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Giant magnetoresistive (GMR) biosensors have emerged as powerful tools for ultrasensitive, multiplexed, real-time electrical readout, and rapid biological/chemical detection while combining with magnetic particles. Finding appropriate magnetic nanoparticles (MNPs) and its influences on the detection signal is a vital aspect to the GMR bio-sensing technology. Here, we report a GMR sensor based detection system capable of stable and convenient connection, and real-time measurement. Five different types of MNPs with sizes ranging from 10 to 100 nm were investigated for GMR biosensing. The experiments were accomplished with the aid of DNA hybridization and detection architecture on GMR sensor surface. We found that different MNPs markedly affected the final detection signal, depending on their characteristics of magnetic moment, size, and surface-based binding ability, etc. This work may provide a useful guidance in selecting or preparing MNPs to enhance the sensitivity of GMR biosensors, and eventually lead to a versatile and portable device for molecular diagnostics.

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A detection scheme for real-time Brownian relaxation of magnetic nanoparticles ͑MNPs͒ is demonstrated by a mixing-frequency method in this paper. MNPs are driven into the saturation region by a low frequency sinusoidal magnetic field. A high frequency sinusoidal magnetic field is then applied to generate mixing-frequency signals that are highly specific to the magnetization of MNPs. These highly sensitive mixing-frequency signals from MNPs are picked up by a pair of balanced built-in detection coils. The phase delays of the mixing-frequency signals behind the applied field are derived, and are experimentally verified. Commercial iron oxide MNPs with the core diameter of 35 nm are used for the measurement of Brownian relaxation. The results are fitted well with Debye model. Then a real-time measurement of the binding process between protein G and its antibody is demonstrated using MNPs as labels. This study provides a volume-based magnetic sensing scheme for the detection of binding kinetics and interaction affinities between biomolecules in real time.

Magnetite ferrofluids with unique magnetic behaviors are attractive for biomedical applications such as magnetic fluid hyperthermia and magnetic particle imaging. A precise nanoparticle-specific characterization by theoretical models and experiments to predict dynamics of ferrofluids and optimize their behaviors for emerging biomedical applications is necessary. In this paper, combining experiments and modeling, we have uncovered interesting magnetic dynamics of nanoparticles that are dependent on magnetic field strength, polymer coating of nanoparticles, viscosity of ferrofluid, and dipolar interactions. It is concluded that either by changing the magnitude of magnetic field or the concentrations of nanoparticles, we are able to convert the dominating relaxation process of magnetic nanoparticles from Néel to Brownian, and vice versa. Polymer coatings on nanoparticles and viscosity of ferrofluids are demonstrated to have varying degrees of influence on effective relaxation times of nanoparticles with different sizes and under different field strengths. Our theoretical models are used to predict the magnetic response of ferrofluid consisting of 35 nm magnetite nanoparticles under alternating magnetic fields, and it turns out that our theoretical data fits well with the experimental data.