Machine-learning Methods to Predict Wetting Properties of Iron-Based Composites (Record no. 52843)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02104nam a2200229Ia 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | MX-MdCICY |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250625162417.0 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | CICY |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) | |
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) | B-18697 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250602s9999 xx |||||s2 |||| ||und|d |
| 245 10 - TITLE STATEMENT | |
| Title | Machine-learning Methods to Predict Wetting Properties of Iron-Based Composites |
| 490 0# - SERIES STATEMENT | |
| Volume/sequential designation | Surface Innovations, p.10.1680/jsuin.20.00024, 2020 |
| 520 3# - SUMMARY, ETC. | |
| Summary, etc. | We used three different methods of statistical data analysis to establish correlations between the water contact angle (CA)on ductile iron as a function of composition, roughness (grit size), elapsed time between sample preparation and CA measurement, and droplet size. The three methods are the Linear Regression Analysis (LRA), Artificial Neural Network (ANN)model, and the multivariate Polynomial Regression Analysis. It is established that the size of the water droplet is statistically insignificant, while correlations with the other three parameters were found. The surface roughness is the most important predictor of the CA. A low coefficient of determination of the linear regression indicates that the correlation is non-linear. The ANN model showed much stronger predictive potential than the LRA. We discuss the correlation with experimental values of the contact angle and physical mechanisms behind the observed trends. It is particularly promising that the ANN can be trained to predict the wetting characteristics. The application of machine learning methods to synthesize new materials and coatings with desired surface properties, such as self-cleaning, is a technology, which may become a part of the emergent "Triboinformatics" field, related to the application of the machine learning methods to surface science and engineering. |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Kordijazi, A. |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Roshan, H.M. |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Dhingra, A. |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Povolo, M. |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Rohatgi, P.K. |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Nosonovsky, M. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://drive.google.com/file/d/1PaMAxt3k90ccVgPSu9A_H1Df-Y3N1ifW/view?usp=drivesdk">https://drive.google.com/file/d/1PaMAxt3k90ccVgPSu9A_H1Df-Y3N1ifW/view?usp=drivesdk</a> |
| Public note | Para ver el documento ingresa a Google con tu cuenta: @cicy.edu.mx |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Clasificación local |
| Koha item type | Documentos solicitados |
| Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection | Home library | Current library | Shelving location | Date acquired | Total checkouts | Full call number | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Clasificación local | Ref1 | CICY | CICY | Documento préstamo interbibliotecario | 25.06.2025 | B-18697 | 25.06.2025 | 25.06.2025 | Documentos solicitados |