Electrostatic Fields in Biophysical Chemistry
Bibliographic record
Abstract
Typical household appliances produce electric fields of roughly 10−10–10−8 V Å−1 and those from cooler climates who use electric blankets to keep warm are exposed to fields of about 10−7 V Å−1. Given these strengths of everyday exposures, it may be surprising that the molecules and organelles of life, such of enzymes and mitochondria, operate in environments that have static electric fields in the range 10−2–10−1 V Å−1. Moreover, those fields are vital for various chemical reactions and processes. Such high fields within our own bodies are possible due to strong localization, while various cancellation effects attenuate or completely nullify their manifestation(s) at a macroscopic level. From the point of view of applications, being able to control localized strong fields would allow for an unprecedented accurate promotion or/and inhibition of various chemical processes. These strong microscopic (static) electric fields are the focus of this chapter. One of the central concepts is the Stark effect, the splitting of spectral lines upon application of (strong) electric fields. This will be discussed by adopting a ground-up approach, that is, starting with the effects of imposed fields on the chemical bonds in simple diatomic molecules which are exploited to interrogate local electric field in large enzymatic active sites, building up to the effects of imposed fields on complex systems including enzyme catalysis and double proton transfers in systems such as nucleic acid base pairs. We conclude with some possible future research directions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".