Plasma‐<scp>B</scp>ased Indoor Air Cleaning Technologies: The State of the Art‐<scp>R</scp>eview
Bibliographic record
Abstract
The negative impact of the presence of volatile organic compounds (VOCs) on indoor air quality has motivated researchers to develop different air treatment technologies. Although, building mechanical ventilation systems can provide a comfortable thermal indoor environment, they are not capable of removing the VOCs effectively. Thus, other components must be integrated with them to be able to carry out this function. Plasma‐based air treatment techniques are a series of processes in which a high voltage discharge is used for elimination of VOCs. Development of plasma‐based methods, and their capabilities for chemical gas decomposition, has motivated designers to employ these methods for indoor air purification. This paper addresses the outcomes of a critical literature review on plasma‐based air cleaner technologies, thermal to non‐thermal plasma and plasma catalyst, and their application for indoor environment VOCs removal. The reaction mechanism, effect of different parameters on the performance of the method, and abilities and limitations of these methods are discussed. Different types of reactors and the most common used catalysts are classified. The role of the presence of the catalyst in improving the non‐thermal plasma efficiency is reviewed. Finally, the scope of the future work to enhance the performance of this method for application in sustainable buildings is discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".