Understanding the Role of Second Coordination Sphere Effects on Electrochemical CO<sub>2</sub> Reduction with Iron Porphyrins
Bibliographic record
Abstract
Enzymes achieve fast kinetics and high selectivity by carefully controlling the functional groups in the vicinity of the active site. Collectively, these peripheral groups are termed the Second Coordination Sphere (SCS). Synthetic chemists have long been inspired by the biological importance of the SCS and have demonstrated that the SCS can play an important role in a number of transformations promoted by molecular catalysts. In particular, it is well-known that the kinetics of electrochemical CO2 reduction depend on the presence of SCS functional groups capable of proton transfer, hydrogen bonding, or electrostatic interactions. However, many aspects still remain incompletely understood, such as the pKa requirements for protic SCS groups, the positional dependence of the SCS group with respect to the active site, and the role(s) of the SCS group in the catalytic reaction mechanism. This talk will showcase various SCS modifications made to iron porphyrins and, relying on a combination of molecular synthesis, electrochemistry, spectroscopy, and computational insights, will outline the various ways that the SCS can perturb reaction mechanisms and outcomes of electrochemical CO2 reduction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".