Cationic Defect Engineering for Promoting Oxidation of 5-Hydroxymethylfurfural While Passivating OER
Bibliographic record
Abstract
Electrochemical organic oxidation has shown great industrial potential due to its green, low-carbon, and energy-efficient advantages. However, the competing oxygen evolution reaction (OER) severely impacts the faradaic efficiency and conversion rate of organic oxidation reactions. In this work, we report a method that can promote the oxidation of 5-hydroxymethylfurfural (HMFOR) while suppressing the OER, which is achieved by etching NiMnFe-LDH with N, N -dimethylacetamide (DMF), resulting in the formation of numerous cationic defects. Specifically, at a current density of 50 mA cm –2, the applied potential for HMFOR decreased by 50 mV, while the OER potential increased by 30 mV. In situ electrochemical impedance spectroscopy found faster reaction kinetics for d-NiMnFe-layered double hydroxide (LDH) compared to that of NiMnFe-LDH in HMFOR, whereas an opposite trend was observed in the OER, confirming that the DMF treatment has opposite effects on the transportation of organic molecules and OH – . To further investigate the reaction pathways and evolution of intermediates during HMFOR, in situ infrared spectroscopy and theoretical calculations were conducted, which demonstrate that cationic defects not only significantly enhance the adsorption of intermediates but also lower the reaction energy barrier, thus accelerating the reaction rate of HMFOR. This work provides a potential strategy for developing industrial-grade electrocatalysts for high current densities.
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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 | 0.000 | 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 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".