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Record W4281778576 · doi:10.5194/amt-2022-153

The Microfluidic Ice Nuclei Counter Zürich (MINCZ): A platform for homogeneous and heterogeneous ice nucleation

2022· preprint· en· W4281778576 on OpenAlexfundno aff
Florin N. Isenrich, Nadia Shardt, Michael Rösch, Julia Nette, Stavros Stavrakis, Claudia Marcolli, Zamin A. Kanji, Andrew J. deMello, Ulrike Lohmann

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaEidgenössische Technische Hochschule Zürich
KeywordsIce nucleusNucleationMicrofluidicsPolydimethylsiloxaneMaterials scienceFreezing pointHomogeneousChemical physicsThermalNanotechnologyChemical engineeringMechanicsChemistryThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract. Ice nucleation in the atmosphere is the precursor to important processes that determine cloud properties and lifetime. Computational models that are used to predict weather and project future climate changes require parameterizations of both homogeneous nucleation (i.e., in pure water) and heterogeneous nucleation (i.e., catalysed by ice-nucleating particles, INPs). Microfluidic systems have gained momentum as a tool for obtaining such parameterizations and gaining insight into the stochastic and deterministic contributions to ice nucleation. In this regard, polydimethylsiloxane (PDMS) devices are typically used to generate droplets in microchannels that are then cooled and monitored “on-chip”. However, using PDMS has two drawbacks. First, it has a low thermal conductivity that generates temperature gradients within a PDMS chip upon cooling from below, which can lead to increased temperature uncertainty at the droplets’ location. Second, it readily absorbs water and is gas permeable, which compromises the stability of droplets over extended timescales. To overcome these shortcomings, we have developed a new instrument: the Microfluidic Ice Nuclei Counter Zürich (MINCZ). In MINCZ, droplets are generated using a PDMS chip, but are then stored in fluoropolymer tubing that is relatively impermeable to water and solvents. Droplets within the tubing are cooled in an ethanol bath that ensures efficient heat transfer and reduces uncertainty in droplet temperature. Herein, we describe the design of MINCZ, which fulfils the following requirements: (i) high accuracy and precision in measuring droplet temperatures within 0.2 K; (ii) ability to reach the homogeneous freezing point of pure water, with a median freezing temperature of 237.3±0.1 K for droplets with a diameter of 75 μm; and (iii) the ability to simultaneously perform several freeze–thaw cycles on hundreds of droplets. These characteristics allow to narrow the reported spread in nucleation rates as a function of temperature in past work, to detect mediocre and poor ice-nucleating particles at any temperature above that of homogeneous freezing, and to investigate the stochastic behaviour of nucleation. We validate MINCZ by measuring homogeneous freezing temperatures of water droplets and heterogeneous freezing temperatures of aqueous suspensions containing microcline, a common and effective INP in the atmosphere. In the future, MINCZ will be used to investigate the stochastic and deterministic behaviour of INPs, motivated by a need for better-constrained parameterizations of ice nucleation in weather and climate models, where the presence or absence of ice influences cloud optical properties and precipitation formation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.231
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations3
Published2022
Admission routes1
Has abstractyes

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