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Record W3093091249 · doi:10.1021/acsami.0c14706

Defect-Rich Dopant-Free ZrO<sub>2</sub> Nanoclusters and Their Size-Dependent Ferromagnetism

2020· article· en· W3093091249 on OpenAlexafffund
Xiaoyi Guan, Saurabh Srivastava, Joseph P. Thomas, Nina F. Heinig, Jung-Soo Kang, Md. Anisur Rahman, K. T. Leung

Bibliographic record

VenueACS Applied Materials & Interfaces · 2020
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceNanoclustersDopantFerromagnetismNanotechnologyDopingCondensed matter physicsOptoelectronics

Abstract

fetched live from OpenAlex

As an intermediate form of matter between a single atom or molecule and the bulk, nanoclusters (NCs) provide novel properties because of their high surface area-to-volume ratios and distinct physical and electronic structures. These ultrasmall NCs offer a new approach to advance charge–spin manipulation for novel devices, including spintronics and magnetic tunneling junctions. Here, we deposit monosized ZrO2 NCs over a large area by using gas-phase aggregation followed by in situ size selection by a quadrupole mass filter. These size-specific NCs exhibit sub-oxide photoemission features at binding energies that are dependent on the cluster size (from 3 to 9 nm), which are attributed to different oxygen vacancy defect states. These dopant-free ZrO2 NCs also show strongly size-dependent ferromagnetism, which provides distinct advantages in solubility and homogeneity of magnetism when compared to traditional dilute magnetic semiconductors. A defect-band hybridization-induced magnetic polaron model is proposed to explain the origin of this size-dependent ferromagnetism. This work demonstrates a new protocol of magnetization manipulation by size control and promises potential applications based on these defect-rich size-selected NCs.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.014
GPT teacher head0.196
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 teacher head, not a consensus.

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

Citations21
Published2020
Admission routes2
Has abstractyes

Explore more

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