Overcoming clay structure challenges in lithium recovery from boron waste using high‐temperature pressure acid leaching
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Boron mines contain significant amounts of lithium along with boron. After boron is extracted, lithium remains in the waste, which has a carbonate‐hosted clay‐type structure, along with other impurities. The scarcity of lithium resources and the increasing need for lithium worldwide make such resources economically important. Although the best hydrometallurgical method for the recovery of lithium trapped within the clay‐structured mineral resources is roasting with chemicals to disrupt the clay structure and acid leaching, the process is quite difficult and costly due to the high energy and chemical addition requirements. To overcome this challenge, this study proposed a high‐temperature–pressure sulphuric acid leaching process to recover lithium from the boron waste. Under the optimized conditions (liquid/solid ratio: 10, acid concentration: 1 M, temperature: 150°C, and contact time: 120 min), 100% of lithium was leached. The leaching mechanism was determined through mineral characterization (X‐ray diffractometry [XRD], X‐ray fluorescence spectrophotometer [XRF], scanning electron microscopy–energy‐dispersive X‐ray spectroscopy [SEM–EDX], Mastersizer), and a shrinking core heterogeneous kinetics model. It was found that high‐temperature–pressure sulphuric acid leaching disrupted clay structure and promoted the leaching of lithium, the leaching kinetics fit the shrinking core heterogeneous kinetics model, and was controlled by a dual mechanism with ash diffusion and chemical reactions on the particle surface. The reaction rate constants increased with increasing temperature, and the activation energy was found to be 32.17 kJ/mol.
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.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it