Ionic liquid interactions with cellulose and the effect of water
Bibliographic record
Abstract
Abstract Ionic Liquids (ILs) have been used to address issues such as recyclability, cost-effectiveness, and tailored thermophysical properties. This is most relevant to recent efforts directed at dissolving cellulose for filament spinning and bioproduct development. Herein, we introduce a simple method to investigate how interactions between cellulose films (roughness, Rh = 37 nm) and ILs specifically 1-butyl-3-methylimidazolium acetate ([bmim][OAc]), 1-butyl-3-methylimidazolium chloride ([bmim][Cl]), 1-ethyl-3-methylimidazolium acetate ([emim][OAc]), and 1-ethyl-3-methylimidazolium chloride ([emim][Cl]), along with their water mixtures (0, 5, and 10 wt%) affect thermophysical properties relevant to cellulose dissolution (surface tension, γ; contact angle, θ; diffusivities, D; and bulk density, ρ) at 363.15 K and 0.1 MPa under argon and air atmospheres. Thermophysical properties relevant to cellulose dissolution were measured at 363.15 K and 0.1 MPa under argon (surface tension, γ, contact angle, θ), and air (diffusivities, D and bulk density, ρ ) atmospheres to reveal the effect of the IL counter ions on the involved interactions with water. In general, water increased γ, θ , but reduced D , which supports experimental observations indicating the detrimental effect of water on IL-cellulose interactions. The [emim] + cation (in [emim][OAc] and [emim][Cl]), produced a lower contact angle with cellulose while the interfacial properties ( γ, θ, D ) for ILs with the [OAc] − anion were marginally affected by water. By contrast, the two ILs carrying [Cl] − anions exhibited a significant reduction in D (from 11.7 $$\cdot {10}^{-13}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>·</mml:mo> <mml:msup> <mml:mrow> <mml:mn>10</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>13</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> to 2.9 $$\cdot {10}^{-13} {m}^{2}{s}^{-1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>·</mml:mo> <mml:msup> <mml:mrow> <mml:mn>10</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>13</mml:mn> </mml:mrow> </mml:msup> <mml:msup> <mml:mrow> <mml:mi>m</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> <mml:msup> <mml:mrow> <mml:mi>s</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> ) in the cation shift from [emim] + to [bmim] + at 363.15 K and 0.1 MPa, with 0% water content. Overall, we present a methodical approach rooted in experimental and theoretical approaches to facilitate our understanding of ionic liquids (ILs), especially within the domain of bioprocessing. Graphical Abstract
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".