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Record W4414877650 · doi:10.1002/anie.202514637

Defect‐Tolerant and Scalable Diffusio–Osmotic Power Generation with Sulfonated Covalent Organic Framework Membrane

2025· article· en· W4414877650 on OpenAlexaff
Shangfa Pan, Qi Li, Qianqian Fu, Jing Zeng, Peng Liu, Lei Jiang, Jun Gao

Bibliographic record

VenueAngewandte Chemie International Edition · 2025
Typearticle
Languageen
FieldEngineering
TopicMembrane-based Ion Separation Techniques
Canadian institutionsInstitute of Particle Physics
FundersNational Key Research and Development Program of ChinaNatural Science Foundation of Shandong ProvinceNational Natural Science Foundation of China
KeywordsReversed electrodialysisOsmotic powerMembraneElectricity generationRenewable energyFabricationGenerator (circuit theory)ScalabilityLimitingElectrodialysis

Abstract

fetched live from OpenAlex

Salinity gradient energy extracted with the reverse electrodialysis technique is attracting great interest and has been suggested as a promising renewable and stable energy source. However, the reverse electrodialysis relies on highly charge-selective membranes, causing a range of problems including the selectivity-permeability trade-off, strong concentration polarization, and strict requirement on the material structure, severely limiting its viability for large scale applications. We demonstrate these problems may be addressed by adopting the diffusio-osmosis process to generate power using sulfonated covalent framework membranes (COF), which does not require any charge selectivity. As a result, the membrane shows much higher power density compared to similar-sized membranes and enables much higher scalability. Remarkably, the generator has loose requirement on material structure and could largely maintain its power generation performance even when a substantial number of pinholes are present. This could make the material fabrication significantly easier than before. We expect our work to advance the practical application of salinity gradient energy extraction.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.230
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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