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Record W1985323573 · doi:10.1021/ic0110528

Ternary Nanoclusters of CuHgS, CuHgSe, and CuInS

2002· article· en· W1985323573 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInorganic Chemistry · 2002
Typearticle
Languageen
FieldMaterials Science
TopicNanocluster Synthesis and Applications
Canadian institutionsUniversity of WaterlooWestern University
Fundersnot available
KeywordsChemistryCopperTernary operationSulfidePhosphineMercury (programming language)Thermal decompositionChalcogenYield (engineering)IndiumChalcogenideMetalInorganic chemistryCrystallographyNanoclustersCrystal structureCluster (spacecraft)Copper sulfideMetallurgyOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Two copper-mercury-chalcogenide clusters [Hg(15)Cu(20)E(25)(PPr(3))(18)] (1, E = S; 2, E = Se) are synthesized in good yield from the reaction of (Pr(3)P)(3)Cu-ESiMe(3) and (Pr(3)P)(2).Hg(OAc)(2) at low temperatures. Single-crystal X-ray analyses illustrate that the two ternary clusters are isomorphous and consist of a phosphine-stabilized core of mixed Hg, Cu, and E centers. Thermolysis of 1 leads to the formation of mercury metal and various forms of copper-sulfide. The copper-indium-sulfide cluster [Cu(6)In(8)S(13)Cl(4)(PEt(3))(12)] (3) is similarly prepared in 50% yield from (Et(3)P)(3)Cu-SSiMe(3), InCl(3), and S(SiMe(3))(2).

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.

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 score0.004
Threshold uncertainty score0.999

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)0.0020.000

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 head0.010
GPT teacher head0.190
Teacher spread0.180 · 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