ArticleACS omega2025
Enhancing Engine Performance and Sustainability: Gold Nanoparticles and Machine Learning for Biodiesel Optimization in Compression Ignition Systems.
Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nanotechnology and machine learning are transforming energy systems by enhancing engine efficiency and sustainability. The integration of advanced nanomaterials, such as gold nanoparticles (AuNPs), with predictive modeling offers new opportunities for optimizing biodiesel performance in compression ignition (CI) engines. This study aims to develop and evaluate a novel biodiesel blend using 50% waste cooking oil and 50% Simarouba oil, enhanced with gold nanoparticles, and to apply machine learning for performance prediction and optimization in CI engines. Gold nanoparticles were synthesized from a plant extract and characterized by UV-visible spectrophotometry, confirming their presence at an absorption peak of 650 nm. Biodiesel blends (B20, B40, B60, and B80) were prepared and tested in a single-cylinder CI engine at various compression ratios (14:1, 16:1, and 18:1) and loads. Key performance parameters, including brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC), were measured. An Extreme Gradient Boosting (XGBoost) machine learning model was trained on the experimental data to predict engine performance. The addition of AuNPs to biodiesel blends resulted in significant performance improvements: BTE increased by up to 6.57% for B20 at a 14:1 compression ratio, and BSFC decreased by up to 9.17% for B40 at a 16:1 compression ratio. The XGBoost model accurately predicted BTE and BSFC, with maximum errors of 4.17% and 3.53%, respectively. Gold nanoparticle-enhanced biodiesel blends offer improved CI engine performance and fuel efficiency. The use of XGBoost enables the reliable prediction of key performance metrics, reducing experimental costs and accelerating optimization. This integrated approach supports the development of sustainable, high-performance biofuels and advances the application of machine learning in energy systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.