Examining the Importance of Hydrogen Bonding and Proton Transfer in Iron Porphyrin-Mediated Carbon Dioxide Upconversion
Bibliographic record
Abstract
Conspectus The title should give a sense of the “big picture” of this Account, but what is it really about? An unexpected change in research direction? A series of courageous and creative students? A team taking on challenging problems in chemistry? The answer is a definite “yes” to all of the above. More specifically, the problem in which we are interested is the upconversion or valorization of carbon dioxide. This problem has captured the attention of a great many chemists in earnest following the gas crisis of the 1970s and more recently galvanized due to climate concerns arising from the ongoing release of anthropogenic carbon. Addressing the problem of atmospheric carbon accumulation requires effort in two very broad areas: capture and conversion. Storage is an alternative to conversion, but this eliminates the opportunity to use what might be otherwise a waste product. Our group has investigated a series of modified versions of iron(III)–5,10,15,20-tetraphenylporphyrin (FeTPP) that can convert CO 2 to carbon monoxide, which is a versatile and useful precursor for other syntheses. Following pioneering work from Savéant and his colleagues in the 1990s and thereafter, we started with a simple question: how many pendent ancillary groups that can donate H-bonds or protons are needed to support efficient CO 2 -to-CO conversion? Using a molecule with only one 2-hydroxylphenyl group, we demonstrated that the single prepositioned −OH group gave rise to efficient turnover, but only when experiments were carried out in a weakly H-bond-accepting solvent system. In other words, the ability of a solvent to accept H-bonds can impede CO 2 reduction. We followed up with a deeper investigation of the influence of H-bonding interactions with external acids in FeTPP-mediated CO 2 reduction. Savéant’s framework mechanism appears to be independent of solvent, and rate differences can be approximated by considering H-bonding equilibria. Following that work, we sought to better understand the minimum catalyst design requirements with respect to internal H-bond/proton donors. To that end, we produced all possible isomers of tetraarylpoprhyrins with 2,6-dihydroxyphenyl + phenyl groups. All else being equal, the complexes with a formally trans orientation of the 2,6-dihydroxyphenyl groups performed the best. Most recently, we surveyed the roles of internal and external Brønsted acids with different p K a values. Surprisingly, the best-performing catalysts have more weakly acidic internal groups. Overall, our work has demonstrated that CO 2 reduction mediated by porphyrin catalysts can be improved by considering solvent H-bonding, the orientation of internal H-bonding groups, and the balance of the p K a values of internal and external acids. The future for molecular electrocatalysts is promising as more ideas emerge about how to design molecules and conditions for CO 2 reduction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".