Dyadic Ru-based nanomaterials for visible light-driven photocatalytic hydrogen evolution
Bibliographic record
Abstract
• Dyadic photosensitizer-nanoparticle hybrids synthesized via an organometallic route. • First demonstration of visible light HER photocatalysis with dyadic hybrid colloids. • Hybrid photocatalysts exhibit long-term HER activity and stability in alkaline media. • Structural evolution of the hybrid colloids correlates with the H 2 production rates. Visible light-driven water splitting is an appealing strategy to store renewable energy in the chemical bonds of molecular hydrogen. In this regard, the development of photocatalytic architectures where charge transfer and recombination can be controlled represents a key challenge. The surface functionalization of Ru/RuO 2 nanoparticles (NPs) with the [Ru(2,2′-bpy) 2 (qpy)](PF 6 ) 2 photosensitizer (PS), yielding PS-NPs “dyadic” hybrid nanomaterials, represents a promising strategy. Four HER photocatalysts with different PS:NPs ratios are synthesized and thoroughly characterized by analytical and spectroscopic techniques. X-ray photoelectron spectroscopy (XPS) reveals the covalent binding of the PS to the NPs surface. Analysis of the photocatalytic performance in aqueous triethanolamine (TEOA) shows that the activation of the nanocatalyst (RuO 2 reduction) and the hydrogen evolution rate improves when the PS loading increases. Under visible-light irradiation, the nanomaterials with higher PS loading show sustained production of hydrogen for at least 80 h. The morphological and compositional evolution of the hybrid nanomaterials under photocatalytic conditions is studied and correlated with hydrogen production rates over time, pointing to a sequential leaching of PS from the nanomaterials surface. Additionally, photophysical experiments allow attaining an insight into the photochemical mechanism, which involves oxidative quenching with a fast electron injection, but also fast back electron transfer.
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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 | 0.001 |
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".