Ongoing Novel Critical Metals Recovery from Coal Ash
Bibliographic record
Abstract
Novel Critical Metals Recovery Techniques from Coal Ash Authors Mr. Claudio Arato - Canada - SonoAsh Abstract Abstract Summary This presentation will focus on the new and emerging areas of interest for SonoAsh. While Critical metals recovery has always been part of the SonoAsh process, this presentation will specifically focus on our new, patent pending methodology to extract Lithium ions from coal ash using a proprietary membrane grafting techniques developed in collaboration with Dr. Mohamad Al-Sheikhly’s at the University of Maryland, College Park. The technology is in the state of optimization and studies are being performed to determine the maximum extraction capacities of these membranes. Furthermore, striping methodologies are currently being developed to determine how the extracted Lithium can be further recovered from the membranes. The selective nature of the membrane assembly through innovative surface coordination chemistry techniques for Lithium separation demonstrates a bespoke pathway to more efficient singular Lithium recovery strategies and the potential for an additional business opportunity for the coal ash industry. From the development program to date, eight unique polymer membranes have been developed using an electron beam direct grafting method requiring irradiation by a linear accelerator at the National Institute for Science and Technology (NIST) in Bethesda, MD, with the specific goal of capturing and separating Lithium salts more selectively. This program was informed by the results and knowledge obtained from a previous membrane development program for the unique recovery of Uranium from sea water. Results are expected to be complete and available for presentation during WOCA 2024.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".