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Record W2920277430 · doi:10.1021/acsaelm.8b00070

Existence of Resistive Switching Memory and Negative Differential Resistance State in Self-Colored MoS<sub>2</sub>/ZnO Heterojunction Devices

2019· article· en· W2920277430 on OpenAlexaff
Mayameen S. Kadhim, Feng Yang, Bai Sun, Wentao Hou, Haixia Peng, Yunming Hou, Yongfang Jia, Ling Yuan, Yanmei Yu, Yong Zhao

Bibliographic record

VenueACS Applied Electronic Materials · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsUniversity of Waterloo
FundersChina Postdoctoral Science FoundationInstitute of Plasma Physics, Chinese Academy of SciencesMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsMaterials scienceHeterojunctionOptoelectronicsResistive random-access memoryColoredNon-volatile memoryLayer (electronics)Resistive touchscreenSputteringSwitching timeNanotechnologyElectrical engineeringThin filmVoltageComposite materialEngineering

Abstract

fetched live from OpenAlex

A resistive switching random access memory (RRAM) has occupied great scientific and industrial interest for next-generation data storage technology because of its advantages of nonvolatile behavior, low power consumption, high density, rapid writing/erasing speed, and simple operating system. In this work, the wide spectrum with self-colored ZnO layers on the Ti foil is obtained by varying the sputtering time, and the colors of these ZnO films can be tuned by a MoS 2 layer covering. Further, an existence of resistive switching (RS) memory and negative differential resistance (NDR) state in MoS 2 /ZnO heterojunction devices was demonstrated, in which the bright yellow Ag/MoS 2 /ZnO/Ti device shows the best performance with long time endurance. This work opens up an opportunity for exploration of the multifunctional components in future electronic 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.004
GPT teacher head0.185
Teacher spread0.182 · 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 source (direct Gemma or distilled Codex), 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

Citations63
Published2019
Admission routes1
Has abstractyes

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