New paradigms in electrocatalysis with alternative oxidation reactions
Bibliographic record
Abstract
Abstract Electrochemical systems are rapidly evolving beyond traditional water electrolysis, including cathodic hydrogen evolution (HER) and anodic oxygen evolution (OER) reactions, to enhance energy efficiency and generate value-added products simultaneously. Cathodic reactions now facilitate multifunctional reductions – ranging from CO 2 conversion into oxygenates and hydrocarbons to nitrogen (N 2 ) fixation, and nitrate (NO 3 − ) reduction – by tuning operational parameters. Hybrid co-reduction approaches, such as CO 2 /nitrile or CO 2 /nitrate, further enable the synthesis of valuable amines, amides and urea derivatives, among many others. Notably, even in the most advanced electrochemical configurations, the inclusion of the OER – or a functionally equivalent alternative – remains the most convenient oxidation reaction for maintaining charge balance within the cell. As highlighted in recent studies, alternative oxidation reactions (AORs) coupled with cathodic reduction reactions, such as CO 2 RR, HER, N 2 RR and NO 3 RR, are essential for overcoming the limitations of OER. These AORs include oxidation of biomass-derived alcohols and aldehydes, chlorine and water contaminants. In this perspective, we discuss the emerging promise of AORs – with a particular focus on aldehyde electrooxidation – as innovative alternatives to traditional OER. This strategy not only reduces the energy requirements for electrochemical hydrogen production by circumventing the sluggish and energy-intensive OER, but also enables concurrent hydrogen generation at both electrodes. Additionally, integrating AORs into electrolyzer design enables the direct coupling of CO 2 reduction at the cathode with high-value chemical transformations at the anode, offering new opportunities for process intensification and enhanced economic viability in the synthesis of sustainable fuels and chemicals.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".