A surface complexation model for simulating sorption of rare earth elements onto goethite in low ionic strength (I = 0.01 M) aqueous solutions for use in groundwater flow systems
Bibliographic record
Abstract
We conducted batch adsorption experiments to examine the adsorption behavior of the 14 naturally occurring lanthanides and yttrium (Y) onto synthetic goethite (α-FeOOH), focusing on the interactions that govern rare earth element (REE) and Y retention in natural environments. Goethite is among the most common Fe(III) oxide/oxyhydroxides in aquifer and sedimentary systems (van der Zee et al., 2003), yet few surface complexation model (SCM) has previously been developed specifically for REE sorption onto this mineral. To fill this gap, we generated adsorption edges and isotherms across a pH range of 2.5 to 10.5 and used these data to parameterize a generalized double-layer SCM. The model accounts for both strong and weak surface sites binding of free REE 3+ (≡FeOLn 2+ ) and weak carbonate complex (≡FeOLnCO₃ + ). The mass action reactions capture the adsorption trends and fractionation patterns (La-Lu) observed experimentally with log K M ∗ ranging from 10.01 to 9.73 for strong sites (≡(s)FeOLn 2+ ), 3.81 to 3.39 for weak sites (≡(w)FeOLn 2+ ), and − 0.52 to −0.33 for weak site carbonate complexation (≡(w)FeOLnCO₃ + ). These results provide the calibrated framework for predicting REE partitioning onto goethite, which is increasingly relevant as REEs driven by their widespread use in high-technology and green energy industries are recognized as emerging environmental contaminants (e.g., Kulaksiz & Bau, 2013; Gwenzi et al., 2018). By quantifying how pH, carbonate speciation (Cantrell & Byrne, 1987; Byrne & Kim, 1990), and surface site heterogeneity govern REE adsorption, this study establishes a transferable SCM for application to groundwater, sediment, and early diagenetic environments, where Fe-oxyhydroxides control REE cycling and mobility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".