Program Information
Magnetic Nanoparticle Quantitation: Compensating for Relaxation Effects
J Weaver1,2*, X Zhang2, S Toraya-Brown2, Daniel Reeves2, Irina Perreard2, S Fiering2, (1) Dartmouth-Hitchcock Medical Center, Lebanon, NH, (2) Dartmouth College, Hanover, NH,
TU-G-217A-7 Tuesday 4:30:00 PM - 6:00:00 PM Room: 217APurpose:
Our hypothesis was that the weight of magnetic nanoparticles could be accurately estimated from the magnetic spectroscopy of nanoparticle Brownian motion (MSB) signal. Quantification is critical to bio-sensing and histology and is becoming more important in medical imagining. A nanoparticle assay is important in histology and bio-sensing where antibody targeted magnetic nanoparticles are used to mark specific protein expression. Quantitative imaging of magnetic nanoparticles is also important in applications including hyperthermia and nanoparticle drug delivery.
Two factors make quantitative estimates very difficult to achieve: relaxation effects that change the signal produced by each nanoparticle and non-nanoparticle iron that confuses mass spectroscopy measurements. We introduce a method only sensitive to nanoparticle iron that compensates for relaxation effects that achieves quantitative estimates of the number of magnetic nanoparticles in a sample.
Methods:
Samples with varying quantities of iron oxide nanoparticles (100 nm mean hydrodynamic diameter) and varying quantities of glycerol were prepared. The samples contained from 1.46 mg to 0.05 mg nanoparticles and from 0% to 27% glycerol. MSB signals were recorded for each sample. The relaxation time was calculated using previously reported methods. The MSB signals were then shifted in frequency to compensate for the change in relaxation time. The scaling of the normalized MSB signal that approximates the reference sample is the weight of nanoparticles present in the sample. The apparatus used was built for 1.5 mL liquid samples; a system for smaller samples would be more sensitive.
Results:
The RMS percentage error in the weight of nanoparticles was 4.9%. The RMS error in the weight of nanoparticles was 0.018 mg iron. Other sources of iron such as blood did not bias the estimates.
Conclusions:
The method presented makes accurate MSB estimates of the weight of nanoparticles.
Acknowledgement: NIH-NCI 1U54CA151662-01
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