Molecular Simulation Methods of Evaporating Electrosprayed Droplets
Bibliographic record
Abstract
Molecular simulations provide considerable insight in elucidating the relation between the release of a macromolecule from charged droplets and its charge state, and in determining possible location of the charge in macroions detected by mass spectrometry. However, there is a number of significant challenges to consider in the modeling. These challenges include the effect of the droplet-size dependent chemistry in the charge state of a macroion, limitations in force fields, performance of efficient droplet evaporation at any temperature, and efficient treatment of the electrostatic interactions. Here, we present a robust methodology for molecular simulations that allows for the study of the chemistry and interactions of macromolecules within a droplet, and the relation of the dynamics of the process of interest to the solvent evaporation rate. The competition of these dynamical processes will determine the mass spectrum. We discuss the simulation setup and the treatment of the electrostatic interactions. Multilevel summation method (MSM) has been developed [Hardy et al. ``Multilevel summation method for electrostatic force evaluation'' J. Chem. Theory Comput. {\bf 11}, 766 (2015)] for the efficient treatment of electrostatics in non-periodic and semi-periodic systems, charged and neutral. We present comparison of MSM with particle-mesh Ewald (PME) method in order to show MSM's ability to study conformational changes of macromolecules in droplets. We demonstrate the capability of spherical boundary condition and MSM in the studies of physical and chemical process in droplets by using the example of the Rayleigh jet formation and charge emission from it.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".