Organic solvent‐free catalytic hydrogenation of diene‐based polymer nanoparticles in latex form: Part I. Preparation of nano‐substrate
Bibliographic record
Abstract
Abstract Poly(butadiene‐ co ‐acrylonitrile) (NBR) nanoparticles were synthesized in a semibatch emulsion polymerization system using Gemini surfactant trimethylene‐1,3‐bis (dodecyldimethylammonium bromide), referred to as Gemini‐type surfactant (GS) 12‐3‐12, as the emulsifier. In this polymerization system, an enhanced decomposition rate of initiator ammonium persulfate was achieved even under the low temperature of 50 °C which is attributed to the acidic initiation environment provided using GS 12‐3‐12. The microstructure and copolymer composition of the polymer nanoparticles were characterized by Fourier‐transformed infrared and 1 H nuclear magnetic resonance spectroscopy. The effects of the surfactant concentration on the particle size, zeta potential, polymerization conversion, copolymer composition, molecular weight, and glass transition temperature ( T g ) were investigated. It was found that the particle diameter can be controlled by the surfactant concentration and monomer/water ratio and particle sizes below 20 nm can be reached. The obtained latex particles exhibit a spherical morphology. A kinetic study of the copolymerization reaction was carried out, which indicated that an azeotropic composition was produced. The synthesized fine NBR nanoparticles can be employed as the nano substrate for a subsequent hydrogenation process so as to overcome the challenge involved in the field of latex hydrogenation of polymers, which can be found in a related report: Organic Solvent‐Free Catalytic Hydrogenation of Diene‐based Polymer Nanoparticles in Latex Form: Part II. Kinetic Analysis and Mechanistic Study. © 2012 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem, 2012
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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 | 0.000 | 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".