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Record W2078185678 · doi:10.1063/1.4757584

Oxygen vacancy filament formation in TiO2: A kinetic Monte Carlo study

2012· article· en· W2078185678 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Applied Physics · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of China
KeywordsKinetic Monte CarloProtein filamentVacancy defectCondensed matter physicsDiffusionElectric fieldMonte Carlo methodChemical physicsMaterials scienceDensity functional theoryChemistryComputational chemistryPhysicsThermodynamics

Abstract

fetched live from OpenAlex

We report a kinetic Monte Carlo (kMC) investigation of an atomistic model for 3-dimensional structural configurations of TiO2 memristor, focusing on the oxygen vacancy migration and interaction under an external voltage bias. kMC allows the access of experimental time scales so that the formation of well defined vacancy filaments in thin TiO2 films can be simulated. The results show that the electric field drives vacancy migration; and vacancy hopping-induced localized electric field plays a key role for the filament evolution. Using the kMC structure of the filaments at different stages of the formation process, electronic density of states (DOS) are calculated by density functional theory. Filament induced gap states are found which gives rise to a transition from insulating behavior to conducting behavior during the filament formation process. By varying kMC simulations parameters, relations between vacancy diffusion, filament formation, and DOS in the TiO2 thin film are elucidated.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score0.327

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)0.0000.000

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 head0.019
GPT teacher head0.240
Teacher spread0.221 · 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