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Record W1633632907

Laboratory Studies of Deposition Mode Heterogeneous Ice Nucleation: Effect of Ice Nuclei Composition, Size and Surface Area

2010· dissertation· en· W1633632907 on OpenAlexfundno aff
Zaminhussein Kanji

Bibliographic record

VenueTSpace (University of Toronto) · 2010
Typedissertation
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoEuropean Science FoundationNational Science Foundation
KeywordsIce nucleusNucleationDeposition (geology)Composition (language)Surface (topology)Mode (computer interface)Materials scienceMineralogyGeologyChemical engineeringChemistryGeomorphologyEngineeringComputer scienceGeometryMathematicsArtOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

The indirect aerosol effect contributes to major uncertainties in determining the radiation budget of the earth. A large uncertainty is due to the formation of ice clouds onto natural or anthropogenic aerosols. Field studies have shown that mineral type particles are often associated with ice crystals in the mid-upper troposphere and given the long residence time in the atmosphere of dust particles (~2 weeks in the absence of precipitation), their contribution to ice formation processes is not fully defined. 
\nIn order to probe ice formation onto natural mineral dust in a setting where it could be suspended as aerosol, a new continuous flow diffusion chamber (CFDC) was built. This allowed investigations of the effects of total aerosol surface area and particle size. The CFDC was also used in an international inter-comparison of ice nucleation instrumentation to compare efficiencies of soot, biological aerosol (bacteria) and samples of natural desert dusts from different regions of the world. The laboratory observations were parameterized using nucleation rates (Jhet) and contact angles () as described by classical nucleation theory. 
\n\tFor both this experimental technique and a static one developed during the candidate’s Masters degree, mineral dust particulate proved to be the most efficient ice nuclei (IN) activating at RH with respect to ice (RHi) as low as 105% at T = 233 K. The efficiency varied with particle size and aerosol surface area (SA). Large particles or higher SA activated at lower RHi than small particles or lower SA. The static chamber was sensitive to the first ice event out of a large SA and therefore gave true onset RHi, which was lower than the onset defined by the CFDC studies, which was not sensitive to a single ice event. In addition the static chamber used a broader size range of particulate matter, including super micron particles while the CFDC particles sizes were restricted to below 0.5 µm. Soot and organic coated dust particles were inefficient IN compared to pure dust. Soot aerosols showed some efficiency at T < 233K where deposition ice formation was apparent. The hygroscopic organics had intermediate ice activity between dusts and alkyl-organics and soot. Bacteria aerosols were active in the deposition mode for T as high as 247 K. Contact angles () computed for ice germs forming onto natural mineral dust were small, 7<  < 29, at 223 K for RH ranging from ice to water saturation. 
\n\tIt was concluded that there is no single value for the onset of ice formation in the atmosphere via deposition freezing. The associated contact angles show that there is a distribution of active sites on IN and that not all active sites have the same affinity for initiation of ice formation even within the same aerosol type. This work provides evidence that deposition mode nucleation can be an alternate pathway to homogeneous nucleation when mineral aerosols are present in the troposphere since the high T - low RH conditions required for deposition mode nucleation are more easily encountered in the atmosphere than the low T - high RH required for homogeneous nucleation.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.764
Threshold uncertainty score0.997

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.247
Teacher spread0.237 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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