Advancing ethanol quality amidst a pandemic: a case study from Saskatchewan
Bibliographic record
Abstract
During the COVID-19 pandemic, Canada faced a significant challenge addressing the demand for hand sanitizers. Despite having a thriving ethanol industry, the country was not equipped to address this need. The automative ethanol sector in Canada produces lower quality ethanol containing significant impurities that make their products unacceptable for use in hand sanitizers. Meanwhile, distilleries that produce higher grades of ethanol could not keep pace with the unprecendented demand. To address this issue, the Saskatchewan Structural Sciences Centre was employed to develop quality assurance methods for upgrading fuel-grade ethanol into higher grade alcohol, suitable for hand sanitizer. Through partnerships with various stakeholders, we successfully purified fuel-grade ethanol to comply with Health Canada guidelines. Our approach involved several strategies to enrich fuel-grade ethanol, including ozonation, alkaline salt and copper sulfate treatments, and carbon filtration. We also optimized the conditions of the distillation column to further reduce fusel oils to acceptable levels. While acetals and acetaldehyde posed a challenge in the purification process, a combination of amine treatments and optimized distillation were effective in decreasing these impurities to compliant levels. This review not only discusses the potential for Saskatchewan's ethanol producers to enrich fuel-grade ethanol to a higher quality but also highlights how the adversities of the pandemic precipitated innovative research applications. These efforts fostered collaborations between academia, government, and industry, addressing urgent needs, advancing economic sustainability, and transforming a crisis into an opportunity for rapid scientific progress and efficient resource optimization, all while promoting the principals of a circular economy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".