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Record W4411707573 · doi:10.14356/kona.2026013

A Review of Air Ionization with Negative Ions for Aerosol Removal and Inactivation of Airborne Microorganisms in Confined Spaces

2025· review· en· W4411707573 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueKONA Powder and Particle Journal · 2025
Typereview
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsYork UniversityUniversity of Manitoba
Fundersnot available
KeywordsAerosolIonMicroorganismIonizationMaterials scienceEnvironmental chemistryChemical engineeringBacteriaMeteorologyChemistryPhysicsBiologyOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

A comprehensive literature review was conducted to summarize and analyze the mechanisms and applications of air ionization for aerosol removal and inactivation of airborne microorganisms in confined spaces. This review focuses on engineered ionization systems (ionizers) that generate negative ions through corona discharge. Numerous studies have proven that air ionization is effective in removing aerosols and inactivating airborne microorganisms in confined spaces. Multiple physical, chemical, and biological processes may be involved in air ionization, including corona discharge and ion generation, attachment of ions to aerosol particles, transport of ions and aerosols in the air, electrostatic drift, deposition of aerosol particles on surfaces, and inactivation of biological agents if air ionization is used to prevent the spread of airborne pathogens. Each of these processes, as well as their interactions, is extremely complex, and only a limited number of studies have explored the interplays of these processes or attempted to integrate them into models that quantify the fundamental behavior of air ionization.

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.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.672
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.025
GPT teacher head0.323
Teacher spread0.297 · 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