AgNPs Embedded in Porous Polymeric Framework: A Reusable Catalytic System for the Synthesis of α-Alkylidene Cyclic Carbonates and Oxazolidinones via Chemical Fixation of CO2
Bibliographic record
Abstract
Porous polymeric frameworks have received great interest over the past few years because of their nonstop growth as crystalline porous polymeric materials connected through covalent bonds and versatile utilities in diverse fields. The production of high-value organic compounds via sustainable and environment-friendly methods is an uphill struggle for researchers. The elegant strategy of using carbon dioxide as a C1 building block is an intriguing platform owing to its non-toxicity, easy accessibility, natural abundance, recyclability, non-flammability, and cheapness. Additionally, CO2 levels are regarded as the main contributor to the greenhouse effect (the most abundant greenhouse gas across the globe) and the aforementioned strategy needs to mitigate CO2 emissions. This present study describes the synthesis of silver nanoparticles (AgNPs) embedded in a porous polymeric framework, a reusable heterogeneous catalyst (recyclable over 5 times), TpMA (MC)@Ag. The synthesized catalyst is characterized by using FT-IR, PXRD, XPS, FE-SEM, TEM, EDAX, TGA DTA, and N2 sorption studies. Additionally, the catalysts can be easily recycled to generate the desired α-alkylidene cyclic carbonates and oxazolidinone compounds under solvent-free conditions. This research demonstrates the potential of nanoporous 2D porous polymeric framework-based materials in the area of catalysis, specially, in CO2 capture and chemical fixation. These findings offer a promising approach for the chemical fixation of CO2 into α-alkylidene cyclic carbonates and oxazolidinones from propargylic alcohols utilizing AgNPs embedded in a 2D catalyst, which functions as a potential heterogeneous catalyst under mild conditions (e.g., solvent-free approach).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".