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Record W3039691665 · doi:10.1021/acs.nanolett.0c01934

Facile Size-Selective Defect Sealing in Large-Area Atomically Thin Graphene Membranes for Sub-Nanometer Scale Separations

2020· article· en· W3039691665 on OpenAlexaff
Peifu Cheng, Mattigan M. Kelly, Nicole K. Moehring, Wonhee Ko, An‐Ping Li, Juan Carlos Idrobo, Michael S. H. Boutilier, Piran R. Kidambi

Bibliographic record

VenueNano Letters · 2020
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsWestern University
FundersDivision of Chemical, Bioengineering, Environmental, and Transport SystemsAmerican Chemical Society Petroleum Research FundVanderbilt University
KeywordsMaterials scienceGrapheneMembraneNanoporePermeanceNanotechnologyNanoporousNanometreChemical engineeringThin filmComposite materialChemistry

Abstract

fetched live from OpenAlex

Atomically thin graphene with a high-density of precise subnanometer pores represents the ideal membrane for ionic and molecular separations. However, a single large-nanopore can severely compromise membrane performance and differential etching between pre-existing defects/grain boundaries in graphene and pristine regions presents fundamental limitations. Here, we show for the first time that size-selective interfacial polymerization after high-density nanopore formation in graphene not only seals larger defects (>0.5 nm) and macroscopic tears but also successfully preserves the smaller subnanometer pores. Low-temperature growth followed by mild UV/ozone oxidation allows for facile and scalable formation of high-density (4–5.5 × 1012 cm–2) useful subnanometer pores in the graphene lattice. We demonstrate scalable synthesis of fully functional centimeter-scale nanoporous atomically thin membranes (NATMs) with water (∼0.28 nm) permeance ∼23× higher than commercially available membranes and excellent rejection to salt ions (∼0.66 nm, >97% rejection) as well as small organic molecules (∼0.7–1.5 nm, ∼100% rejection) under forward osmosis.

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 score0.018
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.023
GPT teacher head0.270
Teacher spread0.248 · 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

Citations63
Published2020
Admission routes1
Has abstractyes

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