Physico-chemical properties of ionic-liquid water mixtures
Bibliographic record
Abstract
In order for Ionic Liquids (ILs) to be utilized to their full potential, it is necessary to have a complete understanding of their physical properties, including phase transitions temperatures1. \nWe have previously reported into the extent of structuring of ILs using photochromic molecular probes, and investigated the appropriate IL water content to yield hydrated IL systems for analysis of polarity and to create environments suitable for effective enzyme activity 1,2,3. \nIn this study we investigated interactions in hydrated ILs containing variable hydrophobic and hydrophilic regions through optical and thermal analysis. The enthalpies and phase transitions of the systems were compared, between the temperature range -50°C to +30°C for the ILs with varying degrees of hydration. Reichardtʼs dye was used as a molecular probe to monitor changes in interactions in the ILs as a function of temperature. Comparisons were made between ILs and for ILs with varying degrees of hydration. Spectroscopic studies were performed using Perkin Elmer UV-Visible Spectrometer and phase transitions monitored using a Perkin Elmer Differential Scanning Calorimeter. \nThe ILs examined are; Trihexyltetradecylphosphonium Chloride [P6,6,6,14 Cl] Tributyl-tetradecylphosphonium Chloride, [P4,4,4,14 Cl], 1-Ethyl-methyl-3-imidazolium-ethyl Sulfate [Emim][EtSO4] and Trihexyltetradecylphosphonium Dicyanamide [P6,6,6,14 DCA]. \n1. Robert Byrne, Simon Coleman, Simon Gallagher, and Dermot Diamond. Designer Molecular Probes for Phosphonium Ionic Liquids. Physical Chemistry Chemical Physics, 2010. \n2. Kyoko Fujita, Douglas R. MacFarlane, Maria Forsyth, Masahiro Yoshizawa-Fujita, Kenichi Murata,† Nobuhumi Nakamura, and Hiroyuki Ohno*,Solubility and Stability of Cytochrome c in Hydrated Ionic Liquids: Effect of Oxo Acid Residues and Kosmotropicity, 2007. \n3. SergeiV.DzyubaandRichardA.Bartsch,Expandingthepolarityrange of ionic liquids, Tetrehedron Letters, 2002.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".