Tuning the speciation and geometry of iron porphyrin catalysts for electrochemical carbon dioxide reduction
Bibliographic record
Abstract
The production of solar fuels and industrial chemicals derived from carbon dioxide (CO₂) is a promising approach to create a more sustainable chemical industry while sequestering environmentally harmful CO₂ emissions. An attractive approach to CO₂ conversion is through electrocatalysis, where the reaction is driven by sustainable electricity under mild temperatures and pressures. Porphyrin transition metal complexes are a well-studied class of electrocatalysts capable of CO₂ reduction; notably, synthetic tunability of the porphyrin framework makes these catalysts a privileged platform for evaluating structure-property and structure-activity relationships. This thesis describes investigation into novel iron porphyrin catalysts designed with the goal of deepening understandings of structure-activity relationships; specifically, a bio-inspired approach has been taken by examining speciation phenomena and structural elements prevalent in biological systems but underexplored in these synthetic catalyst systems. A variety of electrochemical methods, spectroscopy, and coupled spectroelectrochemistry techniques are used to characterize the catalyst properties, determine speciation, and measure catalytic performance. I first outline a previously unrecognized aggregation phenomenon for molecular iron porphyrin catalysts in homogeneous systems; self-assembly of these catalysts into aggregates is demonstrated under commonly employed catalytic conditions which inhibits catalytic performance as a result of inaccessible active sites. The subsequent chapter further investigates the aggregation phenomenon by examining metalloporphyrin complexes with various steric profiles and structural elements, thus providing a comprehensive understanding of how structure influences aggregation behaviour and allowing for the tailoring of catalyst structure to prevent catalytic inhibition. I next investigate iron porphyrin catalysts featuring distortions from planarity. This study reveals that geometric tuning via deplanarization of the porphyrin macrocycle tunes a variety of properties and can alter the catalytic mechanism. Finally, a novel iron palladium porphyrin-pincer hybrid complex is presented which features a non-planar porphyrin macrocycle. This complex displays unique electrochemistry, coordination chemistry, and the potential for bimetallic cooperativity. Overall, this thesis outlines novel structure-property and structure-function relationships for homogeneous iron porphyrin catalysts, providing insight for future iterations of these catalysts as well as fundamental principles which can be applied to other catalyst classes including those more relevant to industrial implementation.
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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".