000			02006nam a2200289Ia 4500
003			MX-MdCICY
005			20250625162501.0
040			_cCICY
090			_aB-21086
245	1	0	_aComputer vision and machine learning for assessing dispersion quality in carbon nanotube/resin systems
490	0		_vCarbon, 213, p.118230, 2023
520	3		_aThe 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	1	4	_aNANOTUBE
650	1	4	_aDISPERSION
650	1	4	_aMACHINE LEARNING
650	1	4	_aMICROSCOPY
650	1	4	_aRESIN
650	1	4	_aCOMPUTER VISION
650	1	4	_aARTIFICIAL INTELLIGENCE
700	1	2	_aDiehl, H. P.
700	1	2	_aSweeney, C. B.
700	1	2	_aTran, T. Q.
700	1	2	_aGreen, M. J.
856	4	0	_uhttps://drive.google.com/open?id=14bV6tLhIktPrQDHT9jhSBN6cdwMQElKo&usp=drive_copy _zPara ver el documento ingresa a Google con tu cuenta: @cicy.edu.mx
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