Solar-Driven Reduction of Aqueous Protons Coupled to Selective Alcohol Oxidation with a Carbon Nitride–Molecular Ni Catalyst System
Bibliographic record
Abstract
Solar water-splitting represents an important strategy toward production of the storable and renewable fuel hydrogen. The water oxidation half-reaction typically proceeds with poor efficiency and produces the unprofitable and often damaging product, O2. Herein, we demonstrate an alternative approach and couple solar H2 generation with value-added organic substrate oxidation. Solar irradiation of a cyanamide surface-functionalized melon-type carbon nitride ((NCN)CNx) and a molecular nickel(II) bis(diphosphine) H2-evolution catalyst (NiP) enabled the production of H2 with concomitant selective oxidation of benzylic alcohols to aldehydes in high yield under purely aqueous conditions, at room temperature and ambient pressure. This one-pot system maintained its activity over 24 h, generating products in 1:1 stoichiometry, separated in the gas and solution phases. The (NCN)CNx-NiP system showed an activity of 763 μmol (g CNx)(-1) h(-1) toward H2 and aldehyde production, a Ni-based turnover frequency of 76 h(-1), and an external quantum efficiency of 15% (λ = 360 ± 10 nm). This precious metal-free and nontoxic photocatalytic system displays better performance than an analogous system containing platinum instead of NiP. Transient absorption spectroscopy revealed that the photoactivity of (NCN)CNx is due to efficient substrate oxidation of the material, which outweighs possible charge recombination compared to the nonfunctionalized melon-type carbon nitride. Photoexcited (NCN)CNx in the presence of an organic substrate can accumulate ultralong-lived "trapped electrons", which allow for fuel generation in the dark. The artificial photosynthetic system thereby catalyzes a closed redox cycle showing 100% atom economy and generates two value-added products, a solar chemical, and solar fuel.
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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".