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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.23583 |
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| _version_ | 1866908737665499136 |
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| author | Restuccia, Paolo Pedretti, Enrico Benini, Francesca Loehlé, Sophie Righi, M. Clelia |
| author_facet | Restuccia, Paolo Pedretti, Enrico Benini, Francesca Loehlé, Sophie Righi, M. Clelia |
| contents | Phosphorus-based lubricant additives are used for protecting metallic contacts under boundary lubrication by forming surface films that reduce wear and friction. Despite their importance, the molecular mechanisms driving their friction-reducing effects remain unclear, especially for phosphate esters, whose molecular structure critically impact tribological behavior. In this study, we use machine learning-based molecular dynamics simulations to investigate the tribological performance of three representative phosphorus-based additives, Dibutyl Hydrogen Phosphite (DBHP), Octyl Acid Phosphate (OAP), and Methyl Polyethylene Glycol Phosphate (mPEG-P), on iron surfaces. The mPEG-P family is further analyzed by varying esterification degree and chain length. DBHP exhibits the lowest friction and largest interfacial separation, resulting from steric hindrance and tribochemical reactivity, as indicated by P-O bond cleavage and enhanced O-Fe interactions. In contrast, OAP and mPEG-P monoesters produce higher friction due to limited steric protection and reduced resistance to shear, leading to partial loss of surface coverage under extreme conditions. Within the mPEG-P family, multi-ester and longer-chain molecules significantly lower friction by maintaining larger separations, demonstrating that steric effects can outweigh surface reactivity under severe confinement. Overall, these results provide atomistic insights into how molecular architecture controls additive performance and support the design of phosphorus-based lubricants combining reactive anchoring with optimized steric structures for durable, low-friction interfaces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_23583 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Phosphorus-based lubricant additives on iron with Machine Learning Interatomic Potentials Restuccia, Paolo Pedretti, Enrico Benini, Francesca Loehlé, Sophie Righi, M. Clelia Materials Science Phosphorus-based lubricant additives are used for protecting metallic contacts under boundary lubrication by forming surface films that reduce wear and friction. Despite their importance, the molecular mechanisms driving their friction-reducing effects remain unclear, especially for phosphate esters, whose molecular structure critically impact tribological behavior. In this study, we use machine learning-based molecular dynamics simulations to investigate the tribological performance of three representative phosphorus-based additives, Dibutyl Hydrogen Phosphite (DBHP), Octyl Acid Phosphate (OAP), and Methyl Polyethylene Glycol Phosphate (mPEG-P), on iron surfaces. The mPEG-P family is further analyzed by varying esterification degree and chain length. DBHP exhibits the lowest friction and largest interfacial separation, resulting from steric hindrance and tribochemical reactivity, as indicated by P-O bond cleavage and enhanced O-Fe interactions. In contrast, OAP and mPEG-P monoesters produce higher friction due to limited steric protection and reduced resistance to shear, leading to partial loss of surface coverage under extreme conditions. Within the mPEG-P family, multi-ester and longer-chain molecules significantly lower friction by maintaining larger separations, demonstrating that steric effects can outweigh surface reactivity under severe confinement. Overall, these results provide atomistic insights into how molecular architecture controls additive performance and support the design of phosphorus-based lubricants combining reactive anchoring with optimized steric structures for durable, low-friction interfaces. |
| title | Phosphorus-based lubricant additives on iron with Machine Learning Interatomic Potentials |
| topic | Materials Science |
| url | https://arxiv.org/abs/2512.23583 |