Abiotic ribonucleoside formation in aqueous microdroplets: mechanistic exploration, acidity, and electric field effects
Bibliographic record
Abstract
Aqueous microdroplets have been reported to dramatically increase the rate of chemical reactions. Proposed mechanisms for this acceleration include confinement effects upon droplet evaporation, and Brønsted acid or electric field catalysis at the air-water interface. However, computational investigations indicate that the operation of these mechanisms is reaction-dependent, with conclusive evidence for a role for electric field catalysis still lacking. Here, we present a computational investigation of the reported abiotic phosphorylation of ribose and the subsequent formation of ribonucleosides, focusing on acidity and oriented external electric field (OEEF) effects. The most plausible reaction mechanism identified involves the protonation of ribose, followed by carbocation formation and an S$_N$2 substitution step. Without an OEEF, all considered pathways are thermally inaccessible. However, in the presence of a significant OEEF, the S$_N$2-based pathway, leading to the $\beta$-ribonucleoside isomer, becomes highly stabilized, reducing the energetic span to a thermally accessible 12-13 kcal/mol. Surprisingly, the OEEF-mediated reaction closely mirrors the enzymatic mechanism of phosphorolysis via S$_N$2 substitution, including a pronounced anomeric selectivity. Our results support the hypothesis that some reactions in aqueous microdroplets are accelerated by electric fields and provide further evidence for the importance of electrostatic catalysis in biological systems, particularly for phosphorylase enzymes.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".