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Record W4387571577 · doi:10.26434/chemrxiv-2023-3r8sf

Catalytic efficiencies for atmospheric methane removal in the high-chlorine regime

2023· preprint· en· W4387571577 on OpenAlexaff
Luisa Pennacchio, Maarten van Herpen, Daphne Meidan, Alfonso Saiz‐Lopez, Matthew S. Johnson

Bibliographic record

VenueChemRxiv · 2023
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsImpact
Fundersnot available
KeywordsMethaneChemistryNOxChlorineCatalysisChlorideAnaerobic oxidation of methaneInorganic chemistryMixing (physics)Selective catalytic reductionEnvironmental chemistryPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Catalytic production of chlorine atoms from iron salt aerosols (ISA) has been suggested as a means of achieving atmospheric methane reduction (AMR). The feasibility of this approach its efficiency and the optimum conditions for deployment must be determined. Success is not obvious because it depends on nonlinear atmospheric free radical chain reactions; under some conditions added chlorine is known to increase methane lifetime. Here we evaluate the catalytic efficiency of atmospheric methane oxidation, initiated by the photocatalytic conversion of chloride to chlorine by iron chlorides Fe(III)Cl(3−n)n , using a OD box model. While HOx and high NOx behaviours are well known, a new regime is characterized by high ClOx conditions ypified by CH3O2 reacting with ClO rather than NO or HO2. We find that at NOx mixing ratios below 50 ppt or above 390 ppt, methane removal per iron atom is always net positive regardless of the Cl2 addition rate. However, between these NOx mixing ratios and for a chlorine production rate below 1×10^6 Cl2 /(cm3 s) the net effect is negative, increasing CH4 concentrations. The efficiencies seen in the model range from -0.26 to 2.63 CH4/Cl.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.440
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.036
GPT teacher head0.244
Teacher spread0.208 · 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 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
Published2023
Admission routes1
Has abstractyes

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