MétaCan
Menu
Back to cohort
Record W2065958265 · doi:10.1063/1.4870196

New metal-organic nanomaterials synthesized by laser irradiation of organic liquids

2014· article· en· W2065958265 on OpenAlexaff
S. Kuzmin, Michal J. Wesolowski, W. W. Duley

Bibliographic record

VenueAIP conference proceedings · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials Characterization Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFerroceneMaterials scienceBenzeneNanomaterialsMetalloceneLaser ablationMoleculeFemtosecondIrradiationNanoparticleNanolithographyMetalChemical engineeringLaserNanotechnologyPhysical chemistryOrganic chemistryChemistryComposite materialPolymerizationOpticsMetallurgy

Abstract

fetched live from OpenAlex

A new type of metal-organic composition consisting of clusters of nanoparticles has been synthesised by laser irradiation of metallocene/benzene solutions. The metallocene molecules in this reaction become the source of the metal. Exposure to high-energy femtosecond laser pulses dehydrogenate benzene molecules and initiate the high-temperature high-pressure conditions that results in the synthesis of new materials. Irradiation experiments have been carried out on ferrocene/benzene and on other solutions. With ferrocene the synthesis of a new compound has been confirmed by X-ray powder diffraction as the peaks detected do not correspond to any known substance in the Crystallography Open Database. Theoretical simulation of the periodic structure of this new carbide predicts that it has hexagonal symmetry and a unit cell with a = 3.2A and c =2.8A. The exact structure is still uncertain but may be determined from scanning tunneling microscope (STM) studies.

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 score0.171
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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.204
Teacher spread0.197 · 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
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueAIP conference proceedingsSame topicAdvanced Materials Characterization TechniquesFrench-language works237,207