Harnessing electrostatic catalysis in single molecule, electrochemical and chemical systems: a rapidly growing experimental tool box
Bibliographic record
Abstract
Static electricity is central to many day-to-day practical technologies, from separation methods in the recycling of plastics to transfer inks in photocopying, but the exploration of how electrostatics affects chemical bonding is still in its infancy. As shown in the Companion Tutorial, the presence of an appropriately-oriented electric field can enhance the resonance stabilization of transition states by lowering the energy of ionic contributors, and the effect that follows on reaction barriers can be dramatic. However, the electrostatic effects are strongly directional and harnessing them in practical experiments has proven elusive until recently. This tutorial outlines some of the experimental platforms through which we have sought to translate abstract theoretical concepts of electrostatic catalysis into practical chemical technologies. We move step-wise from the nano to the macro, using recent examples drawn from single-molecule STM experiments, surface chemistry and pH-switches in solution chemistry. The experiments discussed in the tutorial will educate the reader in some of the viable solutions to gain control of the orientation of reagents in that field; from pH-switchable bond-dissociations using charged functional groups to the use of surface chemistry and surface-probe techniques. All of these recent works provide proof-of-concept of electrostatic catalysis for specific sets of chemical reactions. They overturn the long-held assumption that static electricity can only affect rates and equilibrium position of redox reactions, but most importantly, they provide glimpses of the wide-ranging potential of external electric fields for controlling chemical reactivity and selectivity.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".