A Theoretical Study of the Lubricating Abilities of 2D Layered Hydrogen Bonded Systems
Bibliographic record
Abstract
It has been estimated that 100 million terajoules of energy is used every year to combat friction. For perspective, this corresponds to one fifth of the world’s total energy expenditure. This significant amount leaves plenty of room for improvement. To reduce the environmental and financial impact of friction, lubricants are usually placed in mechanical systems and act as a barrier between moving parts. Lubricants come in many forms but they all have low energy slip mechanisms and resistance to conformational change under pressure. Chemicals with these properties can be predicted through some educated guesswork and computational simulations. Its advantageous to look at novel lubricants computationally because each small reaction can be analyzed, whereas in the lab it may be difficult to see the molecular mechanisms taking place in such short time spans. Additionally, computation is more environmentally friendly than hands-on testing because no chemicals are used. My research studies compounds found in nature and assesses their potential for use as lubricants. The focus of my studies has been on layered systems of melamine molecules that self-assemble into two-dimensional structures through hydrogen bonding. The layered nature of this system is similar to that of graphite – an effective layered lubricant; however, the reversibility of self-assembly may allow the layered structure to reform when disrupted during sliding to increase the robustness of the system. In this presentation, I will discuss the results of my simulations, with an emphasis on the structure of the system, the slip mechanism, slip energetics and friction forces.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 source (direct Gemma or distilled Codex), 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".