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Record W3109107426 · doi:10.5194/acp-21-7791-2021

Soot PCF: pore condensation and freezing framework for soot aggregates

2021· article· en· W3109107426 on OpenAlexaff
Claudia Marcolli, Fabian Mahrt, B. Kärcher

Bibliographic record

VenueAtmospheric chemistry and physics · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of British Columbia
FundersH2020 Marie Skłodowska-Curie ActionsEuropean Commission
KeywordsSootIce nucleusCirrusIce crystalsAerosolCondensationParticle (ecology)NucleationChemical physicsCloud condensation nucleiRelative humidityChemical engineeringMaterials scienceMineralogyChemistryAtmospheric sciencesThermodynamicsCombustionGeologyMeteorologyPhysical chemistryPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Atmospheric ice formation in cirrus clouds is often initiated by aerosol particles that act as ice-nucleating particles. The aerosol–cloud interactions of soot and associated feedbacks remain uncertain, in part because a coherent understanding of the ice nucleation mechanism and activity of soot has not yet emerged. Here, we provide a new framework that predicts ice formation on soot particles via pore condensation and freezing (PCF) that, unlike previous approaches, considers soot particle properties, capturing their vastly different pore properties compared to other aerosol species such as mineral dust. During PCF, water is taken up into pores of the soot aggregates by capillary condensation. At cirrus temperatures, the pore water can freeze homogeneously and subsequently grow into a macroscopic ice crystal. In the soot-PCF framework presented here, the relative humidity conditions required for these steps are derived for different pore types as a function of temperature. The pore types considered here encompass n -membered ring pores that form between n individual spheres within the same layer of primary particles as well as pores in the form of inner cavities that form between two layers of primary particles. We treat soot primary particles as perfect spheres and use the contact angle between soot and water ( θ sw ), the primary particle diameter ( D pp ), and the degree of primary particle overlap (overlap coefficient, C ov ) to characterize pore properties. We find that three-membered and four-membered ring pores are of the right size for PCF, assuming primary particle sizes typical of atmospheric soot particles. For these pore types, we derive equations that describe the conditions for all three steps of soot PCF, namely capillary condensation, ice nucleation, and ice growth. Since at typical cirrus conditions homogeneous ice nucleation can be considered immediate as soon as the water volume within the pore is large enough to host a critical ice embryo, soot PCF becomes limited by either capillary condensation or ice crystal growth. We use the soot-PCF framework to derive a new equation to parameterize ice formation on soot particles via PCF, based on soot properties that are routinely measured, including the primary particle size, overlap, and the fractal dimension. These properties, along with the number of primary particles making up an aggregate and the contact angle between water and soot, constrain the parameterization. Applying the new parameterization to previously reported laboratory data of ice formation on soot particles provides direct evidence that ice nucleation on soot aggregates takes place via PCF. We conclude that this new framework clarifies the ice formation mechanism on soot particles in cirrus conditions and provides a new perspective to represent ice formation on soot in climate models.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.012
GPT teacher head0.218
Teacher spread0.206 · 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 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

Citations64
Published2021
Admission routes1
Has abstractyes

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