Highly Acidic Second Coordination Spheres Promote in situ Formation of Iron Phlorins Exhibiting Fast and Selective CO2 Reduction
Bibliographic record
Abstract
Protic functional groups in the secondary coordination sphere (SCS) can lower reaction barriers for reductive electrocatalytic transformations by directing proton transfers from an exogenous acid to a bound substrate. In a recent report with iron tetraphenylporphyrin (Fe‐TPP) catalysts bearing SCS amides, we found that pairing a more acidic SCS with a more acidic exogenous phenol acid provides the largest kinetic advantage for CO2 reduction to CO. Expanding on this precedent, we report a new series of Fe-TPP catalysts bearing highly acidic thioamides in the SCS (pKas of 17.5 ± 0.1 to 18.7 ± 0.1 in MeCN) and describe their catalytic activity in the presence of exogenous benzoic acid. Despite the uncommonly acidic conditions, we observe the selective 2e–/2H+ reduction of CO2 to CO with minimal competitive H2 evolution or porphyrin decomposition. Decreases in SCS pKa continue to provide significant rate enhancements, and the catalyst bearing the most acidic thioamide displays kinetics (log(kcat) = 8.65 ± 0.09) that are comparable to the leading molecular systems. Cyclic voltammetry, UV-Visible spectroelectrochemistry, kinetic analysis, and density functional theory show that the highly acidic SCS thioamide groups cause a change in catalyst speciation by promoting in situ protonation of the reduced iron porphyrin to form an iron phlorin. This iron phlorin is reduced to form a highly active and selective catalyst for CO2 reduction that operates at more positive potentials compared to traditional Fe‐TPP catalysts. This work therefore reveals a new role for the SCS in promoting beneficial changes to catalyst speciation and motivates further investigation of reduced metalloporphyrinoids.
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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 | 0.000 |
| 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".