Magnetoimpedance Biosensors and Real-Time Healthcare Monitors: Progress, Opportunities, and Challenges

Abstract

A small DC magnetic field can induce an enormous response in the impedance of a soft magnetic conductor in various forms of wire, ribbon, and thin film. Also known as the giant magnetoimpedance (GMI) effect, this phenomenon forms the basis for the development of high-performance magnetic biosensors with magnetic field sensitivity down to the picoTesla regime at room temperature. Over the past decade, some state-of-the-art prototypes have become available for trial tests due to continuous efforts to improve the sensitivity of GMI biosensors for the ultrasensitive detection of biological entities and biomagnetic field detection of human activities through the use of magnetic nanoparticles as biomarkers. In this review, we highlight recent advances in the development of GMI biosensors and review medical devices for applications in biomedical diagnostics and healthcare monitoring, including real-time monitoring of respiratory motion in COVID-19 patients at various stages. We also discuss exciting research opportunities and existing challenges that will stimulate further study into ultrasensitive magnetic biosensors and healthcare monitors based on the GMI effect.

Keywords: COVID-19 detection; healthcare monitors; magnetic biosensors; magnetoimpedance.

Figure 1

Number of published articles and citations per year in the field of magnetoimpedance materials and sensors. The data were collected from Web of Science with “magnetoimpedance” or “magneto-impedance” as a keyword.

Figure 2

The potential applications of MI sensors proposed by Aichi Micro Intelligent Corporation. Reprinted with permission from Ref. [23]. Copyright 2022 Aichi Steel Corporation.

Figure 3

Schematics of stray magnetic field detection of magnetic beads (a) without an external magnetic field. Reprinted with permission from Ref. [43]. Copyright 2022 Elsevier, and (b) with an applied external magnetic field. Reprinted with permission from Ref. [27]. Copyright 2022 AIP Publishing.

Figure 4

(a) Schematic illustration of the principle of detecting magnetic nanoparticles as magnetic labels inside cells; (b) schematic illustration of the principles of targeting and recognizing biomolecules. In Method 1, the magnetic nanoparticles are functionalized with target molecules (Molecule B), while in Method 2 the target molecules (Molecule B) are bound to the surface of the sensor.

Figure 5

(a) Fe₃O₄ particle concentration dependence on MR, MX, and MI detection sensitivities and (b) SEM image of a hole on the ribbon. (c) Magnetic field dependence on MX ratio for a hole-based MX biosensor with cell medium, LLC cells, and magnetically labelled LLC cells (ML-LLC). (d) MI- and MX-based detection sensitivities of the probe for ML-LLC with reference to LLC.

Figure 6

(a) The principle of a GMI-based magnetic biosensor using the ssDNA hybridization phenomenon as an example; (b) impedance response of the sensing element on the magnetic particles concentration. Reprinted with permission from Ref. [24]. Copyright 2022 Elsevier; (c) schematic design of the multiwire-based MI device; and (d) relative change in MI response as a function of the number of active glass-coated microwires for different bead concentrations. Reprinted with permission from Ref. [25]. Copyright 2022 Elsevier.

Figure 7

Compilation of results showing optimization of a multilayer structure’s MI ratio as a function of frequency. (a–c) The optimization of the dimensions of a single strip [Py/Ti]/Cu/[Py/Ti] film (i.e., sensing element) in a meander structure. Reprinted with permission from Ref. [68]. Copyright 2022 Elsevier. (d) The film was grown in a layered structure [Py/Ti]/Cu/[Py/Ti]. Reprinted with permission from Ref. [71]. Copyright 2022 AIP Publishing. (e) The meander has 12 segments, each one 0.16 mm wide and 5 mm long, and the separation between segments is 0.48 mm. GMI ratio as a function of magnetic field (f), frequency (g), and ac current (inset of (g)). (h) Field sensitivity of GMI as a function of frequency, with an inset showing Z(H).

Figure 8

(a) The low-field (-4 Oe to 9 Oe) GMI curve demonstrating the regions measured during nanoparticle detection. ‘A’ corresponds to H_DC = 2 Oe; ‘B’ is H_DC = 3.5 Oe (~H_K: the anisotropy field); and ‘C’ is H_DC = 5.5 Oe (>H_K); (b) the change in resistance (dR) in a soft ferromagnetic Co-rich wire when exposed to different quantities of nanoparticles relative to the resistance with no nanoparticles present. Measurement was performed at each of the DC fields H_DC = 0 Oe, 2 Oe, 3.5 Oe, and 5.5 Oe; (c) linear fit of the low-field region of the MI curve allowed us to calculate the stray field of Fe₃O₄ nanoparticles; and (d) the linear change in resistance (dR) for H_DC = 2 Oe indicates that this MI-based microwire sensor can be used to detect small concentrations of magnetic nanoparticles.

Figure 9

(a) Measurement location of MCG, which was 25 mm to the left of the pit of the stomach and 10 mm from the surface. (b,c) Real-time recordings of MCG and ECG signals measured by a peak-to-peak VD-type MI gradiometer without any magnetic shielding equipment. Reprinted with permission Ref. [86]. Copyright 2022 Elsevier.

Figure 10

(a) Left to right: 3D schematics of the microfluidic system showing the PDMS micro-channel on one side of the glass substrate, the GMI microwire with electrical connections on the other side, and a picture of the actual device. (b) Picture of the experimental bench where one can see the programmable syringe pumps, the shielded box, the Helmholtz coils, and the microfluidic system. (c) Measured magnetic signal before and after (black dotted, t = 87.5 s) injection of the USPIOs plugs with 5.47 × 10⁻⁹ mol contents (2 mm long-20 nL volume with a molar concentration of 230 mmol/L). Reprinted with permission from Ref. [67]. Copyright 2022 AIP Publishing.

Figure 11

(a) Schematic for the respiratory motion test and results; inset is an image of the soft-magnetic-coil-based sensor probe; (b) breathing patterns of a 42-year-old patient were continuously tracked from waking to sleeping; (c) waking of the patient; (d) during sleep, the patient breathed more deeply (higher amplitude) and slowly (14 times per minute) than when awake (20 times per minute); (e) breathing patterns of this patient while sleeping with piano music; the person breathed more regularly and slowly (11 times per minute) than when sleeping without music (14 times per minute) (d). The abnormal breathing observed around 45 s almost disappeared when sleeping with music.

Figure 12

(a) Illustration of COVID-19 symptoms; top: schematic of the virus, bottom: breathing pattern of a patient with COVID-19. (b) Breathing pattern of the COVID-19 patient revealed irregular amplitude and several breathing anomalies. (c) Shortness of breath is evident from analysis of the peak-to-peak time versus measurement time. (d) The broad distribution of frequencies deduced from Fourier transform also reveals these features. (e) The COVID-19 patient lost the ability to hold his/her breath for a long time and required a much longer time to return to normal breathing.