Plasma‐<scp>B</scp>ased Indoor Air Cleaning Technologies: The State of the Art‐<scp>R</scp>eview
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 it