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Record W3160858448 · doi:10.1021/acs.nanolett.0c04704

High Osmotic Power Generation via Nanopore Arrays in Hybrid Hexagonal Boron Nitride/Silicon Nitride Membranes

2021· article· en· W3160858448 on OpenAlexafffund
Khadija Yazda, Katarina Bleau, Yuning Zhang, Xavier Capaldi, Thomas St-Denis, Peter Grütter, Walter Reisner

Bibliographic record

VenueNano Letters · 2021
Typearticle
Languageen
FieldEngineering
TopicNanopore and Nanochannel Transport Studies
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsNanoporeMaterials scienceOsmotic powerSilicon nitrideMembraneBoron nitrideNanotechnologySiliconNitrideFabricationCoatingNanomaterialsChemical engineeringChemical physicsOptoelectronicsChemistryLayer (electronics)Reverse osmosis

Abstract

fetched live from OpenAlex

Nanopores embedded in two-dimensional (2D) nanomaterials are a promising emerging technology for osmotic power generation. Here, coupling our new AFM-based pore fabrication approach, tip-controlled local breakdown (TCLB), with a hybrid membrane formed by coating silicon nitride (SiN) with hexagonal boron nitride (hBN), we show that high osmotic power density can be obtained in systems that do not possess the thinness of atomic monolayers. In our approach, the high osmotic performance arises from charge separation induced by the highly charged hBN surface rather than charge on the inner pore wall. Moreover, exploiting TCLB's capability of producing sub 10 nm pore arrays, we investigate the effects of pore-pore interaction on the overall power density. We find that an optimum pore-to-pore spacing of ∼500 nm is required to maintain an efficient selective transport mechanism.

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 categoriesMeta-epidemiology (narrow)
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 score0.022
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)0.0000.000

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 head0.009
GPT teacher head0.186
Teacher spread0.177 · 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

Citations92
Published2021
Admission routes2
Has abstractyes

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