Recent Advances in Power-to-X Technology for the Production of Fuels and Chemicals
Bibliographic record
Abstract
Environmental issues related to greenhouse gas emissions are progressively pushing the transition towards fossil-free energy scenario, in which renewable energies such as solar and wind power will unavoidably play a key role. However, for this transition to succeed, significant issues related to renewable energy storage have to be addressed. Power-to-X (PtX) technologies have gained increased attention since they actually convert renewable electricity to chemicals and fuels that can be more easily stored and transported. H2 production through water electrolysis is a promising approach since it leads to the production of a sustainable fuel that can be used directly in hydrogen fuel cells or to reduce carbon dioxide (CO2) in chemicals and fuels compatible with the existing infrastructure for production and transportation. CO2 electrochemical reduction is also an interesting approach, allowing the direct conversion of CO2 into value-added products using renewable electricity. In this review, attention will be given to technologies for sustainable H2 production, focusing on water electrolysis using renewable energy as well as on its remaining challenges for large scale production and integration with other technologies. Furthermore, recent advances on PtX technologies for the production of key chemicals (formic acid, formaldehyde, methanol and methane) and fuels (gasoline, diesel and jet fuel) will also be discussed with focus on two main pathways: CO2 hydrogenation and CO2 electrochemical reduction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".