Surface-enhanced Raman spectroscopy analysis of house paint and wallpaper samples from an 18th century historic property
Bibliographic record
Abstract
Conservation efforts for heritage buildings require a substantial knowledge of the chemical makeup of materials that were used throughout the lifetime of the property. In particular, conservators are often concerned with the identification of colorants used in both interior and exterior wall treatments (paint, wallpaper, etc.) in order to gain perspective into how the building may have appeared during a certain time period in its existence. Ideally, such an analysis requires a technique that provides molecular level information as to the identity of the colorant as well as other sample components (binders, fillers, etc.), which is useful for dating purposes. In addition, the technique should be easily applied to paint layer samples which can be extremely thin and fragile. Herein we report the surface-enhanced Raman spectroscopy (SERS) analysis of paint and wallpaper samples taken from exterior and interior surfaces of a historic building. Several pigments were identified in the samples, which ranged from early inorganic pigments (lead white, barium sulfate, calcium carbonate, anhydrous chromium(III) oxide) which have been used in house paints for centuries, to a more modern pigment (phthalocyanine blue), developed in the middle of the 20th century. This analysis highlights the usefulness of SERS in such a conservation effort, and demonstrates for the first time pigment identification in house paints and wallpaper using SERS, which has far-reaching implications not only in the field of conservation, but also in forensics, industrial process control, and environmental health and safety.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".