Integrating Materials Design and Operando Spectroscopy for the Development of Next Generation CO<sub>2</sub> Reduction and Biomass Valorization Catalytic Systems
Bibliographic record
Abstract
Electrochemical conversion of abundant feedstocks to fuels and value-added chemicals is rapidly gaining significance as a promising method to harness renewable electricity. Specific reactions within this context that my research group is focused on are the reduction of CO2 and oxidation of waste biomass. Because the design of new catalytic systems is inherently linked to a precise understanding of how these reactions proceed on heterogeneous surfaces, we put considerable efforts in developing methodology for opernado probing with Raman spectroscopy CO2 reduction and biomass valorization. This talk will detail our efforts in the design, electrochemical characterization, and spectroscopic investigation of 1) Composite systems of metallic nanoparticles decorated with functional organic ligands with steer CO2 reduction reactions down a select pathway on their surface 2) Tandem CO2 catalysis at the material-Metal Organic Framework (MOF) interface 3) Electrochemical oxidationof 5-hydroxymethylfurfural (HMF) on gold and transition metal oxide surface In all, I show how using opernado Raman spectroscopy provides the mechanistic information on surface reaction mechanisms that enhance our understanding of functional hybrid interfaces and provides avenues for future materials design within the context of electrosynthesis of fuels and chemicals. Figure 1
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".