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Record W2765915292 · doi:10.1021/acs.nanolett.7b04396

Controlled Electrochemical Intercalation of Graphene/<i>h-</i>BN van der Waals Heterostructures

2017· article· en· W2765915292 on OpenAlexfundno aff
Sai Zhao, Giselle A. Elbaz, D. Kwabena Bediako, Cyndia Yu, Dmitri K. Efetov, Yinsheng Guo, Jayakanth Ravichandran, Kyung‐Ah Min, Suklyun Hong, Takashi Taniguchi, Kenji Watanabe, Louis E. Brus, Xavier Roy, Philip Kim

Bibliographic record

VenueNano Letters · 2017
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsnot available
FundersAir Force Office of Scientific ResearchArmy Research OfficeDivision of Graduate EducationNatural Sciences and Engineering Research Council of CanadaNational Research Foundation of KoreaMinistry of Education, Culture, Sports, Science and TechnologyJapan Society for the Promotion of ScienceSemiconductor Research Corporation
KeywordsIntercalation (chemistry)Graphenevan der Waals forceHeterojunctionMaterials scienceElectrochemistryHexagonal boron nitrideChemical physicsNanotechnologyInorganic chemistryChemistryMoleculeElectrodeOptoelectronicsPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Electrochemical intercalation is a powerful method for tuning the electronic properties of layered solids. In this work, we report an electrochemical strategy to controllably intercalate lithium ions into a series of van der Waals (vdW) heterostructures built by sandwiching graphene between hexagonal boron nitride ( h -BN). We demonstrate that encapsulating graphene with h -BN eliminates parasitic surface side reactions while simultaneously creating a new heterointerface that permits intercalation between the atomically thin layers. To monitor the electrochemical process, we employ the Hall effect to precisely monitor the intercalation reaction. We also simultaneously probe the spectroscopic and electrical transport properties of the resulting intercalation compounds at different stages of intercalation. We achieve the highest carrier density >5 × 10 13 cm 2 with mobility >10 3 cm 2 /(V s) in the most heavily intercalated samples, where Shubnikov–de Haas quantum oscillations are observed at low temperatures. These results set the stage for further studies that employ intercalation in modifying properties of vdW heterostructures.

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 score0.007
Threshold uncertainty score0.395

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)0.0000.000

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 head0.012
GPT teacher head0.280
Teacher spread0.269 · 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

Citations68
Published2017
Admission routes1
Has abstractyes

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