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Record W2322524665 · doi:10.1021/ie500095s

Synthesis of Different Manganese Oxides Using SO<sub>2</sub>/O<sub>2</sub> Gas Mixtures at Different Temperatures

2014· article· en· W2322524665 on OpenAlexaff
S. Bello-Teodoro, R. Pérez‐Garibay, Jocelyn Bouchard

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2014
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsManganeseActivation energyReagentChemistryDiffusionManganese oxideHydroxideKineticsInorganic chemistryOxidePhysical chemistryThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

The aim of this work is to study the kinetics and required operation conditions for the formation of various manganese oxides using the SO 2 /O 2 gas mixture as the oxidant agent. This promising approach presents some obvious economical advantages over traditional pyrometallurgical routes. Experimental evidence demonstrate that increasing the temperature of the reaction allows modifying the oxidation potential, thus facilitating the oxidation product synthesis. However, at 25 °C by varying the SO 2 /O 2 ratio and the gas mixture flow rate, it was not possible to modify this potential. It was also observed that manganese dioxide (MnO 2 ) is formed between 20 and 50 °C (activation energy ( E a ) = 14.33 kJ/mol), oxy-hydroxide of manganese (MnO·OH) is obtained between 55 and 65 °C ( E a = 25.14 kJ/mol), and manganese(II, III) oxide (Mn 3 O 4 ) is produced between 70 and 90 °C ( E a = 12.44 kJ/mol). The magnitude of these activation energy values is characteristic of processes controlled by the diffusion of the gaseous reagents.

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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.040
GPT teacher head0.271
Teacher spread0.231 · 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

Citations6
Published2014
Admission routes1
Has abstractyes

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