MARC details
| 000 -LEADER |
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| fixed length control field |
02006nam a2200289Ia 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MX-MdCICY |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20250625162501.0 |
| 040 ## - CATALOGING SOURCE |
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| Transcribing agency |
CICY |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
B-21086 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250602s9999 xx |||||s2 |||| ||und|d |
| 245 10 - TITLE STATEMENT |
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| Title |
Computer vision and machine learning for assessing dispersion quality in carbon nanotube/resin systems |
| 490 0# - SERIES STATEMENT |
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| Volume/sequential designation |
Carbon, 213, p.118230, 2023 |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
The addition of nanomaterials to polymeric resins can enhance a range of bulk material properties, but the nanofiller effectiveness varies strongly on the dispersion quality. The ability to independently, objectively, and quickly assess the dispersion quality of nano-loaded resins based on microscopy is desirable, but current techniques are often subjective and time-consuming. For this paper, we utilize a dispersion metric based on the use of image segmentation of optical microscope images. We then show that by training a computer vision model on a dataset of segmented microscopy images, the model can then quickly and accurately assess the dispersion of nanoparticles in a material. We apply this process to microscope images of carbon nanotube-loaded commercial resins. Our results indicate that this machine-learning methodology can match the accuracy and repeatability of current methods. In principle, this same machine-learning approach can be applied to a broad range of nanomaterials and matrices, allowing for rapid and quantitative analysis of microscope images for in-line quality control. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
NANOTUBE |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
DISPERSION |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
MACHINE LEARNING |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
MICROSCOPY |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
RESIN |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
COMPUTER VISION |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
ARTIFICIAL INTELLIGENCE |
| 700 12 - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Diehl, H. P. |
| 700 12 - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Sweeney, C. B. |
| 700 12 - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Tran, T. Q. |
| 700 12 - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Green, M. J. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="https://drive.google.com/open?id=14bV6tLhIktPrQDHT9jhSBN6cdwMQElKo&usp=drive_copy">https://drive.google.com/open?id=14bV6tLhIktPrQDHT9jhSBN6cdwMQElKo&usp=drive_copy</a> |
| Public note |
Para ver el documento ingresa a Google con tu cuenta: @cicy.edu.mx |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Clasificación local |
| Koha item type |
Documentos solicitados |
