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Record W2980357735 · doi:10.1073/pnas.1910490116

Carrier localization in perovskite nickelates from oxygen vacancies

2019· article· en· W2980357735 on OpenAlexaff
Michele Kotiuga, Zhen Zhang, Jiarui Li, Fanny Rodolakis, Hua Zhou, Ronny Sutarto, Feizhou He, Qi Wang, Yifei Sun, Ying Wang, Neda Alsadat Aghamiri, Steven Bennett Hancock, Leonid P. Rokhinson, D. P. Landau, Yohannes Abate, J. W. Freeland, Riccardo Comin, Shriram Ramanathan, Karin M. Rabe

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2019
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic and transport properties of perovskites and related materials
Canadian institutionsCanadian Light Source (Canada)
FundersArmy Research OfficeAir Force Office of Scientific ResearchOffice of Naval ResearchU.S. NavyU.S. ArmyU.S. Department of EnergyNational Science Foundation
KeywordsPerovskite (structure)OxygenMaterials scienceCondensed matter physicsBusinessPhysicsChemistryCrystallographyQuantum mechanics

Abstract

fetched live from OpenAlex

Significance Oxygen vacancies and their associated electrons, ubiquitous in oxide materials, can lead to emergent phenomena. Control of these vacancies allows the manipulation and exploitation of such properties. Using an ultralow oxygen partial pressure environment, we are able to systematically control the oxygen vacancy concentration in thin films of rare-earth nickelates. We find that oxygen vacancies lead to strong carrier localization arising from Ni valence reduction to 2+, resulting in an increase of room-temperature electrical resistivity by several orders of magnitude while maintaining the perovskite structure throughout. The oxygen vacancies can be redistributed by electric fields, leading to analog resistance behavior.

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)0.001
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 score0.075
Threshold uncertainty score0.899

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.017
GPT teacher head0.240
Teacher spread0.223 · 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

Citations121
Published2019
Admission routes1
Has abstractyes

Explore more

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