MétaCan
Menu
Back to cohort
Record W4401335765 · doi:10.1021/acsnano.4c04075

Large-Area Intercalated Two-Dimensional Pb/Graphene Heterostructure as a Platform for Generating Spin–Orbit Torque

2024· article· en· W4401335765 on OpenAlexafffund
Alexander Vera, Boyang Zheng, Wilson Yanez, Kaijie Yang, Seong Yeoul Kim, Xinglu Wang, Jimmy C. Kotsakidis, Hesham El‐Sherif, Gopi Krishnan, Roland J. Koch, Timothy Bowen, Chengye Dong, Yuanxi Wang, Maxwell Wetherington, Eli Rotenberg, Nabil Bassim, Adam L. Friedman, Robert M. Wallace, Chao‐Xing Liu, Nitin Samarth, Vincent H. Crespi, Joshua A. Robinson

Bibliographic record

VenueACS Nano · 2024
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsMcMaster University
FundersNational Science Foundation Graduate Research Fellowship ProgramDivision of Materials ResearchPennsylvania State UniversityCanada Foundation for InnovationAir Force Office of Scientific ResearchAlfred P. Sloan Foundation
KeywordsGrapheneHeterojunctionMaterials scienceSpin (aerodynamics)Orbit (dynamics)Condensed matter physicsTorqueNanotechnologyOptoelectronicsPhysicsAerospace engineeringEngineeringQuantum mechanics

Abstract

fetched live from OpenAlex

A scalable platform to synthesize ultrathin heavy metals may enable high-efficiency charge-to-spin conversion for next-generation spintronics. Here, we report the synthesis of air-stable, epitaxially registered monolayer Pb underneath graphene on SiC (0001) by confinement heteroepitaxy (CHet). Diffraction, spectroscopy, and microscopy reveal that CHet-based Pb intercalation predominantly exhibits a mottled hexagonal superstructure due to an ordered network of Frenkel–Kontorova-like domain walls. The system’s air stability enables ex situ spin torque ferromagnetic resonance (ST-FMR) measurements that demonstrate charge-to-spin conversion in graphene/Pb/ferromagnet heterostructures with a 1.5× increase in the effective field ratio compared to control samples.

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 score0.016
Threshold uncertainty score0.693

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.313
Teacher spread0.291 · 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

Citations18
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueACS NanoSame topicGraphene research and applicationsFrench-language works237,207