Separation of bacteria, protozoa and carbon nanotubes by density gradient centrifugation

Published

October 12, 2016

Author(s)

Monika Mortimer, Elijah Petersen, Bruce A. Buchholz, Patricia Holden

Abstract

Sustainable production and use of carbon nanotube (CNT)-enabled materials requires efficient assessment of CNT environmental hazards, including their potential for bioaccumulation and biomagnification in environmental receptors. Microbes as abundant organisms responsible for nutrient cycling in soil and water are important ecological receptors for studying the effects of CNTs. Quantification of CNT association with microbial cells requires efficient separation of CNT-associated cells from individually dispersed CNTs and CNT agglomerates. Here, we demonstrate optimized procedures for separation of bacteria (Pseudomonas aeruginosa) from unbound multiwall carbon nanotubes (MWCNT) and MWCNT agglomerates using sucrose density gradient. We demonstrate separation of protozoa (Tetrahymena thermophila) from MWCNTs, bacterial agglomerates, and protozoan fecal pellets by centrifugation in iodixanol solution. The location of free MWCNTs in the density gradients after centrifugation was determined by quantification of 14C-labeled MWCNTs; the localization and purity of microbes was determined by optical microscopy. The separation and concentration of live microbes was achieved without affecting their viability; protozoan intracellular content of MWCNTs and bacteria was also unaffected. The optimized methods contribute to improved efficiency and accuracy in quantification of MWCNT association with bacteria and accumulation in protozoan cells, thus supporting improved assessment of CNT bioaccumulation.

Citation

International journal of nanomedicine

Pub Type

Journals

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Citation

Mortimer, M. , Petersen, E. , Buchholz, B. and Holden, P. (2016), Separation of bacteria, protozoa and carbon nanotubes by density gradient centrifugation, International journal of nanomedicine, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=920676 (Accessed December 13, 2024)