Synchrotron-Based Chemical Nano-Tomography of Microbial Cell-Mineral Aggregates in their Natural, Hydrated State
Bibliographic record
Abstract
Chemical nano-tomography of microbial cells in their natural, hydrated state provides direct evidence of metabolic and chemical processes. Cells of the nitrate-reducing Acidovorax sp. strain BoFeN1 were cultured in the presence of ferrous iron. Bacterial reduction of nitrate causes precipitation of Fe(III)-(oxyhydr)oxides in the periplasm and in direct vicinity of the cells. Nanoliter aliquots of cell-suspension were injected into custom-designed sample holders wherein polyimide membranes collapse around the cells by capillary forces. The immobilized, hydrated cells were analyzed by synchrotron-based scanning transmission X-ray microscopy in combination with angle-scan tomography. This approach provides three-dimensional (3D) maps of the chemical species in the sample by employing their intrinsic near-edge X-ray absorption properties. The cells were scanned through the focus of a monochromatic soft X-ray beam at different, chemically specific X-ray energies to acquire projection images of their corresponding X-ray absorbance. Based on these images, chemical composition maps were then calculated. Acquiring projections at different tilt angles allowed for 3D reconstruction of the chemical composition. Our approach allows for 3D chemical mapping of hydrated samples and thus provides direct evidence for the localization of metabolic and chemical processes in situ.
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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 | 0.000 | 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) | 0.001 | 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".