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Record W2065148465 · doi:10.1063/1.2175958

Development and characterization of a laminar aerosol flow tube

2006· article· en· W2065148465 on OpenAlexafffund
Alexei F. Khalizov, Michael E. Earle, Wayde J. W. Johnson, G. D. Stubley, J. J. Sloan

Bibliographic record

VenueReview of Scientific Instruments · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsLaminar flowAerosolTurbulenceMechanicsReynolds numberLaminar flow reactorBuoyancyMaterials scienceStratosphereMixing (physics)MeteorologyFlow (mathematics)PhysicsOpen-channel flow

Abstract

fetched live from OpenAlex

We have developed a new laminar aerosol flow tube (AFT) to study transformations such as ice nucleation, deliquescence, and efflorescence in model atmospheric aerosols. The apparatus consists of four sections which can be independently cooled to reproduce temperature profiles relevant to the troposphere and stratosphere. An automatic control system maintains the average axial temperature along each section between 100 and 300K, within ±0.1K. Changes in aerosol composition, phase, and size distribution are monitored at the tube exit using infrared spectroscopy (AFT-IR). We used computational fluid dynamics simulations to investigate flow velocity and temperature distributions within the flow tube. Based on these computations, the final design was formulated to eliminate turbulent mixing zones and buoyancy-driven convection cells. The latter can occur even under conditions where the Reynolds number indicates laminar flow. In either case, recirculation causes aerosol residence times and temperature histories to be poorly defined, leading to erroneous interpretation of experimental measurements. The resulting AFT design used copper fins to reduce temperature gradients and axial mixing of aerosol and carrier gas flows in the inlet section to reduce turbulence. The performance of the new AFT is significantly better than for previous designs.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score0.719

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)0.0010.000

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 head0.008
GPT teacher head0.208
Teacher spread0.200 · 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

Citations14
Published2006
Admission routes2
Has abstractyes

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