Electrochemical Production of Methyltetrahydrofuran, a Biofuel for Diesel Engines
Bibliographic record
Abstract
Methyltetrahydrofuran (MTHF) can be derived from non-edible biomass and used to replace diesel fuel. MTHF can be produced through the hydrogenation of furfural using hydrogen sourced from methane. Electrochemical hydrogenation (ECH) offers a method to source hydrogen from water, while bypassing the challenges associated with H2 handling and storage. Thus, if furfural were converted into MTHF through ECH, clean liquid fuels could be formed for internal combustion engines. The challenge is that ECH has not been proven to produce MTHF in meaningful yields due to solubility and thermodynamic constraints. We report here the successful electrochemically-driven hydrogenation of furfural to MTHF using a membrane reactor. This membrane reactor is able to produce MTHF production because the site of water electrolysis is separated from the site of hydrogenation so that hydrogenation can occur in organic media at high current densities. We show how the membrane reactor favors MTHF production at a selectivity of >75% at 200 mA/cm2, compared to conventional ECH using a single cell that operates at lower selectivities (<35%) and current densities (50 mA/cm2). We mapped out the reaction pathways to show that MTHF is produced from the deoxygenation of a furfuryl alcohol intermediate, a pathway that does not occur in single-cell ECH. This work shows the power of using the membrane reactor for producing liquid fuel alternative from a biomass-derived chemical, water, and electricity.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".