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Record W4309326065 · doi:10.1098/rsos.220485

Copper oxide nanoparticles doped with lanthanum, magnesium and manganese: optical and structural characterization

2022· article· en· W4309326065 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueRoyal Society Open Science · 2022
Typearticle
Languageen
FieldMaterials Science
TopicCopper-based nanomaterials and applications
Canadian institutionsWestern University
FundersPontificia Universidad Católica del PerúCanada Foundation for Innovation
KeywordsMaterials scienceBand gapCupriteLanthanumScanning electron microscopeManganeseAnalytical Chemistry (journal)NanoparticleMagnesiumTransmission electron microscopySpectroscopyDopingCopperInorganic chemistryNanotechnologyChemistryMetallurgyOptoelectronics

Abstract

fetched live from OpenAlex

Copper oxide (Cu 2 O) is a promising semiconductor for photovoltaic and photocatalytic applications since this material has a high optical absorption coefficient and lower band gap (2.17 eV). Doped lanthanum (La), magnesium (Mg) and manganese (Mn) Cu 2 O nanoparticles (Cu 2 O Nps) were prepared by a displacement reaction. The doped and undoped Cu 2 O Nps were characterized with scanning electron microscopy–energy dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), transmission electron microscopy (TEM) and ultraviolet–visible spectroscopy. The EDS results confirm the presence of La, Mg and Mn in the Cu 2 O Nps. The XRD results confirm the formation a single cubic phase of Cu 2 O with a cuprite structure. TEM images confirm the formation of Nps with mean diameters between 12.0 ± 6.1 and 30.8 ± 11.0 nm. Doped and undoped Nps present a narrow band gap (2.40 eV), blue shifted with respect to bulk Cu 2 O.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.226
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.011
GPT teacher head0.253
Teacher spread0.243 · 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