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Record W2807857912 · doi:10.1039/c8cp02966k

Measuring the size and complex refractive index of an aqueous aerosol particle using electromagnetic heating and cavity-enhanced Raman scattering

2018· article· en· W2807857912 on OpenAlexafffund
Aidan Rafferty, Thomas C. Preston

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAerosolRefractive indexRaman scatteringRADIUSRaman spectroscopyParticle (ecology)ScatteringOpticsMaterials scienceMie scatteringRayleigh scatteringParticle sizeAqueous solutionEffective radiusLight scatteringMolecular physicsChemistryPhysicsMeteorologyPhysical chemistry

Abstract

fetched live from OpenAlex

A quantitative understanding of light scattering by small homogeneous particles requires accurate knowledge of particle geometry and complex refractive index, m = n + ik. In weakly absorbing particles, k can be on the order of 10-9, which is well below the detection limit of almost all light scattering based instruments. Here, we describe a dual-beam optical trap that can simultaneously determine n, k, and the radius, s, of weakly absorbing aerosol particles. We utilize cavity-enhanced Raman scattering to determine n and s and electromagnetic heating from the trapping laser itself to determine k. The relationship between particle size, the trapping cell conditions, the parameters of the trapping laser, and electromagnetic heating is thoroughly discussed and it is shown that the proper choice of a light scattering model is necessary to retrieve accurate values of k when fitting measurements. The phenomenon of optical multistability and its connection to thermal locking and thermal jumping is investigated through both modeling and measurements as understanding this behavior is essential when interpreting results from electromagnetic heating experiments. Measurements are made on three different atmospheric aerosol model systems and k as low as 5.91 × 10-9 are found.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.640

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.242
Teacher spread0.226 · 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

Citations31
Published2018
Admission routes2
Has abstractyes

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