cornerstone method in forensic science, with ongoing developments aimed at enhancing clarity, sensitivity, and substrate compatibility. Due to their ability to tailor surface chemistry and optical properties, nanoparticles present a promising avenue for fingerprint development, especially on various types of surfaces. However, there has been a lack of understanding regarding the comparative behaviour of nanoparticles across different substrates. This review aims to address this gap by critically comparing the surface-specific performance of metal and metal oxide nanoparticles. In which we consider common nanoparticles for LFP development, such as Gold, Silver, silica, zinc oxide, Titanium dioxide, iron oxide, Copper oxide, and Aluminium oxide. Our review examines how various nanoparticles influence fingerprint residue on porous and non-porous surfaces and assesses their effectiveness in terms of clarity, durability using these nanoparticles. Our key finding of comparative analysis highlights that gold nanoparticles yield promising outcomes even on historically challenging porous substrates due to their affinity for sweat and amino acids. Conversely, zinc oxide and titanium dioxide exhibit superior fluorescence-based contrast on non-porous surfaces such as glass and plastics, as well as some porous surfaces. The rest of the nanoparticles were able to achieve their success on porous and non-porous surfaces with some limitations. We also outline diverse methods employed by various researchers, including dusting, brushing, spraying, and fluorescence imaging, while emphasising the role of substrate texture and the functionalization of nanoparticles. The review provides insights using comparative tables on selecting effective nanoparticle-based methods for specific forensic contexts to achieve more stable and universal fingerprint recovery in criminal investigations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".