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Record W3023496548 · doi:10.1021/acs.inorgchem.0c00472

Fast, Low-Cost Synthesis of ZnO:Eu Nanosponges and the Nature of Ln Doping in ZnO

2020· article· en· W3023496548 on OpenAlexaff
Sarmad Naim Katea, Peter Broqvist, Jolla Kullgren, Eva Hemmer, Gunnar Westin

Bibliographic record

VenueInorganic Chemistry · 2020
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsUniversity of Ottawa
FundersNational Supercomputer Centre, Linköpings UniversitetUppsala Multidisciplinary Center for Advanced Computational ScienceStiftelsen ÅforskVetenskapsrådet
KeywordsChemistryDopingZincNanotechnologyCombinatorial chemistryChemical engineeringInorganic chemistryOrganic chemistryOptoelectronics

Abstract

fetched live from OpenAlex

A low-cost template-free solution chemical route to highly porous nanocrystalline sponges of ZnO-EuO1.5 with 0–5 mol % Eu is presented. The process uses Zn– and Eu–acetate–nitrate and triethanolamine as precursors in methanol. After evaporation of the solvent and heating at 200 °C for 3 min, crystalline ZnO:Eu sponges with minor amounts of organic residues were obtained. Heating to 400 °C replaced the organics with carbonate, which in its turn was decomposed at temperatures below 600 °C, forming ZnO:Eu sponges. Samples heated to 200–1000 °C for 3 min were studied with XRD, SEM, TEM, TG, XPS, and IR spectroscopy. The ZnO:Eu crystallite sizes could be tuned from below 10 nm for sponges prepared at 200–500 °C, to over 100 nm range at 900 °C, without sintering of the overall microstructure. XRD showed the presence of hexagonal ZnO:Eu (or at 700–1000 °C, ZnO:Eu and cubic Eu2O3) as the only phases present. The ZnO:Eu had slightly larger unit cell dimensions than the literature value of ZnO for samples obtained at 200–600 °C, while the unit cells of samples obtained at higher temperatures were quite close to the value of undoped ZnO. XPS showed that Eu was mainly in its 3+ state and well-distributed within the sponges but segregated at the ZnO sponge surface upon heating at 700–1000 °C, in accordance with XRD studies showing Eu2O3 formation.

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)0.001
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 score0.004
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.008
GPT teacher head0.201
Teacher spread0.193 · 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

Citations18
Published2020
Admission routes1
Has abstractyes

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