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 to dissolve cellulose for filament spinning and bioproduct development. Herein, we introduce a simple method to experimentally measure the interactions of cellulose films (roughness, Rh = 37 nm) with ILs using a homologous series relevant to biomass fractionation, namely, 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]) and their mixtures with water (0 to 10 wt%). 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 reduce 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, ILs carrying [Cl]- anions exhibited a significant reduction in D (from 11.7⋅10−13 to 2.9⋅10−13 𝑚2𝑠−1). Overall, we present a systematic approach rooted in experimental and theoretical approaches to facilitate the design of green routes for cellulose dissolution and processing by using ILs.
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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 | 0.001 |
| 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".