Further Understanding the Roles of Solvent, Brønsted Acids, and Hydrogen Bonding in Iron Porphyrin-Mediated Carbon Dioxide Reduction
Bibliographic record
Abstract
Improving our understanding of how molecules and materials mediate the electrochemical reduction of carbon dioxide (CO 2 ) to upgraded products is of great interest as a means to address climate change. A leading class of molecules that can facilitate the electrochemical conversion of CO 2 to carbon monoxide (CO) is iron porphyrins. These molecules can have high rate constants for CO 2 -to-CO conversion; they are robust, and they rely on abundant and inexpensive synthetic building blocks. Important foundational work has been conducted using chloroiron 5,10,15,20-tetraphenylporphyrin (FeTPPCl) in N, N -dimethylformamide (DMF) solvent. A related and recent report points out that the corresponding perchlorate complex, FeTPPClO 4, can have superior function due to its solubility in other organic solvents. However, the importance of hydrogen bonding and solvent effects was not discussed. Herein, we present a detailed kinetic study of the triflate (CF 3 SO 3 – ) complex of FeTPP in DMF and in MeCN using a range of phenol Brønsted acid additives. We also detected the formation of Fe(III)TPP–phenolate complexes using cyclic voltammetry experiments. Importantly, our new analysis of apparent rate constants with different added phenols allows for a modification to the established mechanistic model for CO 2 -to-CO conversion. Critically, our improved model accounts for hydrogen bonding and solvent effects by using simple hydrogen bond acidity and basicity descriptors. We use this augmented model to rationalize function in other reported porphyrin systems and to make predictions about operational conditions that can enhance the CO 2 reduction chemistry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".