Assessment of nanofibre dustiness by means of vibro-fluidization

Broßell, Dirk; Heunisch, Elisabeth; Meyer‐Plath, Asmus; Bäger, Daphne; Bachmann, V.; Kämpf, Kerstin; Dziurowitz, Nico; Thim, Carmen; Wenzlaff, Daniela; Schumann, John H.; Plitzko, Sabine

doi:10.1016/j.powtec.2018.10.013

Cited by 14 publications

(10 citation statements)

References 39 publications

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“…The estimated experimental errors are ± 3 nm for D, ± 200 nm for L and ± 1 kHz for ν 1 and ν 2 . We neglect the inner diameter D i for its contribution is very small for the MWCNTs studied here due to the 4th power dependence in the second moment of area: (D i /D) 4 1. The theoretical ratio equals ν 2 /ν 1 = β 2 2 /β 2 1 = 6.267.…”

Section: Bending Modulus Results Of This Workmentioning

confidence: 99%

“…Recently, for some CNT powder products, very high dust and individual fiber release propensities have been identified [3,4]. During CNT synthesis and handling, initial processes like product collection and reactor cleaning, as well as secondary processes like CNT powder milling and mixing, product compounding and machining may release airborne CNT or CNT-containing dust particles.…”

Section: Introductionmentioning

confidence: 99%

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Measurement of Flexural Rigidity of Multi-Walled Carbon Nanotubes by Dynamic Scanning Electron Microscopy

Fortini

Meyer‐Plath

Kehren

et al. 2020

Fibers

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In this work the flexural rigidity of individual large diameter multi-walled carbon nanotubes (MWCNTs) was investigated. The bending modulus were obtained by detecting the resonance frequencies of mechanically excited cantilevered carbon nanotubes using the so-called dynamic scanning electron microscopy technique, and applying the Euler–Bernoulli beam theory. For the nanotubes studied, we determined a modulus of up to 160 GPa. This agrees with values reported by other authors for MWCNTs produced by catalytic chemical vapor deposition, however, it is 6-8 times smaller than values reported for single and multi-walled carbon nanotubes produced by arc-discharge synthesis. Toxicological studies with carbon nanotubes have been showing that inhaled airborne nanofibers that reach the deep airways of the respiratory system may lead to serious, asbestos-like lung diseases. These studies suggested that their toxicity critically depends on the fiber flexural rigidity, with high rigidity causing cell lesions. To complement the correlation between observed toxicological effects and fiber rigidities, reliable and routinely applicable measurement techniques for the flexural rigidity of nanofibers are required.

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Section: Bending Modulus Results Of This Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measurement of Flexural Rigidity of Multi-Walled Carbon Nanotubes by Dynamic Scanning Electron Microscopy

Fortini

Meyer‐Plath

Kehren

et al. 2020

Fibers

Self Cite

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show abstract

“…[14] Most recently, it was recognized that the tendency to form fibrils upon release of dusts requires addition of fibers to the group of hazardous fibers. [15] At the same time, the regulatorily approved criterion of mechanical rigidity led to removal of other fibers from the group, because "low diameter MWCNT (< 30 nm) are not subject to the proposed classification, as it is assumed that due to a more tangled morphology, the fibre pathogenicity paradigm does not apply". [15,16] The example highlights the science-based refinement from a one-sided criterion (threshold) to a two-sided criterion with an upper and lower range of a property required for group members.…”

Section: Classical Examplesmentioning

confidence: 99%

Lessons Learned from the Grouping of Chemicals to Assess Risks to Human Health

Wohlleben

Mehling²,

Landsiedel

2023

Angew Chem Int Ed

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In analogy to the periodic system that groups elements by their similarity in structure and chemical properties, the hazard of chemicals can be assessed in groups having similar structures and similar toxicological properties. Here we review case studies of chemical grouping strategies that supported the assessment of hazard, exposure, and risk to human health. By the EU-REACH and the US-TSCA New Chemicals Program, structural similarity is commonly used as the basis for grouping, but that criterion is not always adequate and sufficient. Based on the lessons learned, we derive ten principles for grouping, including: transparency of the purpose, criteria, and boundaries of the group; adequacy of methods used to justify the group; and inclusion or exclusion of substances in the group by toxicological properties. These principles apply to initial grouping to prioritize further actions as well as to definitive grouping to generate data for risk assessment. Both can expedite effective risk management.

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“…The ground truths 3 , used to train and test the CNNs utilized within this publication, have three origins: manual annotation, semiautomatic annotation and image synthesis.…”

Section: Ground Truth Generationmentioning

confidence: 99%

“…On the other hand, they can have a severe toxicological potential, which impedes their environmental compatibility, especially when being delivered as aerosols [2]. Since both, the desired and the undesired properties, are often shape and size dependent, image-based fiber analysis is an important tool for the exploration and assessment of risks and chances of fibershaped materials [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

FibeR-CNN: Expanding Mask R-CNN to Improve Image-Based Fiber Analysis

Frei,

Kruis

2020

Preprint

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Fiber-shaped materials (e.g. carbon nano tubes) are of great relevance, due to their unique properties but also the health risk they can impose. Unfortunately, image-based analysis of fibers still involves manual annotation, which is a time-consuming and costly process.We therefore propose the use of region-based convolutional neural networks (R-CNNs) to automate this task. Mask R-CNN, the most widely used R-CNN for semantic segmentation tasks, is prone to errors when it comes to the analysis of fiber-shaped objects. Hence, a new architecture -FibeR-CNN -is introduced and validated. FibeR-CNN combines two established R-CNN architectures (Mask and Keypoint R-CNN) and adds additional network heads for the prediction of fiber widths and lengths. As a result, FibeR-CNN is able to surpass the mean average precision of Mask R-CNN by 33 % (11 percentage points) on a novel test data set of fiber images.Source code available online.

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Assessment of nanofibre dustiness by means of vibro-fluidization

Cited by 14 publications

References 39 publications

Measurement of Flexural Rigidity of Multi-Walled Carbon Nanotubes by Dynamic Scanning Electron Microscopy

Measurement of Flexural Rigidity of Multi-Walled Carbon Nanotubes by Dynamic Scanning Electron Microscopy

Lessons Learned from the Grouping of Chemicals to Assess Risks to Human Health

FibeR-CNN: Expanding Mask R-CNN to Improve Image-Based Fiber Analysis

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