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Record W4313278768 · doi:10.1126/sciadv.ade3759

Ultrafast response of spontaneous photovoltaic effect in 3R-MoS <sub>2</sub> –based heterostructures

2022· article· en· W4313278768 on OpenAlexaff
Jingda Wu, Dongyang Yang, Jing Liang, Max Werner, Evgeny E. Ostroumov, Yunhuan Xiao, Kenji Watanabe, Takashi Taniguchi, Jerry I. Dadap, David Jones, Ziliang Ye

Bibliographic record

VenueScience Advances · 2022
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPhotocurrentMaterials scienceHeterojunctionOptoelectronicsPicosecondUltrashort pulseQuantum tunnellingGraphenePhotovoltaic systemQuantum efficiencyPhotovoltaic effectNanotechnologyOpticsPhysics

Abstract

fetched live from OpenAlex

Rhombohedrally stacked MoS 2 has been shown to exhibit spontaneous polarization down to the bilayer limit and can sustain a strong depolarization field when sandwiched between graphene. Such a field gives rise to a spontaneous photovoltaic effect without needing any p-n junction. In this work, we show that the photovoltaic effect has an external quantum efficiency of 10% for devices with only two atomic layers of MoS 2 at low temperatures, and identify a picosecond-fast photocurrent response, which translates to an intrinsic device bandwidth at ∼100-GHz level. To this end, we have developed a nondegenerate pump-probe photocurrent spectroscopy technique to deconvolute the thermal and charge-transfer processes, thus successfully revealing the multicomponent nature of the photocurrent dynamics. The fast component approaches the limit of the charge-transfer speed at the graphene-MoS 2 interface. The remarkable efficiency and ultrafast photoresponse in the graphene-3R-MoS 2 devices support the use of ferroelectric van der Waals materials for future high-performance optoelectronic applications.

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)0.002
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 score0.006
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.005
GPT teacher head0.248
Teacher spread0.243 · 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

Citations51
Published2022
Admission routes1
Has abstractyes

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