Porous Organic Polymers Derived from Ferrocene and Tetrahedral Silicon-Centered Monomers for Carbon Dioxide Sorption
Bibliographic record
Abstract
Herein, we present two novel ferrocene-containing porous organic polymers, FPOP-1 and FPOP-2, by the Heck reactions of 1,1′-divinylferrocene with two tetrahedral silicon-centered units, i.e., tetrakis(4-bromophenyl)silane and tetrakis(4′-bromo-[1,1′-biphenyl]-4-yl)silane. The resulting materials possess high thermal stability and moderate porosity with the Brunauer–Emmer–Teller (BET) surface areas of 499 m2 g−1 (FPOP-1) and 354 m2 g−1 (FPOP-2) and total pore volumes of 0.43 cm3 g−1 (FPOP-1) and 0.49 cm3 g−1 (FPOP-2). The porosity is comparable to previously reported ferrocene-containing porous polymers. These materials possess comparable CO2 capacities of 1.16 mmol g−1 (5.10 wt%) at 273 K and 1.0 bar, and 0.54 mmol g−1 (2.38 wt%) at 298 K and 1.0 bar (FPOP-1). The found capacities are comparable to, or higher than many porous polymers having similar or higher surface areas. They have high isosteric heats of up to 32.9 kJ mol−1, proving that the affinity between the polymer network and CO2 is high, which can be explained by the presence of ferrocene units in the porous networks. These results indicate that these materials can be promisingly utilized as candidates for the storage or capture of CO2. More ferrocene-containing porous polymers can be designed and synthesized by combining ferrocene units with various aromatic monomers under this strategy and their applications could be explored.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".