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Record W2326446407 · doi:10.1021/es403901r

On the Kinetics of the Absorption of Nitric Oxide into Ammoniacal Cobalt(II) Solutions

2014· article· en· W2326446407 on OpenAlexaff
Hesheng Yu, Zhongchao Tan

Bibliographic record

VenueEnvironmental Science & Technology · 2014
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCobaltKineticsNitric oxideChemistryInorganic chemistryAbsorption (acoustics)Environmental chemistryNuclear chemistryMaterials scienceOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Experiments were conducted using a custom double-stirred tank reactor to determine the rate constants of reactions between nitric oxide (NO) and both pentaaminecobalt(II) and hexaaminecobalt(II) at temperatures of 298.2 and 303.2 K and pH levels between 8.50 and 9.87 under atmospheric pressure. The NO concentration of simulated flue gas stream ranged from 400 to 1400 ppmv. Ammoniacal cobalt(II) solutions were prepared by adding aqueous ammonia into a cobalt(II) nitrate solution in the presence of concentrated ammonium nitrate. The reaction rate constants were calculated with an enhancement factor for gas absorption associated with parallel chemical reactions. The results showed that the reaction between NO and pentaaminecobalt(II) was first order with respect to both the NO and the pentaamminecobalt(II) ion. Similarly, the reaction between NO and hexaamminecobalt(II) was first order with respect to both the NO and the hexaamminecobalt(II) ion. The forward reaction rate constants of these two reactions were 6.43 × 10(6) and 1.00 × 10(7) L · mol(-1) · s(-1) at 298.2 K, respectively, and increased to 7.57 × 10(6) and 1.12 × 10(7) L · mol(-1) · s(-1) at 303.2 K, respectively. Ammoniacal cobalt(II) solutions also have the potential to simultaneously remove CO2, SO2, and NOx from postcombustion flue gas.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.009
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.010
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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.

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

Citations25
Published2014
Admission routes1
Has abstractyes

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