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Record W4311758672 · doi:10.1063/5.0131149

Ferroelectric water chains in carbon nanotubes: Creation and manipulation of ordered quantum phases

2022· article· en· W4311758672 on OpenAlexafffund
Tobias Serwatka, Pierre–Nicholas Roy

Bibliographic record

VenueThe Journal of Chemical Physics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCompute Canada
KeywordsFerroelectricityCarbon nanotubeCondensed matter physicsPoint reflectionMaterials scienceQubitQuantumPolarization (electrochemistry)NanotechnologyPhysicsQuantum mechanicsChemistryOptoelectronicsDielectric

Abstract

fetched live from OpenAlex

Systems composed of molecular rotors are promising candidates as quantum devices. In this work, we employ our recently developed density matrix renormalization group approach to study such a rotor system, namely, linear chains of rotating para-water molecules encapsulated in a (6,5)-carbon nanotube. We show that the anisotropic environment provided by the nanotube breaks the inversion symmetry of the chain. This symmetry breaking lifts the degeneracy of the ground state and leads to a splitting between the left- and right-polarized states. In turn, a ferroelectric phase in nanoscopic systems is created, with a polarization that can be switched in a manner analogous to that of a supramolecular qubit. We present results for a few low-lying states and discuss the effect of external electric fields on the energy splitting and the occurrence of a quantum phase transition.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.014
GPT teacher head0.253
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations12
Published2022
Admission routes2
Has abstractyes

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