An energy-efficient heat-driven technology for airborne pathogen inactivation: integration of two-stage HVAC filtration with thermal disinfection
Bibliographic record
Abstract
Air filters are widely used in HVAC systems to control transmission of airborne microorganisms. However, their application is associated with frequent maintenance, accumulation and release of bioaerosols. To address these issues, the present study proposes a novel thermal disinfection approach integrated into a two-stage filtration system comprising a MERV 8 pre-filter and either MERV 11 or MERV 13 main filter. To maintain continuous airflow, the system employs two side-by-side units with the same filters sequentially disinfected via forced convection heating while maintaining continuous filtration. A closed-loop aerosol wind tunnel, based on the ASHRAE 52.2 standard, was implemented to evaluate the filtration efficiency and pressure drop of the system at various configurations and operating modes, including normal operation (both units open) and a disinfection period (one unit closed). Moreover, a heat transfer setup was developed to measure the required time and energy consumption for meeting the literature-based thermal inactivation criteria (10 min exposure to 65 °C) across three inter-filter distances. A comparison of the theoretical correlation for estimating the overall filtration efficiency of multi-stage systems with experimental results confirmed good agreement in normal operation, with some deviations during the disinfection period due to flow redistribution. Furthermore, changing the inter-filter distance has relatively minor impacts on the overall filtration efficiency and pressure drop compared to its effect on the disinfection process. Consequently, the energy performance of the disinfection system should be considered the primary criterion when determining the optimal distance between the pre-filter and the main filter.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".