Au Nanoparticle-Anchored Hollow Nanospheres of a Single-Atomized Porphyrin-Covalent Organic Framework Hybrid for Boosting Photoreduction of CO<sub>2</sub> under Solar Irradiation
Bibliographic record
Abstract
Recent decades have seen a surge of interest in the photoreduction of carbon dioxide to fuels, and significant efforts have been made to develop efficient visible-light-driven photocatalysts. Nonetheless, efficient conversion of CO 2 with selectivity has proven to be a persistent challenge. Here, a hybrid plasmonic covalent organic framework (COF) is developed by combining a hollow porphyrin-based COF with a cobalt (Co) single atom decorated with plasmonic Au nanoparticles (COF-366-Co (H) /Au) to greatly enhance photoreduction of CO 2 . The photocatalytic redox processes in this hybrid system are driven toward generation of CO by the plasmon-induced energetic electron transfer, improved light harvesting, and efficient surface reactions. The COF-366-Co (H) /Au shows excellent activity, producing CO at a rate of up to 1200 μmol g –1 h –1 with a selectivity of ca. 98%. The COF-366-Co (H) /Au exhibits an AQY of 0.5% at 420 nm, which is one of the notable values reported in the literature. This approach demonstrates the use of hollow COFs in tandem with single-atom and plasmonic nanoparticles toward solar-to-fuel conversion.
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".