Overcoming Challenges in Electrosynthesis Using High‐Throughput Electrochemistry: Hypervalent Iodine‐Mediated Phenol Dearomatization, a Case Study
Bibliographic record
Abstract
Abstract Despite many recent efforts, the field of organic electrosyn‐thesis faces important challenges due to the intricate nature of heterogeneous redox processes, the wide parameter space to be explored and the lack of standardized methods. To overcome these limitations, we developed a cost‐effective high‐throughput electrochemical (HTE) reactor capable of running 24 individually controlled parallel reactions. This system allows the rapid testing of electrochemical parameters on a given reaction, assessing not only yield but also reproducibility. Using the hypervalent iodine‐mediated dearomatization of phloretic acid as a demonstration of HTE capabilities, we ran more than 200 electrosyntheses in different experimental conditions and demonstrate the effect of parameters such as total charge transferred, current, electrode materials, electrolyte formulation and concentration, mediator formulation and concentration and electrochemical technique of oxidation. Notably, this report demonstrates that while catalytic amounts of iodine mediator can be used successfully, the reproducibility may be affected, which calls for a cautious approach when developing similar transformations. Using cyclic voltammetry, density functional theory, chronopotentiometry, and Raman spectroscopy, we shed light on the causes of this issue.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".