This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
Research Article
Sahar Andalib
University of California, Los Angeles
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Droplet evaporation plays crucial roles in biodiagnostics, microfabrication, and inkjet printing. Experimentally studying the evolution of a sessile droplet consisting of two or more components needs sophisticated equipment to control the vast parameter space affecting the physical process. On the other hand, non-axisymmetric nature of the problem, attributed to compositional perturbations, introduces challenges to numerical methods. In this work, droplet evaporation problem is studied from a new perspective. We analyze evolution of a sessile methanol droplet through data-driven classification and regression techniques. The models are trained using experimental data of methanol droplet evolution under various environmental humidity levels and substrate temperatures. At higher humidity levels, the interfacial tension and subsequently contact angle increase due to higher water uptake into droplet. Therefore, different regimes of evolution are observed due to adsorption-absorption and possibly condensation of water which turns the droplet into a binary system. We use classification algorithms to predict the regime of droplet with point-by-point analysis of droplet profile. Decision tree demonstrates a better performance compared to Na\text{\"i}ve Bayes (NB) classifier. Furthermore, through utilizing regression techniques, we predict the humidity level surrounding droplet as well as time evolution of macroscopic parameter (diameter or contact angle) of droplet. The prediction results show promising performance for four cases of methanol droplet evolution under conditions that are unseen by the model which demonstrates the capability of the model to capture the complex physics underlying binary droplet evolution.
Keywords
Sessile Droplets, Data-driven Classification, Regression Techniques, Decision Tree Algorithms, Binary Droplet Evolution
Figure 1
Figure 2
Figure 3
Figure 4
No competing interests reported.
Loading...
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 23 Feb, 2021
No community comments so far
Editorial decision: Major revision
On 05 Apr, 2021
Reviews received
Received 20 Mar, 2021
Reviewers agreed
On 10 Mar, 2021
Reviewers agreed
On 08 Mar, 2021
Reviewers invited
Invitations sent on 05 Mar, 2021
Editor assigned
On 05 Mar, 2021
Editor invited
On 24 Feb, 2021
Submission checks complete
On 24 Feb, 2021
First submitted
On 20 Feb, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Learn more about our company.
A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Sahar Andalib
University of California, Los Angeles
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 23 Feb, 2021
No community comments so far
Editorial decision: Major revision
On 05 Apr, 2021
Reviews received
Received 20 Mar, 2021
Reviewers agreed
On 10 Mar, 2021
Reviewers agreed
On 08 Mar, 2021
Reviewers invited
Invitations sent on 05 Mar, 2021
Editor assigned
On 05 Mar, 2021
Editor invited
On 24 Feb, 2021
Submission checks complete
On 24 Feb, 2021
First submitted
On 20 Feb, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Droplet evaporation plays crucial roles in biodiagnostics, microfabrication, and inkjet printing. Experimentally studying the evolution of a sessile droplet consisting of two or more components needs sophisticated equipment to control the vast parameter space affecting the physical process. On the other hand, non-axisymmetric nature of the problem, attributed to compositional perturbations, introduces challenges to numerical methods. In this work, droplet evaporation problem is studied from a new perspective. We analyze evolution of a sessile methanol droplet through data-driven classification and regression techniques. The models are trained using experimental data of methanol droplet evolution under various environmental humidity levels and substrate temperatures. At higher humidity levels, the interfacial tension and subsequently contact angle increase due to higher water uptake into droplet. Therefore, different regimes of evolution are observed due to adsorption-absorption and possibly condensation of water which turns the droplet into a binary system. We use classification algorithms to predict the regime of droplet with point-by-point analysis of droplet profile. Decision tree demonstrates a better performance compared to Na\text{\"i}ve Bayes (NB) classifier. Furthermore, through utilizing regression techniques, we predict the humidity level surrounding droplet as well as time evolution of macroscopic parameter (diameter or contact angle) of droplet. The prediction results show promising performance for four cases of methanol droplet evolution under conditions that are unseen by the model which demonstrates the capability of the model to capture the complex physics underlying binary droplet evolution.
Figure 1
Figure 2
Figure 3
Figure 4
No competing interests reported.
Loading...