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Record W4403658260 · doi:10.1002/cjce.25530

Reaction kinetics of molybdenum dissolution by hydrogen peroxide in acidic and alkaline solutions using tartaric acid and sodium hydroxide: A semi‐empirical model with rotating disc method

2024· article· en· W4403658260 on OpenAlexvenueno aff
Mahmoud Motasim, Tevfik Ağaçayak, Yasin Ramazan Eker, Salih Aydoğan, Ahmed Mohammedelmubarak Abbaker

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsnot available
FundersKonya Teknik Üniversitesi
KeywordsHydrogen peroxideKineticsTartaric acidDissolutionSodium hydroxideChemistryMolybdenumInorganic chemistryPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Molybdenum is an amphoteric metal that dissolves in both acidic and alkaline solutions. This fundamental study explores a sustainable process for the dissolution of molybdenum, focusing on the reaction kinetics in H 2 O 2 , H 2 O 2 ‐NaOH, and H 2 O 2 ‐C 4 H 6 O 6 solutions. A rotating disc method was applied with the Levich's equation. Semi‐empirical models with activation energy were developed for the H 2 O 2 ‐NaOH and H 2 O 2 ‐C 4 H 6 O 6 solutions. The study examined the effects of rotating speed, disc surface area, temperature, H 2 O 2 , NaOH, and C 4 H 6 O 6 concentrations, along with rotating speed, disc surface area, and temperature. Hydrogen peroxide significantly impacted molybdenum dissolution rates across all three solutions. The reaction order of hydrogen peroxide concentration in the H 2 O 2 solution was greater than that of the H 2 O 2 ‐NaOH and H 2 O 2 ‐C 4 H 6 O 6 solutions. The complex of molybdenum peroxo was formed in H 2 O 2 and H 2 O 2 ‐NaOH solutions but decomposed at a temperature ≥50°C. The activation energies were determined to be 49.90, 43.60, and 41.10 kJ/mol for the H 2 O 2 , H 2 O 2 ‐NaOH, and H 2 O 2 ‐C 4 H 6 O 6 solutions.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.457

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 integrity0.0000.001
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 head0.019
GPT teacher head0.245
Teacher spread0.226 · 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 designSimulation or modeling
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

Citations3
Published2024
Admission routes1
Has abstractyes

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