Adoption of Electrochemistry within the Pharmaceutical Industry: Insights from an Industry-Wide Survey
Bibliographic record
Abstract
This article presents the results of a comprehensive survey on the adoption of electrochemistry among 17 major pharmaceutical companies. The study examined key areas, including motivation, vision, personnel, utilization, explored reactions, scale-up experience, and equipment, with a focus on identifying gaps that hinder the realization of electrochemistry’s full potential. The survey findings suggest that although the adoption of electrochemistry is still in its early stages, it is viewed as a promising area that could lead to novel, better, and differentiated chemical transformations and disruptive routes. None of the surveyed companies reported having commercialized electrochemical processes; however, many anticipate reaching late-stage development or commercialization within a few years. The survey provides valuable insights for both industrial and academic laboratories seeking to pursue research in this field. Addressing gaps in knowledge and technology is essential to realizing the potential benefits of electrochemistry, ultimately contributing to the development of more efficient and sustainable manufacturing processes in the pharmaceutical industry.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".