Sequential application of Time of Flight-Secondary Ion Mass Spectrometry after vacuum metal deposition on glass, polyethylene terephthalate and polyvinyl chloride
Bibliographic record
Abstract
In forensic laboratories, a common and versatile process to develop fingerprints is vacuum metal deposition (VMD). In some instances, however, it creates the phenomenon of 'empty prints', where the only development on the surface is outside of the fingerprint area, yielding no ridge detail. Previous work has shown that Time of Flight-Secondary Ion Mass Spectrometry (ToF-SIMS) can enhance fingerprint recovery after ninhydrin, black powder suspension or cyanoacrylate stained with basic yellow 40 (standard processes used by forensic laboratories) on paper, stainless steel and polyethylene surfaces. ToF-SIMS has not yet been compared in sequence with VMD on non-porous surfaces. In particular, it has not been assessed to see if ridge detail can be enhanced following VMD development. This study aims to further inform forensic practitioners of when and how to incorporate ToF-SIMS into the fingermark development workflow. The main focus of the study is to assess the suitability of ToF-SIMS to enhance fingerprints deposited on two surfaces commonly problematic for VMD: polyethylene terephthalate (PET) and polyvinyl chloride (PVC). In this work, a fingerprint expert compared the friction ridge detail developed by VMD to the ridge detail after ToF-SIMS enhancement. Fingerprints were deposited on glass, PET and PVC, developed with VMD and then enhanced with ToF-SIMS. This work demonstrates that ToF-SIMS is compatible with VMD in sequential processing. Overall, in >83 % of samples, the ridge detail produced by ToF SIMS was at least equivalent to VMD. Importantly, ToF-SIMS was able to visualise ridge detail on all samples where VMD gave 'empty prints' or no visible development, which was on 75 % of all PVC samples. ToF-SIMS also overcame some background interferences (such as ink) that affected optical imaging of fingerprints following VMD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".