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Record W4413383318 · doi:10.1080/02786826.2025.2541649

Shape of sodium chloride particles as a function of drying rate

2025· article· en· W4413383318 on OpenAlexafffund
Nishan Sapkota, Rym Mehri, Joel C. Corbin, Steven N. Rogak, Timothy A. Sipkens

Bibliographic record

VenueAerosol Science and Technology · 2025
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsNational Research Council CanadaUniversity of British Columbia
FundersEnvironment and Climate Change CanadaGovernment of Canada
KeywordsSodiumChemistryFunction (biology)Chemical engineeringOrganic chemistryEngineeringBiology

Abstract

fetched live from OpenAlex

Sodium chloride (NaCl) plays an important role both in the laboratory as a calibration aerosol and in nature as a component of sea-spray particles. Despite their ubiquity, NaCl particles show significant variation in their shape depending on the drying rate. This work builds on the current literature by establishing the influence of drying rate on the distribution of effective density and dynamic shape factor of laboratory generated NaCl particles. A calibration factor is first established using a spherical aerosol composed of Santovac (polyphenyl ether), to account for instrumental uncertainties. A total of three different drying rates were tested: −97 RH/s (slow), −260 RH/s (intermediate), and −506 RH/s (fast). The effective density and dynamic shape factor results (in the transition regime) shows that slow dried particles attained a distinct cube-like shape (ρeff ≈ 2200–1600 kg/m3, χ ≈ 1–1.15), intermediate particles achieved much rounded corners while still showing some cube-like features (ρeff ≈ 2200–1800 kg/m3, χ ≈ 1.00–1.08), and fast dried particles retained its spherical morphology (ρeff ≈ 2200–2000 kg/m3, χ ≈ 1.00–1.02). The range of shapes observed is also influenced by particle size; smaller particles (<50 nm) were more spherical regardless of the drying rate. Additionally, bidimensional effective density analysis revealed shape variability within particles of the same size, suggesting a distribution of morphologies, especially for the slow drying case. These results are validated using TEM images at three approximately different mobility sizes − 100, 200 and 400 nm.Copyright © 2025 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)0.000
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 score0.036
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.269
Teacher spread0.258 · 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

Citations5
Published2025
Admission routes2
Has abstractyes

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