Hydrogen Production and Utilization in a Membrane Reactor
Bibliographic record
Abstract
Industrial hydrogenation consumes ~11 Mt of fossil-derived H2 gas yearly. Our group invented a membrane reactor to bypass the need to use H2 gas for hydrogenation chemistry. The membrane reactor sources hydrogen from water and drives reactions using renewable electricity. In this reactor, a thin piece of Pd separates an electrochemical hydrogen production compartment from a chemical hydrogenation compartment. The Pd in the membrane reactor acts as (i) a hydrogen-selective membrane, (ii) a cathode, and (iii) a catalyst for hydrogenation. Herein, we report the use of atmospheric mass spectrometry (atm-MS) and gas chromatography mass spectrometry (GC-MS) to demonstrate that an applied electrochemical bias across a Pd membrane enables efficient hydrogenation without direct H2 input in a membrane reactor. With atm-MS, we measured a hydrogen permeation of 73%, which enabled the hydrogenation of propiophenone to propylbenzene with 100% selectivity, as measured by GC-MS. In contrast to conventional electrochemical hydrogenation, which is limited to low concentrations of starting material dissolved in a protic electrolyte, the physical separation of hydrogen production from utilization in the membrane reactor enables hydrogenation in any solvent or at any concentration. The use of high concentrations and a wide range of solvents is particularly important for reactor scalability and future commercialization.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".