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Record W4323347809 · doi:10.1002/anie.202218129

Toward Scalable Nanofluidic Osmotic Power Generation from Hypersaline Water Sources with a Metal–Organic Framework Membrane

2023· article· en· W4323347809 on OpenAlexaff
Shangfa Pan, Peng Liu, Qi Li, Bin Zhu, Xueli Liu, Junchao Lao, Jun Gao, Lei Jiang

Bibliographic record

VenueAngewandte Chemie International Edition · 2023
Typearticle
Languageen
FieldEngineering
TopicNanopore and Nanochannel Transport Studies
Canadian institutionsInstitute of Particle Physics
FundersNational Natural Science Foundation of ChinaNational Key Research and Development Program of ChinaNatural Science Foundation of Shandong ProvinceShandong Energy Institute, Chinese Academy of Sciences
KeywordsMembraneOsmotic powerOsmosisMaterials scienceNanotechnologyChemical engineeringChemistryForward osmosisChemical physicsEngineeringReverse osmosis

Abstract

fetched live from OpenAlex

Abstract Nanofluidic membranes have shown great promise in harvesting osmotic energy but its scalablity remains challenging since most studies only tested with a membrane area of ≈10 −2 mm 2 or smaller. We demonstrate that metal‐organic‐framework membranes with subnanometer pores can be used for scalable osmotic power generation from hypersaline water sources. Our membrane can be scaled up to a few mm 2 , and the power density can be stabilized at 1.7 W m −2 . We reveal that the key is to improve the out‐of‐membrane conductance while keeping the membrane's charge selectivity, contradicting the previous conception that the ionic conductivity of the membrane plays the dominating role. We highlight that subnanometer pores are essential to ensure the charge selectivity in hypersaline water sources. Our results suggest the importance to engineer the interplay between the in‐membrane and out‐of‐membrane ion transport properties for scalable osmotic power generation.

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 categoriesInsufficient payload (model declined to judge)
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 score0.018
Threshold uncertainty score1.000

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 head0.018
GPT teacher head0.210
Teacher spread0.193 · 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.

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

Citations96
Published2023
Admission routes1
Has abstractyes

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