Noninvasive Synchrotron-Based X-ray Raman Scattering Discriminates Carbonaceous Compounds in Ancient and Historical Materials
Bibliographic record
Abstract
Carbon compounds are ubiquitous and occur in a diversity of chemical forms in many systems including ancient and historic materials ranging from cultural heritage to paleontology. Determining their speciation cannot only provide unique information on their origin but may also elucidate degradation processes. Synchrotron-based X-ray absorption near-edge structure (XANES) spectroscopy at the carbon K-edge (280-350 eV) is a very powerful method to probe carbon speciation. However, the short penetration depth of soft X-rays imposes stringent constraints on sample type, preparation, and analytical environment. A hard X-ray probe such as X-ray Raman scattering (XRS) can overcome many of these difficulties. Here we report the use of XRS at ∼6 keV incident energy to collect carbon K-edge XANES data and probe the speciation of organic carbon in several specimens relevant to cultural heritage and natural history. This methodology enables the measurement to be done in a nondestructive way, in air, and provides information that is not compromised by surface contamination by ensuring that the dominant signal contribution is from the bulk of the probed material. Using the backscattering geometry at large photon momentum transfer maximizes the XRS signal at the given X-ray energy and enhances nondipole contributions compared to conventional XANES, thereby augmenting the speciation sensitivity. The capabilities and limitations of the technique are discussed. We show that despite its small cross section, for a range of systems the XRS method can provide satisfactory signals at realistic experimental conditions. XRS constitutes a powerful complement to FT-IR, Raman, and conventional XANES spectroscopy, overcoming some of the limitations of these techniques.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".