Modeling uses and analysis of production scenarios for acrylonitrile‐butadiene (NBR) emulsions
Bibliographic record
Abstract
Abstract The first part of this article discusses different partitioning options used in modeling emulsion copolymerization of acrylonitrile and butadiene for the production of acrylonitrile‐butadiene rubber (NBR) in batch and continuous reactors. It is observed that the performance of the model using different partitioning methods for estimation of monomer concentration in the different phases (i.e., droplet, aqueous and particle) would be very similar by proper selection of the different parameters of the particular partitioning method. In the second part, the effects of important aspects (during the continuous production of NBR) such as desorption and monomer soluble impurities on polymer properties are discussed. The third part of the paper focuses on procedures and benefits of different kinds of reactor start‐ups, such as starting the reactor(s) full of water, half full of water, full of “batch recipe” or empty. All of the above studies and analysis are targeted towards their effect on principal emulsion polymer characteristics such as number‐ and weight‐average molecular weights, monomer conversion, copolymer composition (bound acrylonitrile), tri‐ and tetra‐functional branching frequencies, and number and average size of the polymer particles. POLYM. ENG. SCI., 2011. © 2011 Society of Plastics Engineers
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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 | 0.000 | 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 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".