Transient performance of two‐phase partitioning bioreactors treating a toluene contaminated gas stream
Bibliographic record
Abstract
Two-phase partitioning bioreactors (TPPBs) consist of a cell-containing aqueous phase and an immiscible organic phase that sequesters and delivers toxic substrates to cells based on equilibrium partitioning. The immiscible organic phase, which acts as a buffer for inhibitory substrate loadings, makes it possible for TPPBs to handle high volatile organic compound (VOC) loadings, and in this study the performance of liquid n-hexadecane and solid styrene butadiene (SB) polymer beads used as partitioning phases were compared to a single aqueous phase system while treating transient loadings of a toluene contaminated air stream by Achromobacter xylosoxidans Y234. The TPPBs operated as well-mixed stirred tanks, with total working volumes of 3 L (3 L aqueous for the single-phase system, 2 L aqueous and 1 L n-hexadecane for the solvent system, and 2.518 L aqueous volume and 500 g of SB beads for the polymer system). Two 60-min step changes (7 and 17 times the nominal loading rates, termed "small" and "large" steps, respectively) were imposed on the systems and the performance was characterized by the overall removal efficiencies, instantaneous removal efficiency recovery times (above 95% removal), and dissolved oxygen recovery times. For the small steps, with a nominal loading of 343 g/m3/h increasing to 2,400 g/m3/h, the TPPB system using n-hexadecane as the second phase performed best, removing 97% of the toluene fed to the system compared with 90% for the polymer beads system and only 69% for the single-phase system. The imposed large transient gave similar results, although the impact of the presence of a second sequestering phase was more pronounced, with the n-hexadecane system maintaining much reduced aqueous toluene concentrations leading to significantly improved performance. This investigation also showed that the presence of both n-hexadecane and SB beads improved the oxygen transfer within the systems.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
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".