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Record W2334039176 · doi:10.1021/nn4059852

“Frozen” Block Copolymer Nanomembranes with Light-Driven Proton Pumping Performance

2013· article· en· W2334039176 on OpenAlexafffund
Liangju Kuang, Donald A. Fernandes, Matthew O’Halloran, Wan Zheng, Yunjiang Jiang, Vladimir Ladizhansky, Leonid S. Brown, Hongjun Liang

Bibliographic record

VenueACS Nano · 2013
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsUniversity of Guelph
FundersDivision of Materials ResearchNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsMembraneMaterials scienceCopolymerNanoengineeringNanotechnologyFluidicsAmphiphileMicrofluidicsChemistryPolymer

Abstract

fetched live from OpenAlex

Cellular membranes are natural nanoengineering devices, where matter transport, information processing, and energy conversion across the nanoscale boundaries are mediated by membrane proteins (MPs). Despite the great potential of MPs for nanotechnologies, their broad utility in engineered systems is limited by the fluidic and often labile nature of MP-supporting membranes. Little is known on how to direct spontaneous reconstitution of MPs into robust synthetic nanomembranes or how to tune MP functions through rational design of these membranes. Here we report that proteorhodopsin (PR), a light-driven proton pump, can be spontaneously reconstituted into "frozen" (i.e., glassy state) amphiphilic block copolymer membranes via a charge-interaction-directed reconstitution mechanism. We show that PR is not enslaved by a fluidic or lipid-based membrane environment. Rather, well-defined block copolymer nanomembranes, with their tunable membrane moduli, act as allosteric regulators to support the structural integrity and function of PR. Versatile membrane designs exist to modulate the conformational energetics of reconstituted MPs, therefore optimizing proteomembrane stability and performance in synthetic systems.

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 score0.013
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.023
GPT teacher head0.255
Teacher spread0.231 · 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

Citations43
Published2013
Admission routes2
Has abstractyes

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