Length Separation of Fibers

Baron, Paul A.; Deye, Gregory J.; Fernback, Joseph E.

doi:10.1080/02786829408959707

Cited by 35 publications

(33 citation statements)

References 13 publications

(15 reference statements)

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“…Baron and coworkers developed such a classifier (schematic in Fig. 5) and were able to obtain length distributions with standard deviations of about 10-20% 81,82) . This classifier separated fibers by placing them in a gradient electric field, which polarized the fibers, aligned them parallel to the electric field, and caused them to drift toward the higher field electrode.…”

Section: Fiber Separation By Lengthmentioning

confidence: 99%

Measurement of Airborne Fibers: A Review.

Baron

2001

INDUSTRIAL HEALTH

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Current fiber measurement techniques arose primarily due to health concerns over asbestos exposure. Fiber toxicity appears to be primarily a function of fiber concentration, dimensions and durability in the lungs. There are two basic approaches to fiber measurement. Fibers can be collected on filters and counted or analyzed by light or electron microscopy; alternatively, fibers can be detected directly using a combination of fiber alignment and light scattering techniques. All of these measurement approaches work best when the fibers are simple rod-shaped particles. However, most fibers can exist as curved rods, complex bundles of fibrils, and agglomerates of fibers and compact particles. These non-ideal shapes contribute to measurement bias and variability.

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Section: Fiber Separation By Lengthmentioning

confidence: 99%

Measurement of Airborne Fibers: A Review.

Baron

2001

INDUSTRIAL HEALTH

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“…Based on these predictions, Baron et al (1994) constructed a dielectrophoresis classier that demonstrated ber length separation. This design had a similar con guration to the Electrical Aerosol Analyzer (Model 3030, TSI, Inc., St. Paul, MN), though with smaller diameter electrodes, and was limited to collection of size-classi ed samples on an oiled electrode, while bers shorter than a selected length could be retained in the aerosol phase.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of a Fiber Length Classifier

Deye¹,

Gao²,

Baron³

et al. 1999

Aerosol Science and Technology

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ABSTRACT. A performance evaluation was conducted on a differential mobility classi er that separates bers according to length using dielectrophoresis. The classi er had been constructed and used for several applications in previous studies. The performance of the classi er was predicted using a two-dimensional axisymmetric model of the ow eld and then calculating particle trajectories for a variety of conditions. Based on the ow calculations, several regions of the classi er were improved to reduce likelihood of turbulent losses. For a given total ow through the classi er and a maximum voltage across the electrodes, the performance of the classi er was found to depend on the ratios of the aerosol ow to the inner and the outer sheath ows. It was found that the minimum classi able length, the minimum length distribution width, and the throughput of classi ed bers can each be optimized, but not independently. Several approaches to testing the resolution of the classi er were tried. The rst was to measure the length distribution of bers passing through the classi er under different conditions using electron microscopy. However, this was a slow and imprecise measure of performance. Two approaches using monodisperse latex spheres were used; one operated the instrument as an electrical mobility (electrophoresis) analyzer and the other evaluated only the ow system accuracy. All measures indicate that the classi er operates close to theoretical performance, but improvements are still possible. Suggested improvements require redesign of the ow system and improved electrode alignment.

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“…The health effects of glass fibers remain to be fully investigated. Recently, a classifier has been developed to separate fibers by length using dielectrophoresis that involves the movement of neutral particles in a gradient electric field (1,2). The development of this classifier makes it possible to study the role of fiber length in toxicity.…”

mentioning

confidence: 99%

Activation of Mitogen-activated Protein Kinase p38 and Extracellular Signal-regulated Kinase Is Involved in Glass Fiber-induced Tumor Necrosis Factor-α Production in Macrophages

Zeidler

Young

et al. 2001

Journal of Biological Chemistry

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In a previous study, we demonstrated that the length of glass fibers was a critical determinant of fiber potency in induction of tumor necrosis factor (TNF)-␣ and that activation of NF-B was an important factor in this response. In the present study, we analyzed the role of mitogen-activated protein (MAP) kinases in the induction of TNF-␣ by glass fibers. Glass fibers induced phosphorylation of MAP kinases, p38, and ERK in primary rat alveolar macrophages, and this phosphorylation was associated with TNF-␣ gene expression. Long fibers were more potent than short fibers in activation of MAP kinases. Results from mechanistic analysis support that MAP kinases activate transcription factor c-Jun. The activated c-Jun acts on the TNF-␣ gene promoter through two binding sites, the cyclic AMP response element and the activator protein 1-binding site. These results suggest that in addition to the NF-B pathway for TNF-␣ production, glass fibers are able to activate c-Jun through MAP kinase pathways that lead to induction of TNF-␣ expression.

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Length Separation of Fibers

Cited by 35 publications

References 13 publications

Measurement of Airborne Fibers: A Review.

Measurement of Airborne Fibers: A Review.

Performance Evaluation of a Fiber Length Classifier

Activation of Mitogen-activated Protein Kinase p38 and Extracellular Signal-regulated Kinase Is Involved in Glass Fiber-induced Tumor Necrosis Factor-α Production in Macrophages

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