Double Layer Capacitance Measurements To Characterize the Water Intrusion into Porous Materials
Bibliographic record
Abstract
In this work, we have proposed and substantiated a novel approach to study the dynamic wetting behavior during water intrusion, demonstrated for a porous carbon fiber substrate. The proposed methodology quantifies the evolution of the wetted interfacial area during intrusion by electrochemically measuring the double layer capacitance, which is proportional to the solid–liquid interfacial area. We investigated the intrusion behavior for three commercially available substrates with distinct thicknesses and internal microstructures, using a combination of capacitance and pressure measurements. For the same imbibed volume of water, the pressure increase was comparable, while the capacitance increase was distinct for the substrates with dissimilar internal microstructures. The hydraulic radius and the cross section of the intruding meniscus of water reduced during the course of intrusion. A correlation between the capacitance and the pressure–volume work has been proposed as a measure for quantifying the favorability of wetting the fiber surface, during the liquid intrusion into the porous structure. The pressure–volume work done in wetting the fiber surface showed dependence on the internal microstructure and remains constant during the course of water intrusion. The approach presented here can facilitate quantitative characterization of the wetting behavior, and the new parameter (wetted interfacial area) could be used as the basis of analytical models for the water transport behavior through these porous structures.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".