Solubility of bio-sourced feedstocks in ‘green’ solvents
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
A group of 14 different bio-sourced, renewable feedstocks (homoserine, 1; glutamic acid, 2; aspartic acid, 3; 2,5-furandicarboxylic acid, 4; fumaric acid, 5; oxalacetic acid, 6; tartaric acid, 7; malic acid, 8; succinic acid, 9; levulinic acid, 10; γ-hydroxybutyrolactone, 11; xylitol, 12; mannitol, 13; sorbitol, 14) have been examined for their solubility/miscibility in a variety of ‘green’ solvents, including water, supercritical carbon dioxide (scCO2), and ionic liquids. Two other bio-based compounds 5-hydroxymethylfurfural, 15, and D-xylose, 16, were studied in selected solvents. Trends in solubility have been assessed so that these data may be extrapolated to help predict solubilities of other related compounds. For example, 10, 11 and 15 all demonstrated appreciable solubility in scCO2, as they possess weak intermolecular interactions. The dicarboxylic acids studied (4–9) all proved soluble in modified scCO2 (by use of MeOH as a cosolvent). While the polyols (12–14) and 1 were insoluble in scCO2 but water of various pHs and ionic liquids proved adept at their dissolution. Some of the amino acids studied (2 and 3) were only soluble in water with an adjustment of pH.
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 | 0.000 |
| 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