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Record W2344522774 · doi:10.1038/srep14267

A size-dependent structural evolution of ZnS nanoparticles

2015· article· en· W2344522774 on OpenAlexafffund
Mohammad Khalkhali, Qingxia Liu, Hongbo Zeng, Hao Zhang

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Centre for Clean Coal/Carbon and Mineral Processing TechnologiesWestern Canada Research Grid
KeywordsWurtzite crystal structureNanoparticleQuantum dotMaterials scienceNanocrystalline materialZincDipoleNanotechnologyCrystal structureChemical physicsCrystallographyChemistry

Abstract

fetched live from OpenAlex

Recently, ZnS quantum dots have attracted a lot of attention since they can be a suitable alternative for cadmium-based quantum dots, which are known to be highly carcinogenic for living systems. However, the structural stability of nanocrystalline ZnS seems to be a challenging issue since ZnS nanoparticles have the potential to undergo uncontrolled structural change at room temperature. Using the molecular dynamics technique, we have studied the structural evolution of 1 to 5 nm freestanding ZnS nanoparticles with zinc-blende and wurtzite crystal structures. Simulation results revealed that relaxed configurations of ZnS nanoparticles larger than 3 nm consist of three regions: a) a crystalline core, b) a distorted network of 4-coordinated atoms environing the crystalline core, and c) a surface structure made entirely of 3-coordinated atoms. Decreasing the size of ZnS nanoparticle to 2 nm will cause the crystalline core to disappear. Further reducing the size will cause all of the atoms to become 3-coordinated. Dipole moments of zinc-blende and wurtzite nanoparticles are in the same range when the nanoparticles are smaller than 3 nm. Increasing the size makes dipole moments converge to the bulk values. This makes zinc-blende and wurtzite nanoparticles less and more polar, respectively.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.019
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.030
GPT teacher head0.248
Teacher spread0.219 · 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

Citations39
Published2015
Admission routes2
Has abstractyes

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