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Record W4402230776 · doi:10.3390/nano14171443

Self-Assembled Hybrid Halide Perovskite Quantum Wire Bundle/Dot for Multiband Applications

2024· article· en· W4402230776 on OpenAlexafffund
H. C. Jeon, Seonghwan Kim, Young‐Seong Kim

Bibliographic record

VenueNanomaterials · 2024
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Science and ICT, South KoreaAlberta InnovatesNational Research Foundation of KoreaKorea Institute for Advancement of TechnologyMinistry of Trade, Industry and EnergyNational Research Foundation
KeywordsQuantum dotHalidePerovskite (structure)Materials scienceNanometrePhotoluminescenceOptoelectronicsTransmission electron microscopyComposite numberNanotechnologyBundleCrystallographyChemistryComposite material

Abstract

fetched live from OpenAlex

In this study, self-assembled halide perovskite quantum wire bundles (QWBs)/quantum dots (QDs) are fabricated using a room temperature-based formation method. The one-dimensional (1D) perovskite-based QWB structures incorporate zero-dimensional QDs within a composite quantum structure. Transmission electron microscopy reveals that quantum wires with diameters ranging from tens of nanometers to approximately 200 nm maintain a single-crystal atomic arrangement in a bundle form. Conversely, QDs are uniformly distributed within the single-phase wire and appear as black dots < 10 nm. Photoluminescence analysis identifies the multiband characteristics of the emissions. The 420–440 nm band is attributed to 1D QWB, whereas the peak appearing in the 530–550 nm range corresponds to lead halide PbBr2 QDs. Thus, the proposed self-assembled 1D QWB/QD composite structure exhibits novel multiband physical properties in the 420–440 and 530–550 nm bands; it offers new opportunities for designing materials with potential applications in optoelectronic devices.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.448
Threshold uncertainty score1.000

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.010
GPT teacher head0.246
Teacher spread0.236 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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