Renewable chemical & fuel production in a membrane reactor
Bibliographic record
Abstract
Chemical manufacturing is a major contributor to global greenhouse gas emissions because fossil fuels are used as both an energy source and a feedstock. Electrosynthesis presents an opportunity to use renewable electricity to drive chemical manufacturing instead of fossil fuel. Hydrogenation is an ideal candidate for electrification because the primary feedstock (hydrogen) can be sourced from water and renewable electricity. The challenge is that conventional electrochemical hydrogenation reactors have issues with reactant solubility, cell voltage, and separation of anodic reactions from cathodic reactions. Herein, I present a Pd membrane reactor as an alternative platform for driving hydrogenation reactions using electrochemistry. The Pd membrane addresses the issues associated with conventional electrochemical hydrogenation by using a dense Pd membrane/cathode to separate electrochemical hydrogen production from hydrogenation. In this thesis, I showcase how the Pd membrane reactor can enable the sustainable production of chemicals & fuels by investigating the hydrogenation of furfural, a biomass derived compound. Next, I investigate toluene hydrogenation for the transport and storage of hydrogen in liquid molecules. This approach to hydrogen storage enables the transport of renewable produced hydrogen using existing fuel infrastructure. Finally, I close a fundamental gap in Pd membrane reactor literature by identifying the mechanisms of H transfer from Pd–H to reactants in solution.
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 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 | 0.001 | 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) | 0.011 | 0.007 |
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".