Theory and simulation of thermodynamics and flow induced order in carbonaceous mesophase binary mixtures
Bibliographic record
Abstract
Carbonaceous mesophases (CMs) obtained from petroleum pitches and naphthalene precursors are mixtures of discotic nematic liquid crystals (DNLCs) employed to produce high performance carbon fibers (CFs). Natural pitches are usually polydisperse while synthetic ones are currently produced with very narrow molecular weight distributions.To design and control the final structure and mechanical properties of CFs three key parameters have to be considered: (i) characteristics of the raw material including the molecular weight, molecular interactions and the concentration of each species, (ii) the processing temperature and (iii) the extensional flow applied in the fiber spinning process. Experimental synthesis, processing, and characterization of CM materials are expensive due to the required equipment and operating conditions. Hence the computational modeling methodology adopted in this thesis is a cost effective tool for these novel materials. This thesis uses theory, mathematical modeling and computational simulations to characterize the effect of three above mentioned major factors on the orientational and molecular ordering behavior of a mixture of two monodisperse DNLCs, of relevance to the manufacturing of high performance CFs.The statistical mechanics Maier-Saupe model which effectively predicts the molecular ordering behavior of pure discotic systems is first extended to binary mixtures and then further extended to incorporate uniaxial extensional flow effects. Thermodynamic and thermo-rheological phase diagrams of binary lyotropic/thermotropic CM mixtures are predicted by this theory and partially validated by previous theoretical results and experimental observations. The generic thermo-rheological phase diagram which specifies the orientational structure of each component and their degree of molecular orientation under extensional spinning flow is obtained. X-ray diffraction intensity and orientational specific heat are also simulated in the present thesis, verified by available data and used as characterization tools for the orientation behavior of CM mixtures. In summary the thesis provides a new practical route for targeted structure-property relations for high performance CFs, through the chemistry and composition of the precursors, thus extending the traditional routes based on modifications of operating conditions and process geometry. At the fundamental level, the thesis presents the first dynamical model for DNLC mixtures. The models and results of the thesis are also applicable to rod-like systems under biaxial extensional flow, and DNLC under magnetic and electric fields.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".