Synthesis and characterization of synthetic lubricants based on dibasic acid
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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 ( 1 H‐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 C 1 exhibited maximum values of VI: 187 compared to those which were based on azelaic acid such as F 1 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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it