Towards a more sustainable future: Transitioning from thermochemical to electrochemical processes in clean energy technologies relevant to hydrogen‐containing fuels
Bibliographic record
Abstract
Abstract This review paper explores the transition from thermochemical to electrochemical processes in clean energy technologies, particularly focusing on hydrogen‐containing fuels, namely hydrogen, ammonia, and methanol. The main characteristics of the thermochemical and electrochemical technologies are compared, followed by a focus on specific approaches in production of each of these e‐fuels. Steam methane reforming, partial oxidation of hydrocarbons, coal and biomass gasification, as well as thermal decomposition and autothermal reforming processes are discussed for hydrogen thermochemical production. Electrochemical technologies for green hydrogen production are then described, including water electrolysis based on alkaline, proton exchange membrane, anion exchange, and solid oxide cells. The paper further compares the Haber–Bosch process with the electrochemical synthesis of ammonia, and discusses thermochemical technologies for methanol synthesis from syngas, comparing them to the two electrochemical approaches‐electrochemical CO 2 reduction and methane oxidation reaction. Additionally, approaches for extracting hydrogen from ammonia and methanol by electrochemical reforming are briefly discussed. The paper closes with the future prospects and challenges of the transition from the traditional thermochemical technologies to the more sustainable electrochemical processes. Despite the promising prospects of the electrochemical technologies, challenges such as high initial capital costs, the need for advanced materials, and scalability must be addressed. Ongoing research, policy incentives, and collaborative efforts are essential to overcome these barriers and facilitate the transition to a low‐carbon economy. In the meantime, the integration of these technologies represents a transformative approach to chemical manufacturing and energy management, offering a pathway towards more sustainable and versatile industrial practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".