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 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".