Effect of External Electric Field upon Lower Alkanols
Bibliographic record
Abstract
Applying the Monte Carlo method, molecules of methanol, ethanol and 1-butanol are brought to temperature of 300 K and under the influence of external electric field of 0.01 a.u their electronic spectra are simulated with HyperChem 8.0 involving ZINDO/S semiempirical method. Particular molecules differently react to the electric field applied as shown by the electronic spectra simulated in the range of 250-2.84 nm. Total energy of the methanol and ethanol molecules turn slightly more negative in the electric field whereas that energy found for 1-butanol more significantly turn to less negative. HyperChem 8.0 software is used together with the AM1 method for optimization of the conformation of the molecules of methanol, ethanol, 1-propanol and 1-butanol. Then polarizability, charge distribution, potential and dipole moment for molecules placed in the external electric field of 0.000, 0.001, 0.01 and 0.05 a.u. are calculated. External field induces a slightly field strength dependent polarizability of the molecules and the electron density redistribution at particular atoms. Total dipole moment (DM) for particular alkanols increases with the strength of the field applied. There is particularly sharp increase in DM at 0.05 a.u. field. Key words: Butanol; Computer simulations; Ethanol; Methanol; Propanol
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".