Ni Nanoparticles Supported Over Triazine Based Porous Organic Polymer for Selective CO<sub>2</sub> Photo‐Reduction to Methanol
Bibliographic record
Abstract
Abstract Porous organic polymers (POPs) have attracted substantial attentions over the years due to their exceptionally high specific surface areas, high chemical stability of the organic network and ease of surface functionalization with the desired organic groups. In this work, a triazine based POP (TrzPOP) was synthesized through Schiff base polycondensation reaction between a tetramine bearing triazine rings and phenolic–OH group rich dialdehyde. Ni nanoparticles (NiNP) synthesized independently were immobilized over TrzPOP to obtain the NiNP@TrzPOP composite catalyst. This TrzPOP possesses a high BET surface area of 1494 m2 g−1 and low band gap, which facilitates its role as visible light absorbent. NiNP@TrzPOP with N‐rich surfaces and phenolic–OH moieties displayed excellent photocatalytic activity in the CO2 photoreduction under mild reaction conditions. NiNP@TrzPOP composite selectively reduces CO2 to methanol. The turn over number (TON) for this photoreduction of CO2 under optimized reaction conditions is 270, which is considerably high comparing to other reported photocatalytic systems. Moreover, NiNP@TrzPOP composite catalytic system showed high recycling efficiency without noticeable decrease in its performance over five consecutive reaction cycles, suggesting its huge potential for large‐scale methanol synthesis from the renewable carbon source.
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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".