MétaCan
Menu
Back to cohort
Record W2064251974 · doi:10.1080/08958370490277245

Physical and Chemical Characterization of Mn Phosphate/Sulfate Mixture Used in an Inhalation Toxicology Study

2004· article· en· W2064251974 on OpenAlexaff
Linda Ann Beaupré, Fariba Salehi, Joseph Zayed, Philippe Plamondon, Gilles L’Éspérance

Bibliographic record

VenueInhalation Toxicology · 2004
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsPolytechnique MontréalUniversité de Montréal
Fundersnot available
KeywordsSulfatePhosphateInhalationManganeseChemistryEnvironmental chemistryInhalation exposureScanning electron microscopeNuclear chemistryMineralogyToxicityMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

The use of methylcyclopentadienyl manganese tricarbonyl (MMT) in unleaded gasoline has given rise to numerous debates on the potential public health risk associated with manganese emissions. In fact, combustion products are mainly Mn phosphate, Mn sulfate, and Mn phosphate/sulfate mixture. Our research group did several inhalation studies in order to assess the toxicity of each Mn species. The objective of this study is to determine the physical and the chemical characteristics of a mixture of Mn phosphate/sulfate used in one of these inhalation toxicology studies. First, the mixture was analyzed by X-ray diffraction in order to obtain the specific peak of Mn phosphate and Mn sulfate. These peaks were used as reference. Second, samples of the mixture were collected on filters in the inhalation chamber at a concentration level of 3000 microg/m(3). They were analyzed by scanning electron microscopy (SEM), analytical transmission electron microscopy (ATEM), and x-ray energy-dispersive spectrometry (EDS) to show their size, morphology, and chemical composition. Results indicate that 33% of the particles were found to be agglomerated, while free particles accounted for 44% for Mn phosphate and 23% for Mn sulfate.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.742

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 integrity0.0000.000
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 head0.010
GPT teacher head0.268
Teacher spread0.257 · 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

Citations12
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueInhalation ToxicologySame topicElectrochemical Analysis and ApplicationsFrench-language works237,207