Biased Cyclical Electrical Field Flow Fractionation for Separation of Sub 50 nm Particles

Abstract

Cyclical electrical field flow fractionation (CyElFFF) is a technique for characterizing and separating nanoparticles based on their size and charge using cyclical electric fields. The high diffusion rate of nanoparticles has prevented CyElFFF from being applicable to particles smaller than 100 nm. In this work, the diffusion challenges associated with nanoparticles was resolved using biased cyclical electric fields. This new method, biased cyclical electrical field flow fractionation (BCyElFFF), achieves baseline separation of 15 and 40 nm gold nanoparticles. Theoretical considerations show that the optimal resolution is achieved when the applied bias yields electrical transport that counteracts the diffusive transport of nanoparticles. BCyElFFF greatly extends separation capabilities of the cyclical electrical field flow fractionation to sub 50 nm nanoparticles and provides a powerful alternative to other separation and characterization techniques capable of separating nanoparticles smaller than 50 nm.

Biased cyclical electrical field-flow fractionation for separation of submicron particles

“…The CyElFFF channel used for these experiments is identical to that used in previous works [4,7,13] and consists of parallel rectangular graphite plates separated by a Mylar spacer. The channel is 64 cm long, 2 cm wide, and 178 μm thick.…”

“…The notation dc* was used in previous publications to represent the % dc that was required to push the particles back to wall after every cycle ( l e = l dx ) [13]. This case would ideally separate particles by only their charge or electrophoretic mobility and should give the best resolution as shown in Fig.…”

“…Depending on the frequency and magnitude of the applied field, the required dc* for given conditions will change. [13]. When dc > dc* , particles will reach the wall before the cycle is complete and begin to operate as if they were under a normal, DC electric field (i.e.…”

“…$$R_s=\frac{t_2-t_1}{2false({\sigma }_1+{\sigma }_{2}false)}$$ where t 1 and t 2 are the positions of the peaks and σ 1 and σ 2 are the standard deviations of the peaks as they are approximated by a Gaussian curve [13]. …”

“…Particles tend to drift away from wall and gradually move into the center of the channel during each voltage cycle resulting in peak broadening and poor resolution in the fractogram. Recently, Tasci et al [13] suggested that using biased cyclical electric fields, which applies the positive cycle voltage longer than the negative cycle voltage, can help solve this diffusion problem. They were able to separate nanoparticles smaller than 50 nm.…”