Bibliographic record
Abstract
Recently, the demand for lithium metal and its associated compounds has been growing exponentially, mainly due to the increased consumption of lithium ion batteries. Consequently, to meet this demand, minerals such as spodumene have become the most important lithium-bearing resources. Although numerous methods have been studied for the extraction of lithium from spodumene, the conventional process of spodumene decrepitation followed by leaching in sulfuric acid, remains the proven commercial process. In the high temperature decrepitation process, α-spodumene is converted into β-spodumene and also some intermediate γ-spodumene can form. In the current research, a comprehensive thermodynamic analysis of the decrepitation of spodumene has been performed using HSC Chemistry® 7.1. Firstly, the thermodynamic data available in the literature for the various relevant lithium aluminosilicates was evaluated and then this data was incorporated into the HSC data base. Secondly, using the experimental data available in the literature, the non-ideal behaviour of spodumene was accounted for by the incorporation of activity coefficients. Finally, the model was applied to the decrepitation of both pure spodumene and also a spodumene concentrate. The modelled conversion results were in good agreement with the process data available in the literature.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".