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Record W2981766032 · doi:10.1039/c9cp05007h

Chemistry of iron nitrate-based precursor solutions for spray-flame synthesis

2019· article· en· W2981766032 on OpenAlexaff
Malte F. B. Stodt, Munko Gonchikzhapov, Tina Kasper, Udo Fritsching, Johannes Kiefer

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2019
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsIONICS Mass Spectrometry (Canada)
FundersDeutsche Forschungsgemeinschaft
KeywordsNitrateChemistryInorganic chemistryEnvironmental chemistryChemical engineeringOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

). Moreover, the impact of adding 2-ethylhexanoic acid is explored. The FTIR spectra reveal the formation of carboxylates and allow deriving information about the coordination of the metal-carboxylate complexes. The stability of the precursor solutions is investigated by monitoring precipitation phenomena and turbidity. Furthermore, gas chromatography is employed to provide additional information on oxidation products and esters as well as to aid the interpretation of the FTIR data. It is found that the formation of esters has an enhancing effect on iron sorption and, thus, it promotes precursor stability.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0060.002

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 head0.015
GPT teacher head0.247
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), 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

Citations61
Published2019
Admission routes1
Has abstractyes

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