Experimental Design and Statistical Analysis of AGET ATRP of MMA in Emulsion Polymer Reactor
Bibliographic record
Abstract
Abstract This study investigates atom transfer radical polymerization (ATRP) of methyl methacrylate (MMA) using activators generated by electron transfer (AGET) as the initiation technique in an emulsion well‐mixed 2L stirred tank reactor. The performance of the AGET ATRP of MMA is analyzed for five key independent variables, namely temperature, catalyst complex (CuBr2/dNbpy), initiator (EBiB), reducing agent (ascorbic acid), and surfactant (Brij 98). The reaction is carried out based on a two‐step polymerization procedure. A resolution 5 fractional factorial design technique is employed to assess the influence of the five independent variables on the monomer conversion, polymer average molecular weights, and polydispersity index (PDI). An input–output model is constructed from the data of 21 designed experimental tests. A statistical analysis of the results shows that the temperature is the most influential variable for the three output process responses. The initiator strongly affects the poly(methyl methacrylate) (PMMA) molecular weights. It is the least important key variable affecting MMA conversion and PDI, and the surfactant is the least one affecting PMMA Mn. On assessing the independent interactions effect, the interactions of temperature‐surfactant on conversion, and temperature‐initiator for PMMA Mn are considered. Process simulation in 3D mapping has demonstrated that model predictions agree well with experimental data.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".