Adsorption of Se(-II) and Tc(IV)
Bibliographic record
Abstract
Canada is in the process of implementing a Deep Geologic Repository (DGR) to dispose of used nuclear waste. Adsorption behaviour of both Se(-II) and Tc(IV) onto granite, shale, limestone, illite, and MX-80 bentonite has been elucidated. Se(-II) adsorption onto granite and MX-80 bentonite displays a decrease in Rd with an increase in solution pH. Se(-II) adsorption onto granite decreases with an increase in solution ionic strength. Se(-II) adsorption onto MX-80 bentonite does not return evidence which supports an apparent effect due to the ionic strength. Tc(IV) adsorption onto shale, limestone, illite, and MX-80 bentonite remains constant as the solution pH increases. Ionic strength does not affect the magnitude of Tc(IV) adsorption across the adsorbents, however an increase in ionic strength accelerates Tc(IV) adsorption. Se(-II) surface complexation models are best simulated with the following surface complexes: ≡Feldspar_sSe-, ≡Biotite_sOH2HSe, ≡Albite_sSe-, ≡Montmorillonite_sSe-, and ≡Montmorillonite_sOH2HSe. Tc(IV) adsorption is best simulated with: ≡Biotite_sOTcO(OH), ≡Quartz_sOTcO(OH), (≡Feldspar_sOH)2TcO(OH)-, ≡Montmorillonite_sOTcO(OH), (≡Albite_sOH)2TcO(OH)-, ≡Illite_sOTcO(OH), and ≡Chlorite_sOTcO(OH). Se(-II) adsorption onto granite and MX-80 bentonite in CR-10 solution returns Rd values of (1.80 ± 0.10) m3∙kg-1 and (0.47 ± 0.38) m3∙kg-1, respectively. Tc(IV) adsorption onto granite and MX-80 bentonite in CR-10 solution returned Rd values of (1.47 ± 0.25) m3∙kg-1 and (2.19 ± 0.33) m3∙kg-1, respectively. Tc(IV) adsorption onto shale, limestone, illite, and MX-80 bentonite in SR-270-PW solution returned Rd values of (0.16 ± 0.10) m3∙kg-1, (0.44 ± 0.21) m3∙kg-1, (1.86 ± 0.44) m3∙kg-1, and (0.23 ± 0.10) m3∙kg-1, respectively. This thesis will further deepen the understanding of Se(-II) and Tc(IV) adsorption.
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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) | 0.042 | 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".