Revealing Wetting Patterns of Porous Hydrophobic Membranes for Desalination Using Electrochemical Impedance Spectroscopy
Bibliographic record
Abstract
Membrane distillation (MD) holds great promise for high salinity wastewater desalination, but membrane pore wetting remains a major hurdle to the process. We develop a novel framework to dynamically monitor the wetting progression of hydrophobic MD membranes and quantitatively describe the wetting status changes by implementing four-electrode mode electrochemical impedance spectroscopy (EIS). We decouple the membrane impedance into resistance and capacitance components, which are then translated into air gap span and wetted pore fractions. Distinguishing the wetting progression and the occurrence of pore breakthroughs, this framework successfully depicts the disparate wetting patterns of hydrophobic membranes induced by surfactants and low-surface-tension liquids. We show that the ability to instantaneously capture the wetting progression makes our framework particularly attractive for MD operations accompanied by slow wetting. EIS applied in our framework further reveals that a membrane of a randomly porous structure incurs a stepwise wetting front advancing by low-surface-tension liquids, as opposed to the single pore breakthrough event in the membrane of straight pores. Combined with the internal surface area and the pore size analyses, this illustration by EIS is used to model a randomly porous membrane as tortuous pores with multiple 'necks' of local minimum pore sizes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".