Pith. sign in

REVIEW 1 cited by

Exciton-Peierls mechanism and universal many-body gaps in carbon nanotubes

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1807.05589 v2 pith:KJTLYYUM submitted 2018-07-15 cond-mat.mes-hall cond-mat.str-el

classification cond-mat.mes-hallcond-mat.str-el
keywords gapstubescarbonelectronicexcitonicinstabilitiesmechanismmodes
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
abstract

"Metallic" carbon nanotubes exhibit quasiparticle gaps when isolated from a screening environment. The gap-opening mechanism is expected to be of electronic origin, but the precise nature is debated. In this work, we show that hybrid density functional theory predicts a set of excitonic instabilities capable of opening gaps of the size found in experiment. The excitonic instabilities are coupled to vibrational modes and, in particular, the modes associated with the ${\bf \Gamma}-E_{2g}$ and ${\bf K}-A'_1$ Kohn anomalies of graphene, inducing Peierls lattice distortions with a strong electron-phonon coupling. In the larger tubes, the longitudinal optical phonon mode becomes a purely electronic dimerization that is fully symmetry conserving in the zigzag and chiral tubes, but breaks the symmetry in the armchair tubes. The resulting gaps are universal (i.e., independent of chirality) and scale as 1/R with tube radius.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. HARLF: Hierarchical Reinforcement Learning and Lightweight LLM-Driven Sentiment Integration for Financial Portfolio Optimization

    q-fin.PM 2025-07 conditional novelty 4.0 of 10

    A hierarchical RL portfolio optimizer using FinBERT sentiment and market indicators reports 26% annualized return and Sharpe 1.2 on a 2018-2024 backtest, beating equal-weight and S&P 500 benchmarks.

Pith tools