Data for the paper "Self-sorting of bidisperse particles in evaporating sessile droplets"
Bibliographic record
Abstract
Dataset Description This dataset contains simulation data corresponding to the study presented in the paper titled:"Self-sorting of bidisperse particles in evaporating sessile droplets"Authors: Aman Kumar Jain, Fabian Denner, and Berend van Wachem Dataset Structure The dataset includes six folders, each representing a distinct simulation case (C1 through C6) from Stage 2 of the simulation study, as outlined in Table 3 of the paper. Cases C1 and C2: Simulate droplets with an initial contact angle of 60°. Cases C3 and C4: Simulate droplets with an initial contact angle of 90°. Cases C5 and C6: Correspond to additional conditions as specified in the publication. In each pair of cases: Odd-numbered cases (C1, C3, C5) neglect Marangoni stresses. Even-numbered cases (C2, C4, C6) include Marangoni stresses. The suffixes in the case folder names denote particle types: “_S”: Standard silica particles “_N”: Neutrally buoyant particles Each case folder contains: Subdirectories: Fields: Fluid field data (e.g., velocity, pressure) DMs: Particle and domain-related metadata Meshes: Computational mesh files Particles: Lagrangian particle data Visualization files: results.xmf: XMF wrapper for reading fluid data (velocity, pressure, liquid volume fraction α) in ParaView results_DEM.xmf: XMF wrapper for particle data visualization (e.g., position, velocity) Particle position data: Five CSV files representing particle positions at five distinct time instances. Analysis Script A Python script named ParticleCombined.py is included. This script processes the particle position CSV files from each case folder to compute particle surface density over time. Funding Acknowledgment This research was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Grant No. 452916560.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.071 | 0.047 |
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".