A Review of Air Ionization with Negative Ions for Aerosol Removal and Inactivation of Airborne Microorganisms in Confined Spaces
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".