Effect of temperature on microbial communities in bentonite for use in engineered barrier systems
Bibliographic record
Abstract
Bentonite is an important component of deep geological repository (DGR) designs, where it will serve as a buffer between used fuel containers (UFCs) and subsurface rock walls of the repository. The potential for microbial activity in bentonite is being studied to understand the influence of microbial metabolisms (e.g., sulfate reduction by sulfate-reducing bacteria) on the long-term safety of the DGRs (e.g., through contributions to microbiologically influenced corrosion). Most studies of microorganisms in bentonite involved culturing microorganisms at 30°C or below, even though the placement room in a DGR is expected to experience elevated temperatures for up to one thousand years after used fuel placement. The purpose of this study was to test the abundance and community composition of microorganisms in as-received and hydrated bentonite at a range of DGR-relevant temperatures (15-105°C) using a combination of cultivation and DNA-based techniques. In certain clays tested, aerobic heterotrophs, anaerobic heterotrophs, and sulfate-reducing bacteria were culturable at 15, 30, 45, and 60°C from both the as-received and hydrated bentonite, demonstrating that the bentonite microbial community includes representatives capable of growth at a range of DGR-relevant temperatures. Although cultivation results showed no significant increase in the abundance of culturable microorganisms from as-received bentonite to hydrated bentonite at temperatures greater than 45°C, sequencing results for two bentonite samples hydrated and incubated at 60°C revealed that 16S rRNA gene profiles were dominated (>99%) by sequences associated with the putative thermophilic family Thermoactinomycetaceae, which was not detectable in the as-received bentonite starting material. Not only does this suggest that as-received bentonite harbours viable thermophiles, but it also highlights the importance of a multifaceted (e.g., cultivation coupled to DNA sequencing) approach to study microbial communities of bentonite.IMPORTANCEPredicting the abundances and types of microorganisms that may be active within a deep geological repository is critical to ensure that DGR design specifications minimize or prevent microbially mediated deterioration of DGR components. To date, research in this area has focused on the effect of bentonite dry density and the associated swelling pressure on suppression of microbial growth, but most of these experiments have been conducted at relatively low temperatures (e.g., 30°C). Studying the microbiology of bentonite exposed to elevated temperatures is critical given that a DGR is expected to experience high temperatures for up to one thousand years.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".