Calculating absorption dose when X-ray irradiation modifies material quantity and chemistry
Bibliographic record
Abstract
X-ray absorption is a sensitive and versatile tool for chemical speciation. However, when high doses are used, the absorbed energy can change the composition, amount and structure of the native material, thereby changing the aspects of the absorption process on which speciation is based. How can one calculate the dose when X-ray irradiation affects the chemistry and changes the amount of the material? This paper presents an assumption-free approach which can retrieve from the experimental data all dose-sensitive parameters - absorption coefficients, composition (elemental molecular units), material densities - which can then be used to calculate accurate doses as a function of irradiation. This approach is illustrated using X-ray damage to a solid film of a perfluorosulfonic acid fluoropolymer in a scanning transmission soft X-ray microscope. This new approach is compared against existing dose models which calculate the dose by making simplifying assumptions regarding the material quantity, density and chemistry. While the detailed measurements used in this approach go beyond typical methods to experimental analytical X-ray absorption, they provide a more accurate quantitation of radiation dose, and help to understand mechanisms of radiation damage.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".