Ferrocene-Based Terpolyamides and Their PDMS-Containing Block Copolymers: Synthesis and Physical Properties
Bibliographic record
Abstract
Aromatic polyamides are well-known as high-performance materials due to their outstanding properties making them useful in a wide range of applications. However, their limited solubility in common organic solvents restricts their processability and becomes a hurdle in their applicability. This study is focused on the synthesis of processable ferrocene-based terpolyamides and their polydimethylsiloxane (PDMS)-containing block copolymers, using low-temperature solution polycondensation methodology. All the synthesized materials were structurally characterized using FTIR and 1H NMR spectroscopic techniques. The ferrocene-based terpolymers and block copolymers were soluble in common organic solvents, while the organic analogs were found only soluble in sulfuric acid. WXRD analysis showed the amorphous nature of the materials, while the SEM analysis exposed the modified surface of the ferrocene-based block copolymers. The structure–property relationship of the materials was further elucidated by their water absorption and thermal behavior. These materials showed low to no water absorption along with their high limiting oxygen index (LOI) values depicting their good flame-retardant behavior. DFT studies also supported the role of various monomers in the polycondensation reaction where the electron pair donation from HOMO of diamine monomer to the LUMO of acyl chloride was predicted, along with the calculation of various other parameters of the representative terpolymers and block copolymers.
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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".