3D Chemical Mapping: Application of Scanning Transmission (Soft) X-ray Microscopy (STXM) in Combination with Angle-Scan Tomography in Bio-, Geo-, and Environmental Sciences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The identification of environmental processes and mechanisms often requires information on the organochemical and inorganic composition of specimens at high spatial resolution. X-ray spectroscopy (XAS) performed in the soft X-ray range (100-2,200 eV) provides chemical speciation information for elements that are of high biogeochemical relevance such as carbon, nitrogen, and oxygen but also includes transition metals such as iron, manganese, or nickel. Synchrotron-based scanning transmission X-ray microscopy (STXM) combines XAS with high resolution mapping on the 20-nm scale. This provides two-dimensional (2D) quantitative information about the distribution of chemical species such as organic macromolecules, metals, or mineral phases within environmental samples. Furthermore, the combination of STXM with angle-scan tomography allows for three-dimensional (3D) spectromicroscopic analysis of bio-, geo-, or environmental samples. For the acquisition of STXM tomography data, the sample is rotated around an axis perpendicular to the X-ray beam. Various sample preparation approaches such as stripes cut from TEM grids or the preparation of wet cells allow for preparing environmentally relevant specimens in a dry or in a fully hydrated state for 2D and 3D STXM measurements. In this chapter we give a short overview about the principles of STXM, its application to environmental sciences, different preparation techniques, and the analysis and 3D reconstruction of STXM tomography data.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it