Characterization of colloidal nanoparticles in mixtures with polydisperse and multimodal size distributions using a particle tracking analysis and electrospray-scanning mobility particle sizer

Lee, Handol; Kwak, Dong Bin; Kim, Seong Chan; Pui, David Y.H.

doi:10.1016/j.powtec.2019.07.029

Cited by 12 publications

(5 citation statements)

References 45 publications

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“…To carefully characterize the size distribution of mixtures containing nanoparticles of very different sizes like in our study other techniques such as particle tracking analysis and electrospray-scanning mobility particle sizer should be used. 55 However, such characterization was beyond the scope of this preliminary study as the main aim was to evaluate ER activity of PNPs given individually or in mixtures.…”

Section: Physico-chemical Characteristics and Stability Of Plastic Na...mentioning

confidence: 99%

Estrogenic activity of plastic nanoparticle mixtures under in vitro settings

Božičević,

Altmann,

Hildebrandt

et al. 2024

Environ. Sci.: Nano

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Section: Physico-chemical Characteristics and Stability Of Plastic Na...mentioning

confidence: 99%

Estrogenic activity of plastic nanoparticle mixtures under in vitro settings

Božičević,

Altmann,

Hildebrandt

et al. 2024

Environ. Sci.: Nano

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“…Another disadvantage is based on the modifications that could appear in the sample. Such techniques require a careful preparation before analysis, so a possible aggregation appeared in the entire population could be disturbed during the preparation steps [13].…”

Section: Direct Particle Tracking -Theoretical Considerationsmentioning

confidence: 99%

Nanoparticles Size Distribution Assessment During Early Synthesis Stages

Chicea

Codescu²,

Nicolae

et al. 2021

MATEC Web Conf.

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Nanotechnology, an innovative field of material science, designs and produces nanostructures materials with unique properties that make them suited for various bioengineering applications. The shape and the dimension are the main variables depending on the method of synthesis or chemical precursors. Silver nanoparticles obtained through chemical synthesis exhibit as strong antibacterial effect if their size lays in a well-defined range. However, a small size of such particles, in the range of nanometers to several tens of nanometers, requires specific techniques as Dynamic Light Scattering or Transmission Electron Microscopy, both being affected by certain assumptions or by the high cost and long time required for a measurement. A nonconventional method based on Direct Particle Tracking can be used to produce the particle size distribution in the early nanoparticle synthesis stages. The work presents a realistic computer simulation of nanoparticle diffusion performed with the CHODIN code and a size distribution assessment using a code for Direct Particle Tracking written for this purpose. The results are consistent with the particle size used for simulation and states as a proof of concept for using the Direct Particle Tracking method for particle size distribution in the early stages of nanoparticle synthesis.

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“…Small angle X-ray scattering (SAXS) is another method that uses scattered electromagnetic radiation to estimate the radius of gyration of NPs, but NP sizes measured by this technique are limited to about 70 nm in diameter . Many other methods of determining the size of NPs also exist, − each of them with their limitations, such as price and time requirements. UV–vis E (λ) spectra also can be used to characterize the size distribution of liquid-suspended NPs but are not straightforward to interpret …”

Section: Introductionmentioning

confidence: 99%

Deep Learning Methods for Colloidal Silver Nanoparticle Concentration and Size Distribution Determination from UV–Vis Extinction Spectra

Klinavičius,

Khinevich,

Tamulevičienė

et al. 2024

J. Phys. Chem. C

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Electron microscopy, while reliable, is an expensive, slow, and inefficient technique for thorough size distribution characterization of both monodisperse and polydisperse colloidal nanoparticles. If rapid in situ characterization of colloid samples is to be achieved, a different approach, based on fast, widely accessible, and inexpensive optical measurements such as UV−vis spectroscopy in combination with spectral interpretation related to Mie scattering theory, is needed. In this article, we present a tandem deep neural network (DNN) for the size distribution and concentration prediction of close to spherical silver colloidal nanoparticle batches synthesized via the seeded-growth method. The first DNN identified the dipole component of the localized surface plasmon resonance, and the second one determined the size distribution from the isolated spectral component. The training data was engineered to be bias-free and generated numerically. High prediction accuracy with root-mean-square percentage error of mean size down to 1.2% was achieved, spanning the entire prediction range from 1 up to 150 nm in radius, suggesting the possible extension limits of the effective medium theory used for simulating the spectra. The DNN-predicted nanoparticle concentrations also were very close to the ones expected based on synthesis precursor contents as well as those measured by atomic absorption spectroscopy.

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Characterization of colloidal nanoparticles in mixtures with polydisperse and multimodal size distributions using a particle tracking analysis and electrospray-scanning mobility particle sizer

Cited by 12 publications

References 45 publications

Estrogenic activity of plastic nanoparticle mixtures under in vitro settings

Estrogenic activity of plastic nanoparticle mixtures under in vitro settings

Nanoparticles Size Distribution Assessment During Early Synthesis Stages

Deep Learning Methods for Colloidal Silver Nanoparticle Concentration and Size Distribution Determination from UV–Vis Extinction Spectra

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