Net Electron Capture in Collisions of Multiply Charged Projectiles with Biologically Relevant Molecules
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Bibliographic record
Abstract
A model for the description of proton collisions from molecules composed of atoms such as hydrogen, carbon, nitrogen, oxygen and phosphorus (H, C, N, O, P) was recently extended to treat collisions with multiply charged ions with a focus on net ionization. Here we complement the work by focusing on net capture. The ion–atom collisions are computed using the two-center basis generator method. The atomic net capture cross sections are then used to assemble two models for ion–molecule collisions: An independent atom model (IAM) based on the Bragg additivity rule (labeled IAM-AR), and also the so-called pixel-counting method (IAM-PCM) which introduces dependence on the orientation of the molecule during impact. The IAM-PCM leads to significantly reduced capture cross sections relative to IAM-AR at low energies, since it takes into account the overlap of effective atomic cross sectional areas. We compare our results with available experimental and other theoretical data focusing on water vapor (H2O), methane (CH4) and uracil (C4H4N2O2). For the water molecule target we also provide results from a classical-trajectory Monte Carlo approach that includes dynamical screening effects on projectile and target. For small molecules dominated by a many-electron atom, such as carbon in methane or oxygen in water, we find a saturation phenomenon for higher projectile charges (q=3) and low energies, where the net capture cross section for the molecule is dominated by the net cross section for the many-electron atom, and the net capture cross section is not proportional to the total number of valence electrons.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it