Nuclear Magnetic Resonance Investigation of the Fractionation of Water–Ethanol Mixtures with Cellulose and Its Cross-Linked Biopolymer Forms
Bibliographic record
Abstract
Cellulose (CE) was cross-linked with epichlorohydrin (EPI) at variable compositions, and the fractionation properties were investigated in binary water–ethanol (W–E) solutions, including the pure solvent systems. The relative uptake of each solvent was measured using quantitative 1 H nuclear magnetic resonance (qNMR) spectroscopy. This study highlights the utility of qNMR as a rapid screening method for estimation of solvent selective fractionation in binary mixtures. The uptake properties of CE–EPI cross-linked polymers with ethanol and water were well-described using the Sips isotherm model. Modeling shows that the monolayer surface coverage ( Q m ) of ethanol and water onto the polymer materials covers a range (1.13–2.44 g/g) of values with heterogeneous adsorption behavior, in agreement with the Sips exponential fitting parameter ( n s ≠ 1). The CE–EPI adsorbents display unique fractionation with water and ethanol from binary solutions, as evidenced by the relative selectivity ( R selectivity ) value in binary W–E solvent systems. The R selectivity [ Q m (W)/ Q m (E)] values at saturative conditions varied (from 1.10 to 2.03) and further illustrate that CE materials display molecular selective solvent fractionation in binary W–E solutions. This study provides a greater molecular level understanding for the adsorptive uptake properties of CE that are relevant to developing CE-based adsorbent technology for the fractionation of biofuels and related chemical separations.
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 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".