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Record W4414874416 · doi:10.3390/lubricants13100440

Advances and Challenges in Bio-Based Lubricants for Sustainable Tribological Applications: A Comprehensive Review of Trends, Additives, and Performance Evaluation

2025· review· en· W4414874416 on OpenAlexaff
Jay Patel, Kamlesh V. Chauhan, Sushant K. Rawal, Nicky P. Patel, Dattatraya Subhedar

Bibliographic record

VenueLubricants · 2025
Typereview
Languageen
FieldEngineering
TopicLubricants and Their Additives
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEuropean unionSustainable developmentTribologyMainstreamEfficient energy useMaterial efficiency

Abstract

fetched live from OpenAlex

Bio-based lubricants are rapidly gaining prominence as sustainable alternatives to petroleum-derived counterparts, driven by their inherent biodegradability, low ecotoxicity, and strong alignment with global environmental and regulatory imperatives. Despite their promising tribological properties, their widespread adoption continues to confront significant challenges, particularly related to oxidative and thermal instability, cold-flow behavior, and cost competitiveness in demanding high-performance applications. This comprehensive review critically synthesizes the latest advancements in bio-based lubricant technology, spanning feedstock innovations, sophisticated chemical modification strategies, and the development of advanced additive systems. Notably, recent formulations demonstrate remarkable performance enhancements, achieving friction reductions of up to 40% and contributing to substantial CO2 emission reductions, ranging from 30 to 60%, as evidenced by comparative life-cycle assessments and energy efficiency studies. Distinguishing this review from existing literature, this study offers a unique, holistic perspective by integrally analyzing global market trends, industrial adoption dynamics, and evolving regulatory frameworks, such as the European Union Eco-Label and the U.S. EPA Vessel General Permit, alongside technological advancements. This study critically assesses emerging methodologies for tribological evaluation and benchmark performance across diverse, critical sectors including automotive, industrial, and marine applications. By connecting in-depth technical innovations with crucial socio-economic and environmental considerations, this paper not only identifies key research gaps but also outlines a pragmatic roadmap for accelerating the mainstream adoption of bio-based lubricants, positioning them as an indispensable cornerstone of sustainable tribology.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.066
GPT teacher head0.333
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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