Oxygen vacancies enhance non-Faradaic deprotonation in furfural electro-oxidation
Bibliographic record
Abstract
Electrochemical oxidation of biomass-derived furfural enables simultaneous production of furoic acid and hydrogen evolution, providing a sustainable strategy for integrated chemical synthesis and clean energy generation. However, its efficiency is limited by a non-Faradaic deprotonation process, which requires hydroxide (OH⁻) participation, as these ions become depleted at the catalyst interface under high reaction rates. Herein, we introduce oxygen vacancy (V O )-rich copper catalysts that enhance local OH⁻ concentration and accelerate key deprotonation steps. Theoretical simulations reveal that enhance the local OH⁻ concentration and accelerate the key deprotonation steps. Theoretical simulations reveal that high OH⁻ coverage lowers the energy barrier for gem-diolate anion (GDA) formation, and V Os increase the interfacial OH⁻ concentration by 1.76-fold at 100 mA cm −2 . Guided by these insights, we synthesized the Cu nanowires with tunable V O densities via controlled reduction. The V O-rich Cu catalyst achieves a current density of 208.1 mA cm −2 at 0.5 V vs. RHE, 4.8 times higher than V O-poor Cu, while maintaining a furoic acid Faradaic Efficiency of 99 %. Operando infrared spectroscopy confirms that V Os facilitate OH⁻ enrichment and enable efficient adsorption of FF on Cu 0 /Cu + active sites, synergistically promoting GDA formation and accelerating C–H bond cleavage. When integrated into a flow reactor, the V O-rich Cu enables bipolar hydrogen production at a cell voltage of just 0.25 V, compared to 1.73 V for water splitting, achieving an 85 % reduction in energy input. These findings establish V O engineering as an effective strategy for advancing biomass electrooxidation and low-voltage hydrogen generation. Oxygen vacancy (V O )-rich copper catalysts significantly enhance the electrochemical conversion of biomass-derived furfural to furoic acid by increasing local hydroxide ion concentration at the catalyst interface. The V O -rich Cu catalyst achieved a current density of 208.1 mA cm⁻ 2 (4.8 times higher than V O -poor Cu) while maintaining 99 % furoic acid efficiency, and enabled hydrogen production in a flow reactor at just 0.25 V compared to 1.73 V for conventional water splitting. • V O-rich Cu boosts OH⁻ enrichment, speeding FOR deprotonation process. • The V O-rich Cu catalyst achieves a FE FA of 99 % at 208.1 mA cm⁻ 2 , 4.8 times higher than V O-poor Cu. • V O-rich Cu enables bipolar H 2 production with 85 % energy reduction compared to conventional water splitting.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".