Photocatalytic CO <sub>2</sub> Reduction Using an Osmium Complex as a Panchromatic Self‐Photosensitized Catalyst: Utilization of Blue, Green, and Red Light
Bibliographic record
Abstract
Abstract The photocatalytic reduction of carbon dioxide (CO 2 ) represents an attractive approach for solar‐energy storage and leads to the production of renewable fuels and valuable chemicals. Although some osmium (Os) photosensitizers absorb long wavelengths in the visible‐light region, a self‐photosensitized, mononuclear Os catalyst for red‐light‐driven CO 2 reduction has not yet been exploited. Here, we discovered that the introduction of an Os metal to a PNNP‐type tetradentate ligand resulted in the absorption of light with longer‐wavelength (350–700 nm) and that can be applied to a panchromatic self‐photosensitized catalyst for CO 2 reduction to give mainly carbon monoxide (CO) with a total turnover number (TON) of 625 under photoirradiation ( λ ≥400 nm). CO 2 photoreduction also proceeded under irradiation with blue ( λ 0 =405 nm), green ( λ 0 =525 nm), or red ( λ 0 =630 nm) light to give CO with >90 % selectivity. The quantum efficiency using red light was determined to be 12 % for the generation of CO. A catalytic mechanism is proposed based on the detection of intermediates using various spectroscopic techniques, including transient absorption, electron paramagnetic resonance, and UV/Vis spectroscopy.
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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) | 0.000 | 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 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".