| Literature DB >> 26024419 |
Gungun Lin1,2, Denys Makarov3, Oliver G Schmidt4,5.
Abstract
We report a magnetofluidic device with integrated strong ferromagnetically-coupled and hysteresis-free spin valve sensors for dynamic monitoring of ferrofluid droplets in microfluidics. The strong ferromagnetic coupling between the free layer and the pinned layer of spin valve sensors is achieved by reducing the spacer thickness, while the hysteresis of the free layer is eliminated by the interplay between shape anisotropy and the strength of coupling. The increased ferromagnetic coupling field up to the remarkable 70 Oe, which is five-times larger than conventional solutions, brings key advantages for dynamic sensing, e.g., a larger biasing field giving rise to larger detection signals, facilitating the operation of devices without saturation of the sensors. Studies on the fundamental effects of an external magnetic field on the evolution of the shape of droplets, as enabled by the non-visual monitoring capability of the device, provides crucial information for future development of a magnetofluidic device for multiplexed assays.Entities:
Keywords: droplet microfluidics; ferrofluid; ferromagnetic coupling; high field sensing; spin valve
Mesh:
Year: 2015 PMID: 26024419 PMCID: PMC4507669 DOI: 10.3390/s150612526
Source DB: PubMed Journal: Sensors (Basel) ISSN: 1424-8220 Impact factor: 3.576
Figure 1(a) Schematic representation of the layer stack of spin valve sensors (left) and a sensor stripe on a silicon substrate (middle), as well as the photograph of a spin valve sensor patterned with a size of 1 × 16 mm2 (right); (b) Schematics of two anisotropy configurations of spin valve sensors, which are achieved by setting the direction of exchange bias with respect to shape anisotropy. For Configuration A (crossed anisotropy), the exchange bias is along the short axis of the sensor stripe, and hence, it is perpendicular to the direction of the shape anisotropy. For Configuration B (parallel anisotropy), it is along the long axis of the sensor stripe parallel to the direction of the shape anisotropy; (c) Giant magnetoresistive (GMR) curves for the spin valve sensors prepared with crossed and parallel anisotropy configurations. The spin valve sensors are of the same layer stack structure as shown in (a) and the thickness of Cu ranges from 1.8 nm to 3.0 nm. Inset: The GMR response of the free layer. The x- and y-axis correspond to those in the main figure.
Figure 2(a) Ferromagnetic coupling field as a function of the spacer thickness for spin valve sensors with the parallel anisotropy configuration, as well as the fit of the data to the Néel model. Lines connecting the symbols are a guide to the eyes; (b) GMR ratio (b-1), as well as the coercivity of the free layer (b-2) as a function of the spacer thickness for spin valve sensors of parallel and crossed anisotropy configurations.
Figure 3(a) Photograph of an assembled microfluidic device with an integrated on-chip T-junction (a1) and spin valve sensors (a2). (a1) and (a2) are micrographs corresponding to the dashed squares indicated on the photograph of the device; (b) GMR response of the free layer of an integrated spin valve sensor with a spacer thickness of 1.8 nm with a crossed anisotropy configuration (top). The sensitivity is plotted as a function of the magnetic field (bottom).
Figure 4(a) Real-time detection of emulsion droplets with different lengths (130 µm and 380 µm), but the same concentration (7.5 mg/mL) of loaded ferrofluids; (b) The detection signal amplitude (square) and peak width (sphere) as a function of droplet length (with volume ranging from 200 nL to a few nanoliters). Lines are a guide to the eye; (c) The droplet width as a function of the droplet length for different concentrations of encapsulated ferrofluids. Lines are a guide to the eye. The inset depicts the schematics to measure the width and length of droplets; (d) The signal amplitude as a function of the droplet width for different concentrations of encapsulated ferrofluids. Lines are linear fittings to the data. Error bars are standard deviations of measured data.