Inhibitor additives to mitigate fossil fuel emissions and its potential role in promoting the energy transition in global cities
Bibliographic record
Abstract
Abstract Emission-based fuels are a major source of greenhouse gases like CO 2 , NO x , CO, SO x , and particulate matter, exacerbating climate change and air pollution. While post-combustion technologies, such as catalytic converters, help reduce emissions, they are expensive and do not address pollutants at the source. Inhibitor additives present a promising solution by modifying combustion chemistry to suppress pollutant formation, enhance oxidation efficiency, and improve fuel performance. Research shows that inhibitors, such as metal-based catalysts (e.g., CeO 2 , Fe-based compounds), oxygenated additives, and halogen-based flame suppressants, reduce emissions by altering radical chain reactions and promoting complete combustion. When integrated with alternative fuels like biofuels, inhibitors further support energy transitions in global cities by enabling cleaner and more efficient combustion. However, challenges like fuel compatibility, secondary emissions, and long-term engine performance effects must be addressed. Understanding the mechanisms, efficiency, and limitations of inhibitors is crucial for optimizing them in sustainable combustion systems. As emission regulations tighten, inhibitor-based strategies offer a cost-effective, scalable solution to reduce fossil fuel-related pollution. This review explores recent advancements, practical applications, and future research directions to bridge the gap between fundamental science and real-world deployment in energy and transportation sectors.
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 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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".