Synthesis and characterization of synthetic lubricants based on dibasic acid
Bibliographic record
Abstract
Abstract Synthetic esters have long been used in a variety of applications due to their excellent thermal stability, excellent cleanliness, natural lubricity, and polarity. In the present work, we aimed to prepare some synthetic base oils through preparation of different dibasic esters by esterification of dicarboxylic acids (adipic acid and azelaic acid) with different linear alcohols (hexanol, octanol, and decanol) and branched alcohol (2‐ethyl hexanol) at 120°C. The reaction yield ranges between 85% and 94%. Fourier‐transform infrared spectroscopy (FT‐IR) and proton nuclear magnetic resonance (1H‐NMR) spectroscopy were used to analyze the structures of the produced compounds. Using thermo gravimetric analysis (TGA), the heat stability of the produced esters was determined, and it was found that the prepared esters have high thermal stability. The degradation of the prepared esters takes place in the range between 300 and 600°C. The rheological behaviour of prepared esters shows Newtonian behaviours, which means that Newtonian fluids obey viscosity Newton's law. The viscosity is independent of the shear rate. The results showed that the lubricity properties, based on their pour point, flash point, and oxidation stability of the esters, were significantly affected by the linear and branched alcohols used. There is a slight increase in kinematic viscosity and viscosity index values with decreasing the internal chain length of the dibasic acid. The esters which were based on adipic acid such as C1 exhibited maximum values of VI: 187 compared to those which were based on azelaic acid such as F1 with VI: 182. Viscosity and viscosity index increases with increasing the number of carbon atoms of the used mono‐ol alcohols. Using branched alcohols gave almost the same viscosity results compared to using linear alcohol with the same number of carbons. Almost all prepared esters give pour point results ≤ −30°C.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".