Ongoing Novel Critical Metals Recovery from Coal Ash.pdf
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.
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 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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; both teacher heads agree on what is shown here.
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".