Purification of rutin by supercritical fluid simulated moving bed chromatography
Bibliographic record
Abstract
Abstract Recent studies have shown that rutin (Quercetin‐3‐O‐rhamnosylglucoside) may have an inhibitory effect on COVID‐19. Rutin can be extracted from Tartary buckwheat as an active pharmaceutical ingredient. Nevertheless, its purification is mainly hindered by Kaempferol‐3‐O‐rutinoside (K3R) due to their similar molecular structures. This study intends to propose a simulated moving bed (SMB) chromatography process of rutin and K3R to achieve continuous production. True moving bed (TMB) and SMB models were established to numerically analyze and optimize this process. The system consists of a four‐zone SMB with two columns in each zone. The effects of the switch interval, feed flowrate, desorbent flowrate, extract flowrate, raffinate flowrate, and recycle flowrate on the purity and yield of rutin and K3R were investigated and the optimized conditions were chosen as 5 min, 3.5, 40, 34, 9.5, and 24.5 L/min, respectively. Consequently, the purities of 99.64% and 99.25%, and the yields of 99.81% and 99.37% for rutin and K3R were obtained, respectively. The simulation results can provide a guidance for the future industrial application of SF‐SMB to separate rutin and K3R.
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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 | 0.000 | 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) | 0.001 | 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 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".