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Record W2602144810 · doi:10.1039/c6nr09395g

Spin filtering with poly-T wrapped single wall carbon nanotubes

2017· article· en· W2602144810 on OpenAlexafffund
Kazi M. Alam, Sandipan Pramanik

Bibliographic record

VenueNanoscale · 2017
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCarbon nanotubeMaterials scienceSpin (aerodynamics)Carbon fibersNanotechnologyCondensed matter physicsComposite materialPhysicsThermodynamicsComposite number

Abstract

fetched live from OpenAlex

Spin filtering is an essential operation in spintronics that allows the creation and detection of spin polarized carriers. Transition metal ferromagnets are used as spin filters in most cases, though their spin filtering efficiency is only around ∼50%, thereby limiting the efficiency of spintronic devices. Recently, chiral systems such as DNA have been shown to exhibit efficient spin filtering, a phenomenon often dubbed as “chirality induced spin selectivity” (CISS). In this work, we consider single wall carbon nanotubes helically wrapped with single stranded poly-T DNA. By magnetoresistance measurements we show that this system exhibits significant spin polarization of ∼80%, which could be attributed to the Rashba spin–orbit interaction induced by the inversion-asymmetric helical potential of the DNA. The observed spin polarization is larger than that reported before for d(GT)15 strands. Such systems allow tailoring of spin polarization by chemical means and also allow extremely localized creation and detection of spin polarization without any magnetic element and could lead to extreme miniaturization and compact integration of spintronic devices and circuits.

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 score0.017
Threshold uncertainty score0.923

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 communication0.0010.000
Open science0.0010.000
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 head0.019
GPT teacher head0.255
Teacher spread0.236 · 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

Citations28
Published2017
Admission routes2
Has abstractyes

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