Real-time non-destructive monitoring of supported liquid membranes using electrochemical impedance spectroscopy
Bibliographic record
Abstract
The degradation of supported liquid membranes (SLMs) due to membrane liquid leaching has been a hurdle for their implementation in resource recovery from waste streams. We present a novel approach utilizing four-electrode mode electrochemical impedance spectroscopy (EIS) for real-time, non-destructive monitoring of SLM degradation, providing detailed analysis of SLM's structural changes. We first showed that SLMs may be depicted as an electrical circuit of resistors and capacitors by EIS. The EIS then revealed that SLM degradation progressed over four different phases as a result of varying rates of thickness and area reduction of the SLM. To demonstrate the effectiveness of EIS-based diagnosis, we prepared SLMs designed for selective medium-chain fatty acid (MCFA) extraction in intact, partially, and completely degraded states, determined by the EIS. The intact SLM showed a high selectivity (∼50) for MCFAs over short-chain fatty acids. The selectivity decreased to ∼22 in the partially degraded SLMs, and less than 1 in the completely degraded SLM, indicating the clear linkage between the degree of degradation and the separation performance. Our four-electrode mode EIS method is expected to serve as a key tool for efficient SLM operation and stable SLM development. • We present four-electrode mode EIS for real-time monitoring of SLM degradation. • EIS analysis enables an accurate estimation of SLM area and thickness. • EIS reveals four distinct phases of SLM degradation. • We show the correlation between SLM structural changes and separation performances.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".