Joule-heating membranes: Do they really work for membrane distillation processes?
Bibliographic record
Abstract
• Developed a validated model for Joule-heated membrane distillation (JHMs). • Identified system-level limits, not membrane flaws, as performance barriers. • Achieved 2-3 × reduction in temperature polarization using JHMs. • Demonstrated modest flux gains due to unoptimized thermal integration. • Proposed strategies for scalable, energy-efficient JHM-MD system design. This study critically evaluates the effectiveness of Joule-heated membranes (JHMs) in membrane distillation (MD) processes. Using a Nusselt-based mathematical model and experimental validation, we assess whether JHMs can significantly enhance mass transfer and energy efficiency in MD systems. Our results demonstrate that although JHMs do reduce temperature polarization by 2-3 times compared to conventional MD, the overall flux gains remain modest under standard configurations due to limitations in system design, such as uninsulated tanks and insufficient heat localization. The findings suggest that the limitations are not inherent to the JHMs themselves, but rather stem from suboptimal system integration. With targeted improvements in module insulation, flow configuration, and coating materials, JHMs hold promise for scalable and energy-efficient water treatment applications.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".