Selective adsorption and separation of stevioside and rebaudioside A by a metal-organic framework with boronic acid
Bibliographic record
Abstract
The boronic acid functionalization metal-organic frameworks (MOFs), as unique boronate affinity adsorbents, have desired specific molecular affinity for the separation and enrichment of cis-diol-compounds. Herein, the boronic acid functionalized Zn-based MOF adsorbent (MOF-BA) was synthesized through a simple one-step microwave method and used for the recognition and isolation of steviol glycosides (SGs). This MOF-BA exhibits the same spherical structure and isostructure with the parent framework composed only of the primitive ligand as verified by SEM and XRD characterization. It was confirmed that changing the ratio of ligands could achieve the adjustability of the boron content in the framework. At the same time, the MOF-BA-1.0 showed a suitable pore size (4.69 nm), and the presence of boric acid functional groups showed favorable selectivity for stevioside (STV). The static adsorption results showed that adsorption performances of rebaudioside A (RA) and STV from crude sugar solution (5.0 mg mL −1 , pH 8) on MOF-BA-1.0 were investigated at 303 K for 15 h. The adsorption capacities for STV and RA were 42.93 mg g −1 and 22.96 mg g −1 , respectively, and the adsorption selectivity (α STV/RA ) reached 4.35. The adsorption isotherm and kinetic data of MOF-BA-1.0 for RA and STV obeyed the Langmuir isotherm model and pseudo second order kinetic model, respectively. The study demonstrated that MOF-BA-1.0 adsorbent could be used as a potential adsorbent to purify the active ingredients of stevia and obtain a high concentration of RA products.
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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) | 0.001 | 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 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".