Search NASA⌕ Search

DOE OSTI · 2538410

Current and candidate additives for environmentally acceptable lubricants—A review

Abstract

The world consumes approximately 40 million tons of lubricants annually, and nearly half of them end up in the environment because of leaks, discharges, and evaporation, causing significant environmental and economic impacts. The need for moving toward environmentally acceptable lubricants (EALs) is increasingly being recognized. Synthetic ester, polyalkylene glycol, vegetable oil, and water are among the base fluids approved by the U.S. Environmental Protection Agency for EALs. However, neat base fluids cannot fully meet the lubrication requirements without incorporating functional additives such as anti-wear, antioxidant, corrosion inhibitor, and viscosity modifiers. Both the performance and environmental compatibility of lubricant additives are critical. In this article, current and candidate EAL additives, both in liquid and solid forms, are reviewed, with a focus on their functionalities in friction and wear reduction, toxicity, and biodegradability. Finally, a perspective for future research on EAL additives is discussed.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wang, Wenbo [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000241111802), Qu, Jun [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000194663179). 2025-02-28. Current and candidate additives for environmentally acceptable lubricants—A review. https://doi.org/10.26599/frict.2025.9440988

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗