Catalytic Photothermal Hydrogenation of Carbon Dioxide to Liquid Fuels using Pt/GaN prepared via SOMC
Bibliographic record
Abstract
Renewable liquid fuels are expected to play a crucial role in transitioning to a more sustainable future. Their synthesis via the hydrogenation of CO2 using solar energy emerges as a promising technology, that combines both the use of a renewable primary energy source and the (re)utilization 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 1H 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 synthesized Pt/GaN promotes the hydrogenation of CO2 to C2+ products such as acetone, EtOH, iPrOH, 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".