Endergonic Hydrogenation at Ambient Conditions Using an Electrochemical Membrane Reactor
Bibliographic record
Abstract
Here, we determine how the hydrogen loading ( x ) of an electrochemical palladium membrane reactor (ePMR) varies with electrochemical conditions (e.g., applied current density, electrolyte concentration). We detail how x influences the thermodynamic driving force of an ePMR. These studies are accomplished by measuring the fugacity ( P ) of hydrogen desorbing from the palladium–hydrogen membrane and subsequently relating P to pressure–composition isotherms to determine x . We find that x increases with both applied current density and electrolyte concentration, but plateaus at a loading of x ≅ 0.92 in 1.0 M H 2 SO 4 at −200 mA·cm –2 . The validity of the fugacity measurements is supported experimentally and computationally by: (a) electrochemical hydrogen permeation studies; and (b) a palladium–hydrogen porous flow finite element analysis (FEA) model. Both (a) and (b) agree with the fugacity measurements on the following x -dependent properties of the palladium–hydrogen system during electrolysis: (i) the onset for spontaneous hydrogen desorption; (ii) the point of steady-state hydrogen loading; and (iii) the function describing hydrogen desorption between (i) and (ii). We proceed to detail how x defines the free energy of palladium–hydrogen alloy formation (Δ G ( x ) PdH ), which is a descriptor for the thermodynamic driving force of hydrogenation at the PdH x surface of an ePMR. A maximum value Δ G PdH of 11 kJ·mol –1 is observed, suggesting that an ePMR is capable of driving endergonic hydrogenation reactions. We empirically demonstrate this capability by reducing carbon dioxide to formate (Δ G CO 2 /HCO 2 H = 3.4 kJ·mol –1 ) at ambient conditions and neutral pH.
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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".