<scp>CHEMampere</scp> : Technologies for sustainable chemical production with renewable electricity and <scp> CO <sub>2</sub> </scp> , <scp> N <sub>2</sub> </scp> , <scp> O <sub>2</sub> </scp> , and <scp> H <sub>2</sub> O </scp>
Bibliographic record
Abstract
Abstract The chemical industry must become carbon neutral by 2050, meaning that process‐, energy‐, and product‐related CO 2 emissions from fossil sources are completely suppressed. This goal can only be reached by using renewable energy, secondary raw materials, or CO 2 as a carbon source. The latter can be done indirectly through the bioeconomy or directly by utilizing CO 2 from air or biogenic sources (integrated biorefinery). Until 2030, CO 2 waste from fossil‐based processes can be utilized to curb fossil CO 2 emissions and reach the turning point of global fossil CO 2 emissions. A technology mix consisting of recycling technologies, white biotechnology, and carbon capture and utilization (CCU) technologies is needed to achieve the goal of carbon neutrality. In this context, CHEMampere contributes to the goal of carbon neutrality with electricity‐based CCU technologies producing green chemicals from CO 2 , N 2 , O 2 , and H 2 O in a decentralized manner. This is an alternative to the e‐Refinery concept, which needs huge capacities of water electrolysis for a centralized CO 2 conversion with green hydrogen, whose demand is expected to rise dramatically due to the decarbonization of the energy sector, which would cause a conflict of use between chemistry and energy. Here, CHEMampere's core reactor technologies, that is, electrolyzers, plasma reactors, and ohmic resistance heating of catalysts, are described, and their technical maturity is evaluated for the CHEMampere platform chemicals NH 3 , NO x , O 3 , H 2 O 2 , H 2 , CO, and C x H y O z products such as formic acid or methanol. Downstream processing of these chemicals is also addressed by CHEMampere, but it is not discussed here.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.068 | 0.017 |
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".