Reducing pH Gradients in Free-Flow Electrophoresis
Bibliographic record
Abstract
Small-volume continuous-flow synthesis (small-volume CFS) offers a number of benefits for use in small-scale chemical production and exploratory chemistry. Typically, small-volume CFS is followed by discontinuous purification; however, a fully continuous synthesis-purification combination is more attractive. Milli free-flow electrophoresis (mFFE) is a promising continuous-flow purification technique that is well suited for integration with small-volume CFS. The purification stability of mFFE, however, needs to be significantly improved before it can be feasible for this combination. One of the major sources of instability of mFFE is attributed to the ions produced as a result of electrolysis. These ions can form pH and conductivity gradients in mFFE, which are detrimental to separation quality. The severity of these gradients has not been thoroughly studied in mFFE. In this paper, we have experimentally demonstrated that detrimental pH gradients occur at flow rates of 8 mL/min and less, and electric field strengths of 25 V/cm and greater. To decrease the pH gradients, it is necessary to evacuate H(+) and OH(-) as soon as they are generated; this can be done by increasing local hydrodynamic flow rates. We calculated the necessary flow rate, to be applied at the electrode, which can effectively wash away both ions before they can cause a detrimental pH gradient. These optimized flow rates can be attained by designing a device that incorporates deep channels. We have confirmed the effectiveness of these channels using a prototyped device. The new design allows mFFE users to work over a wider range of flow-rate and electric-field conditions without experiencing significant changes in pH.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".