Identification of darkened pigments in cultural objects by graphite furnace atomic absorption spectroscopy and inductively coupled plasma-mass spectrometry
Bibliographic record
Abstract
Inorganic pigments in paint have been used by artists throughout history, primarily because of their resistance to chemical change over long periods of time. Many of the historically-important pigments consist of transition metal oxides, sulfides or carbonates. The determination of metals in pigments is a powerful tool that can be used by conservators and researchers to identify pigments. In this study, pigments were sampled using a dry cotton bud that was contacted lightly with the surface of painted objects to remove a small quantity of pigment (<1 μg). This sampling approach causes no visible damage to a paint surface. Evidence of contact cannot be detected visually, even with the aid of a magnifying glass. Prior to analysis, pigment metals were extracted from the cotton with concentrated HNO3 in a 2 ml polystyrene beaker. The pigment metals were determined using graphite furnace atomic absorption spectroscopy (GFAAS) and electrothermal vaporization inductively coupled plasma mass spectrometry (ETV-ICP-MS). The small sample size requirement and high sensitivity of these instruments make them very suitable for analyzing pigment metals. Solution nebulization SN-ICP-MS was also used for obtaining a mass scan for most elements in the periodic table. The application of both GFAAS and ICP-MS is described for determining the identity of the metals in different pigments of two cultural objects (a painting and a map), which had darkened over time. Raman spectroscopy was used for confirming the identity of a darkened red pigment in a map as Red Lead (Pb3O4).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".