Towards the taxonomic categorization and recognition of nanoparticle shapes

Muñoz-Mármol, Miguel; Crespo, José; Fritts, M. J.; Maojo, Víctor

doi:10.1016/j.nano.2014.07.006

Cited by 13 publications

(11 citation statements)

References 33 publications

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“…Mathematical morphology methods are suggested for this task. This approach has been used in processing of nanomaterials in the studies by Pré 17 and Munoz-Mármol et al 18…”

Section: Methods For Nanostructured Materials Characterizationmentioning

confidence: 99%

“…Mathematical morphology methods are suggested for this task. This approach has been used in processing of nanomaterials in the studies by Pré 17 and Munoz-Mármol et al 18 Morphological transform C(X) is given by a relation between the image I and the set of points B which is called an SE. The SE usually includes representative point P. Selected examples of simple SEs are presented in Figure 7.…”

Section: Post-processingmentioning

confidence: 99%

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Automatic characterization of titanium dioxide nanotubes by image processing of scanning electron microscopic images

Slavikova

Mudrová

Petrová

et al. 2016

Nanomaterials and Nanotechnology

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The presented article deals with the assessment of titanium dioxide nanotube microscopic images by means of imageprocessing methods. Inner diameter, wall thickness, and fraction of intertubular space are among the basic parameters characterizing the quality of nanostructured material. These parameters are especially important during the process of nanomaterial development. Nanostructures were prepared by electrochemical oxidation on the surface of Ti-6Al-4V alloy. Results of this process are greatly influenced by the chosen experimental conditions, so objective evaluation of experimental results is very important for the optimal setting of experimental parameters. Image-processing methods could be successfully applied to microscopic images to obtain the aforementioned objective characterization criteria. Various methods, such as object classification, image filtering, and mathematical morphology, could be taken into consideration with respect to a specific type of image data. In addition, the algorithm proposed uses the advantages of adaptive thresholding, watershed transform, low-pass filtering, mathematical morphology and cluster analysis. The nanotube wall thickness evaluation is suggested by two different approaches for the characterization of variable quality of the images processed.

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“…Mathematical morphology methods are suggested for this task. This approach has been used in processing of nanomaterials in the studies by Pré 17 and Munoz-Mármol et al 18…”

Section: Methods For Nanostructured Materials Characterizationmentioning

confidence: 99%

Section: Post-processingmentioning

confidence: 99%

Automatic characterization of titanium dioxide nanotubes by image processing of scanning electron microscopic images

Slavikova

Mudrová

Petrová

et al. 2016

Nanomaterials and Nanotechnology

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“…It is an impediment to progress and uptake by new users when LNP materials are nominally the same but have empirically different behaviors. Again, this is a longstanding challenge with longstanding calls to action regarding classification and characterization, − but the magnitude and importance of overcoming this challenge is still underappreciated by many. When appreciated, it still remains difficult to address in full.…”

Section: Perspectivementioning

confidence: 99%

Photoluminescent Nanoparticles for Chemical and Biological Analysis and Imaging

et al. 2021

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Research related to the development and application of luminescent nanoparticles (LNPs) for chemical and biological analysis and imaging is flourishing. Novel materials and new applications continue to be reported after two decades of research. This review provides a comprehensive and heuristic overview of this field. It is targeted to both newcomers and experts who are interested in a critical assessment of LNP materials, their properties, strengths and weaknesses, and prospective applications. Numerous LNP materials are cataloged by fundamental descriptions of their chemical identities and physical morphology, quantitative photoluminescence (PL) properties, PL mechanisms, and surface chemistry. These materials include various semiconductor quantum dots, carbon nanotubes, graphene derivatives, carbon dots, nanodiamonds, luminescent metal nanoclusters, lanthanidedoped upconversion nanoparticles and downshifting nanoparticles, triplet−triplet annihilation nanoparticles, persistent-luminescence nanoparticles, conjugated polymer nanoparticles and semiconducting polymer dots, multi-nanoparticle assemblies, and doped and labeled nanoparticles, including but not limited to those based on polymers and silica. As an exercise in the critical assessment of LNP properties, these materials are ranked by several application-related functional criteria. Additional sections highlight recent examples of advances in chemical and biological analysis, point-of-care diagnostics, and cellular, tissue, and in vivo imaging and theranostics. These examples are drawn from the recent literature and organized by both LNP material and the particular properties that are leveraged to an advantage. Finally, a perspective on what comes next for the field is offered.

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“…Shape type. Shape, as a substantial parameter affecting the behavior of nanoparticles, [27][28][29][30][31][32] has been poorly taken into account in nanomaterial risk assessment studies suffering from the lack of a method to translate the shape type into numerical values. In the present contribution, we successfully dened shape descriptors that could distinguish between different shape types.…”

Section: Nano-descriptorsmentioning

confidence: 99%