<i>Operando</i> vibrational spectroscopy for electrochemical biomass valorization
Bibliographic record
Abstract
Electrocatalysis is a promising route to generate fuels and value-added chemicals from abundant feedstocks powered by renewable electricity. The field of electrocatalysis research has made great progress in supplementing electrocatalyst development with operando vibrational spectroscopic techniques, those carried out simultaneously as the reaction is occurring. Such experiments unveil reaction mechanisms, structure-activity relationships and consequently, accelerate the development of next generation electrocatalytic systems. While operando techniques have now been extensively applied to water electrolysis and CO2 reduction, their application to the emerging area of biomass valorization is rather nascent. The electrocatalytic conversion of biomass can provide an alternate, environmentally friendly route to the chemicals which power our society, but this field still requires much growth before the envisioned technologies are economically competetive with thermochemical routes. Within this context, a growing body of work has begun to translate the methodology and concepts from water/CO2 electrolysis to biomass valorization to elucidate links between catalyst structure, adsorbed surface intermediates, and the resultant catalytic performance. The reactions of interest here include the upgrading of biomass platforms such a 5-hydroxymethylfurfural or glycerol to value-added chemicals. In this feature article we highlight these efforts and provide a critical view on the steps necessary to take to further progress the field. We further show how the knowledge derived from these studies can be translated to a plethora of other organic transformations to forge new avenues in renewable energy electrocatalysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".