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Record W2059839091 · doi:10.1021/cm050850j

PbS-Doped Mesostructured Silica Films with High Optical Nonlinearity

2005· article· en· W2059839091 on OpenAlexaff
Dario Buso, Paolo Falcaro, Stefano Costacurta, M. Guglielmi, Alessandro Martucci, Plinio Innocenzi, Luca Malfatti, Valentina Bello, G. Mattei, C. Sada, Heinz Amenitsch, Irina Gerdova, Alain Haché

Bibliographic record

VenueChemistry of Materials · 2005
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsMaterials scienceLead sulfideNanocrystalTransmission electron microscopyMesoporous materialDopingNanoparticleMesoporous silicaNanoreactorChemical engineeringNanotechnologyAbsorption (acoustics)Quantum dotOptoelectronicsComposite materialOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

Mesostructured silica films have been doped with lead sulfide (PbS) nanocrystals by a two-step impregnation, using the mesopores as nanoreactors for PbS growth. Secondary ion mass spectrometry and transmission electron microscopy characterizations demonstrated nanocrystals growth inside the pores and throughout the film thickness, pointing out the feasibility of an impregnation-based synthesis made possible by the porous properties of mesoporous silica films. The mean particles diameter is 5 nm, which is compatible with the pore dimensions. The mesostructure order was retained after the growth of the nanocrystals, as pointed out by small-angle X-ray scattering measurements. Optical absorption and Z -scan measurements indicated that PbS nanoparticles show a quantum confinement effect, while the films are characterized by high nonlinearities of the optical response. The optical properties of this material can be usefully exploited in several nonlinear optical applications.

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 categoriesInsufficient 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 score0.013
Threshold uncertainty score0.987

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)0.0130.000

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 head0.012
GPT teacher head0.220
Teacher spread0.208 · 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

Citations52
Published2005
Admission routes1
Has abstractyes

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