Data for the paper "Dispersion of particles in a sessile droplet evaporating on a heated substrate"
Bibliographic record
Abstract
This file contains the data generated for each case demonstrated in the paper titled "Dispersion of particles in a sessile droplet evaporating on a heated substrate" Authors: Aman Kumar Jain, Fabian Denner, and Berend van Wachem. The repository contains the 7 folders for 7 cases performed in stage 2 of the simulation described in table 4 of the paper. Cases C1 and C2 involve a droplet on a substrate at Ts = 25 ◦ C, while C3 and C4 involve a substrate at Ts = 50 ◦C. Marangoni stresses are considered in the even-numbered cases and neglected in the odd-numbered ones. The case names ending with suffix S denotes the standard silica particles and the case names ending with suffix N denotes neutrally buoyant articles. Along with these folders a python script "ParticleCombined.py" is added which uses the particle position data in each case folder to calculate the particle surface density. Each case folder contains: The fluid fields, mesh, and particle information are stored in folders Fields, DMs, Meshes and Particles. A .xmf wrapper file is provided to read the simulation results in Paraview. The "results.xmf" file shows the fluid data such as velocity, pressure and liquid volume fraction. The liquid volume fraction value, alpha, tracks the interface of an evaporating sessile droplet. The "results_DEM.xmf" shows the particle data such as the position, velocity and other data sets associated with the particles. Each case folder contains five *.csv files which contain the information of particle position for 5-time instances and are processed using the python script "ParticleCombined.py". This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), grant number 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.281 | 0.125 |
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".