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Record W4412884634 · doi:10.1021/acs.nanolett.5c02232

Multiparametric Characterization of Individual Suspended Nanoparticles Using Confocal Fluorescence and Interferometric Scattering Microscopy with Microfluidic Confinement

2025· article· en· W4412884634 on OpenAlexafffund
Eric Boateng, Erik Olsén, Albert Kamanzi, Yao Zhang, Bin Zhao, Pieter R. Cullis, Sabrina Leslie

Bibliographic record

VenueNano Letters · 2025
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaCanadian Institutes of Health ResearchNanoMedicines Innovation NetworkMitacsVetenskapsrådetKillam Trusts
KeywordsCharacterization (materials science)Confocal microscopyMicrofluidicsMaterials scienceMicroscopyInterferometryFluorescenceConfocalFluorescence microscopeNanotechnologyNanoparticleScatteringOpticsPhysics

Abstract

fetched live from OpenAlex

Detailed characterization of the size, mass, payload, and structure of suspended mRNA-lipid nanoparticles (LNPs) is necessary to improve our understanding of how these heterogeneous properties influence therapeutic efficacy and potency. Methods currently in use face limitations in reporting ensemble-average particle properties or requiring dedicated home-built microscopes that are beyond the reach of nanoparticle developers. In this work, we overcome these limitations by combining a commercially available confocal microscope and a convex lens-induced confinement (CLiC) instrument to achieve simultaneous characterization and correlation of the size, mass, refractive index, and nucleic acid payload of individual LNPs. We established the accuracy and precision of our method using nanosized beads and used it to investigate the size, payload, and water content of LNPs in different solvent pH. By employing readily available microscopy tools, we open the door to widespread adoption of our quantitative, in-solution nanoparticle characterization method.

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 score0.007
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.223
Teacher spread0.211 · 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

Citations6
Published2025
Admission routes2
Has abstractyes

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