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Record W4319302258 · doi:10.1080/02786826.2023.2176739

High-accuracy effective density measurements of sodium methanesulfonate and aminium chloride nanoparticles using a particulate calibration standard

2023· article· en· W4319302258 on OpenAlexafffund
Véronique Perraud, James N. Smith, Jason S. Olfert

Bibliographic record

VenueAerosol Science and Technology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsScanning mobility particle sizerAerosolParticle (ecology)SodiumAnalytical Chemistry (journal)CalibrationChlorideChemistryParticle sizePolystyreneNanoparticleMaterials scienceChromatographyNanotechnologyParticle-size distributionPolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Methanesulfonate and aminium salts are commonly found in ambient nanoparticles and are often used as calibration standards. However, the effective densities of the particles generated from these standards are required if they are to be used to calibrate aerosol mass spectrometers or to estimate hygroscopic growth factors from electrodynamic balance experiments. A centrifugal particle mass analyzer and scanning mobility particle sizer were used in tandem (CPMA-SMPS) to measure the effective density of five salts. The effective densities were determined to be: sodium methanesulfonate, 1474 ± 13 kg m−3; methylammonium chloride, 1236 ± 29 kg m−3, monoethanolamine hydrochloride, 1136 ± 26 kg m−3; 1,4-diaminobutane dihydrochloride, 1135 ± 33 kg m−3; and NaCl was found to have a size-dependent effective density due to its non-spherical shape. It is also shown how Santovac® vacuum pump oil can be used to accurately calibrate the CPMA-SMPS system for particles less than 100 nm in diameter, which is a size range that has been problematic for calibration techniques relying on polystyrene latex spheres.Copyright © 2023 American Association for Aerosol Research

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)0.002
metaresearch head score (Gemma)0.001
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 score0.028
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.046
GPT teacher head0.311
Teacher spread0.265 · 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

Citations8
Published2023
Admission routes2
Has abstractyes

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