Catalytic Photothermal Hydrogenation of Carbon Dioxide to Liquid Fuels using Pt/GaN Prepared via Surface Organometallic Chemistry
Bibliographic record
Abstract
Abstract Renewable liquid fuels are expected to play a crucial role in transitioning to a more sustainable future. Their synthesis via the hydrogenation of CO 2 using solar energy emerges as a promising technology, combining both the utilisation of a renewable primary energy source and the (re)utilisation of a major greenhouse gas. In this context, GaN has attracted a lot of attention in harnessing solar energy to drive chemical transformations. In this work, we study GaN by 1 H solid‐state NMR spectroscopy, revealing the presence of terminal Ga‐OH, bridging Ga‐NH‐Ga, as well as Ga‐OH‐Ga surface functional groups and combinations thereof. With this knowledge in hand, we make use of surface organometallic chemistry (SOMC) to prepare a Pt/GaN catalyst with highly dispersed Pt nanoparticles on GaN. Under photothermal conditions using visible light (>320 nm), the synthesised Pt/GaN promotes the hydrogenation of CO 2 to C 2+ products such as acetone, EtOH, i PrOH, and acetic acid in a batch reactor at 60 °C and 1 bar of pressure, while the pristine GaN counterpart only produces minor amounts of MeOH and acetone. Furthermore, a recycling test was performed to showcase the stability of the catalyst over multiple batch reaction cycles.
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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".