Multispecies Biofilms Transform Selenium Oxyanions into Elemental Selenium Particles: Studies Using Combined Synchrotron X-ray Fluorescence Imaging and Scanning Transmission X-ray Microscopy
Bibliographic record
Abstract
Selenium (Se) is an element of growing environmental concern, because low aqueous concentrations can lead to biomagnification through the aquatic food web. Biofilms, naturally occurring microbial consortia, play numerous important roles in the environment, especially in biogeochemical cycling of toxic elements in aquatic systems. The complexity of naturally forming multispecies biofilms presents challenges for characterization because conventional microscopic techniques require chemical and physical modifications of the sample. Here, multispecies biofilms biotransforming selenium oxyanions were characterized using X-ray fluorescence imaging (XFI) and scanning transmission X-ray microscopy (STXM). These complementary synchrotron techniques required minimal sample preparation and were applied correlatively to the same biofilm areas. Sub-micrometer XFI showed distributions of Se and endogenous metals, while Se K-edge X-ray absorption spectroscopy indicated the presence of elemental Se (Se 0 ). Nanoscale carbon K-edge STXM revealed the distributions of microbial cells, extracellular polymeric substances (EPS), and lipids using the protein, saccharide, and lipid signatures, respectively, together with highly localized Se 0 using the Se L III edge. Transmission electron microscopy showed the electron-dense particle diameter to be 50–700 nm, suggesting Se 0 nanoparticles. The intimate association of Se 0 particles with protein and polysaccharide biofilm components has implications for the bioavailability of selenium in the environment.
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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) | 0.000 | 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 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".