Sorption Modelling of Np(IV), Np(V), and Pd(II) on Illite and Montmorillonite Under High Ionic Strength Conditions
Bibliographic record
Abstract
A database is being developed by the NWMO that will be capable of predicting sorption for key elements of interest onto crystalline and sedimentary rocks in the highly saline groundwaters and porewaters at DGR depths in Ontario, Canada. Pd(II), Np(IV), and Np(V) have all been identified as elements of interest by the NWMO. Sorption experiments of Pd(II) were conducted on illite, bentonite, and shale to investigate the effects that ionic strength (I), and pH have on sorption. Na-Ca-Cl solutions ranging from 0.01 – 6.0 M I with pH values between 3 and 9 were considered under aerobic conditions at 25°C for sorption testing conditions. Sorption data for Pd(II), Np(IV), and Np(V) was used with 2SPNE SC and 2SPNE SC/CE models developed in PHREEQC to model sorption onto illite and montmorillonite at different values of I and pH. These models were also used to test proposed reaction constants from various sources in the existing literature, and to establish and propose optimized surface complexation constants of our own. Optimized reaction constants were compared with the LFER to determine its validity across all test conditions. The LFER was found to not adequately describe sorption of Pd(II), Np(IV), or Np(V) on either illite or montmorillonite. The JAEA TDB, R04, and R07 THEREDA were all tested along with the Davies, Debye-Hückel, SIT, and Pitzer computational methods in order to determine their overall accuracy. The JAEA TDB using the SIT computational method was found to not be capable of modelling Np(IV) onto illite or montmorillonite in 4.0 M I.
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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 | 0.001 | 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) | 0.012 | 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".