Constraining lithium-clay equilibria in sedimentary environments using a new thermodynamic dataset
Bibliographic record
Abstract
Lithium-rich formation brines from sedimentary basins are emerging as key unconventional resources in response to the growing global demand for lithium. This study integrates geochemical data from diverse settings, including the Smackover and Edwards Formations (Gulf Coast, USA), the Alberta Basin (Canada), and Salsomaggiore (Northern Apennine, Italy), to investigate the role of diagenetic processes and clay mineral equilibria on lithium mobility and retention. A new thermodynamic dataset was developed for lithium-bearing clay minerals and jadarite, allowing the construction of activity diagrams, calculation of saturation indices, and modeling. Activity diagrams indicate progressive brine evolution from kaolinite to montmorillonite, and toward Mg-rich saponite/chlorite assemblages, consistent with advanced diagenetic stages and lithium uptake into octahedral sites. The transition from equilibrium with smectites to chlorite-like phases reflects increasing temperature and prolonged water-rock interactions. A hyperalkaline paleo-fluid in equilibrium with jadarite and associated phases was also modeled, indicating that lithium concentrations in the Jadar Basin may have reached levels comparable to those currently observed in the Salar de Atacama. These findings underscore the dual role of clay minerals as buffers and potential sources for lithium in sedimentary systems, providing new insights for exploration and geochemical modeling of lithium-rich formation brines. • Developed a new thermodynamic dataset for Li-bearing clays and jadarite • Modeled brine evolution toward Li-rich chlorite and saponite equilibria • Predicted Li content in Jadar paleo-fluids rivaling salar values
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".