Location, speciation, and quantification of carbon in silica phytoliths using synchrotron scanning transmission X-ray microspectroscopy
Bibliographic record
Abstract
Phytoliths of biogenic silica play a vital role in the silicon biogeochemical cycle and occlude a fraction of organic carbon. The location, chemical speciation, and quantification of this carbon within phytoliths have remained elusive due to limited direct experimental evidence. In this work, phytoliths (bilobate morphotype) from the sugarcane stalk epidermis are sectioned with a focused ion beam to produce lamellas (≈10 × 10 μm2 size, <500 nm thickness) and probed by synchrotron scanning transmission X-ray microspectroscopy (≈100-200 nm pixel size; energies near the silicon and carbon K-absorption edges). Analysis of the spectral image stacks reveals the complementarity of the silica and carbon spatial distributions, with carbon found at the borders of the lamellas, in islands within the silica, and dispersed in extended regions that can be described as a mixed silica-carbonaceous matrix. Carbon spectra are assigned mainly to lignin-like compounds as well as to proteins. Carbon contents of 3-14 wt.% are estimated from the spectral maps of four distinct phytolith lamellas. The results provide unprecedented spatial and chemical information on the carbon in phytoliths obtained without interference from wet-chemical digestion.
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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 | 0.000 |
| 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".