Expanding the Scope of Electrosynthesis through Catalyst Design and Operando Spectroscopy
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. To this end, research group develops heterogeneous catalysts (e.g. nanomaterials, MOFs) geared for the reduction of CO2 and oxidation of biomass platforms to fuels and value-added chemicals. Further, we establish strategies in emergent areas of electrocatalysis such as electrochemically forming and cleaving C-N bonds. 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 operando probing of these systems with vibrational spectroscopy. In all, I show how using these experiments provides key mechanistic information on surface reaction mechanisms that enhance our understanding of functional interfaces and how the research provides avenues for future materials design within the context of 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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".