Polymeric Ionic Liquid Compositions for In-Situ Product Removal Bioreactor Applications
Bibliographic record
Abstract
Although the biosynthesis of n-butanol and succinic acid can afford renewable fuels, solvents, and chemical intermediates, these bioprocesses suffer from the cytotoxicity of their end products, leading to reduced production rates. In-situ product removal using a two-phase partitioning bioreactor (TPPB) can alleviate this problem by sequestering reaction products in a secondary, non-aqueous phase. Effective sorbents for TPPB applications are biocompatible materials with a high thermodynamic affinity for the product, as quantified by partition coefficient (PC), and a preference for solute over water, as measured by selectivity (α). In this work, the absorptive capacity of imidazolium-based ionic liquids (ILs) are combined with the attractive physical properties of polymeric sorbents by synthesizing a range of polymeric ionic liquid (PIL) compositions. Careful measurements of PIL molecular weight, glass transition temperature (Tg) and crystallinity were followed by systematic studies of the effect of imidazolium cation substituents and counter-anions on PC and α for the n-butanol-water system. P[(VC12-linIm)(Br)] was identified as the most promising PIL n-butanol absorbent by virtue of a PCBuOH = 7.4 ± 0.3, and αb/w = 97 ± 7. An expanded study of the ternary PIL/water/n-butanol phase diagram was integrated with material balance equations for a solute recovery process to gain insight into the impact of polymer phase fraction, PC, α, feed concentration, and Tg on the recovery of n-butanol from dilute fermentation media. The final phase of the project exploited experience gained on the preparation and phase behavior of P[(VC12-linIm)(Br)] to prepare a hydroxide-functionalized PIL. The utility of this base-functionalized material to chemisorb succinic acid from dilute aqueous solution was demonstrated along with its resilience toward repeated regeneration and reuse.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".