The role of tunable secondary coordination sphere (thio)amides and unconventional exogenous acids during iron tetraphenylporphyrin catalyzed CO₂ reduction
Bibliographic record
Abstract
Electrochemical CO₂ reduction offers a sustainable strategy for converting this inert molecule into value-added products; however, significant kinetic barriers hinder efficient and selective CO₂ conversion. The activity of electrocatalysts can be enhanced by modification of the Secondary Coordination Sphere (SCS), which often consists of proteogenic groups in the catalyst periphery. Using catalysts bearing SCS (thio)amides of tunable acidity and positioning, this work investigates how a protic SCS influences kinetics, selectivity, and catalyst speciation during iron tetraphenylporphyrin (FeTPP)-catalyzed CO₂ reduction. Chapters 2, 5, and 6 investigate methods to enhance catalytic activity. Chapter 2 describes the kinetics of FeTPP catalysts bearing SCS amides of tunable acidity in the presence of exogenous phenols of tunable pKa; it is shown that pairing a more acidic SCS amide with a more acidic exogenous acid provides the fastest rates of CO₂ reduction. Chapter 5 demonstrates that installation of highly acidic SCS thioamides can promote catalytic rates that are comparable to the leading molecular systems. These SCS thioamides also change catalyst speciation by promoting protonation of the reduced porphyrin to form an iron phlorin that operates at unexpectedly positive potentials. Additional work in Chapter 6 demonstrates that the structure of the exogenous acid significantly influences catalytic activity. Chapters 4 and 5 describe how SCS donor positioning and acidity influence CO versus H₂ selectivity. Positional effects were investigated with four isomers bearing amides at varying positions around the porphyrin core: NH donors are placed at either the meta or ortho position of the meso aryl porphyrin ring, as well as proximal or distal to the porphyrin. Under highly acidic conditions, the ortho-distal or both meta isomers produce H₂ as a major product; however, the ortho-proximal isomer shows high CO selectivity. More acidic ortho-proximal amides promote the highest selectivity for CO. Kinetic analysis of the competing CO and H₂ evolution pathways suggests that product selectivity largely operates under kinetic control. Altogether, this work demonstrates how SCS donor positioning and acidity—as well as exogenous acid identity—can beneficially alter the rates and selectivity of FeTPP-catalyzed CO₂ reduction and will guide the design of rapid and highly selective electrocatalysts.
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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".