Electrochemical Biomass Valorization on Gold-Metal Oxide Nanoscale Heterojunctions Enables Investigation of both Catalyst and Reaction Dynamics with Operando Surface-Enhanced Raman Spectroscopy
Bibliographic record
Abstract
<p>The electrochemical oxidation of biomass platforms such as 5-hydroxymethylfurfural (HMF) to value-added chemicals is an emerging clean technology. However, mechanistic knowledge of this reaction in an electrochemical context is still lacking and <i>operando</i> studies are even more rare. In this work, we utilize core-shell gold-metal oxide nanostructures which enable<i> operando</i> surface-enhanced Raman spectroelectrochemical studies to simultaneously visualize catalyst material transformation and surface reaction intermediates under an applied voltage. As a case study, we show how the transformation of NiOOH from ~1-2 nm amorphous Ni layers facilitates the onset of HMF oxidation to 2,5-furandicarboxylic acid (FDCA), which is attained with near 100% Faradaic efficiency in 1M KOH. In contrast to the case in 1M KOH, NiOOH formation is suppressed, and consequently HMF oxidation is sluggish 10 mM KOH, even at high potentials. <i>Operando </i>Raman experiments elucidate how surface adsorption and interaction dictates product selectivity and how the surface intermediates evolve with applied potential. We further extend our methodology to investigate NiFe, Co, Fe, and CoFe catalysts and demonstrate that high water oxidation activity is not necessarily correlated with excellent HMF oxidation performance and highlight catalytic factors important for this reaction such as reactant-surface interactions and catalysts’ physical and electronic structure. </p>
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".