Single molecule real-time label-free dynamic biosensing with nanoscale magnetic field sensor

Abstract

Disclosed herein are devices, systems, and methods for monitoring single-molecule biological processes using magnetic sensors and magnetic particles (MNPs). MNPs are attached to biological polymers (e.g., nucleic acids, proteins, etc.) and the motion of the MNPs is detected and / or monitored using magnetic sensors. Because the MNPs are small (e.g., their size is comparable to the size of the molecules being monitored) and tethered to biological polymers, changes in the volume of Brownian motion of the MNPs in solution can be monitored to monitor the movement of the MNPs and, by inference, the movement of the tethered biological polymers. The magnetic sensors are small (e.g., nanoscale or have a size that is about the size of the MNPs and the biological polymers) and can be used to detect even small changes in the position of the MNPs within the sensing region of the magnetic sensors.

Description

Background Technology

[0001] The ability to quantify interactions between biomolecules is of interest in a variety of applications, such as diagnostics, screening, disease staging, forensic analysis, pregnancy testing, drug development and testing, and scientific and medical research. Examples of measurable properties of biomolecular interactions include the affinity (e.g., the strength of molecular binding / interaction) and kinetics (e.g., the rate of molecular association and dissociation).

[0002] Traditional enzyme-linked immunosorbent assay (ELISA) systems are large-volume simulation systems that require final dilution of the reaction product, necessitating millions of enzyme labels to generate a signal detectable using conventional plate readers. Therefore, the sensitivity of traditional ELISA is limited to and beyond the picomolar (pg / mL) range.

[0003] Compared to ELISA systems, single-molecule systems are inherently digital because each molecule provides a corresponding signal that can be detected and counted. Single-molecule systems have the advantage of making it easier to determine the presence or absence of a signal than by detecting the absolute amount or amplitude of the signal. In other words, counting is easier than ensemble counting.

[0004] There has been increased attention on single-molecule assays in recent years. For example, the COVID-19 pandemic has put cancer patients at higher risk than usual because they may be more susceptible to viral infections after chemotherapy, stem cell transplantation, or surgery. Another example is the need for highly sensitive viral and pathogen detection to detect COVID-19 or human SARS-CoV-2 antibodies. Yet another application that could benefit from single-molecule assays is single-molecule immunoassays, which provide simple and highly sensitive detection of protein biomarkers.

[0005] Single-molecule detection has become possible for several applications. For example, the use of transtethered particle motion (TPM) technology has made it possible to detect the binding of a single biomolecule to a receptor anchored to the surface of a sensing device. In TPM, one end of a biopolymer (e.g., DNA, RNA, etc.) is immobilized to a rigid support, thereby forming a "transtethered biopolymer," and small particles (e.g., micrometer- or nanometer-sized) are attached to the other end. In solution, the transtethered biopolymer and the attached particles move due to constrained Brownian motion (the random motion of particles suspended in a medium). The volume occupied by the transtethered biopolymer (and the attached particles) is finite and depends on the size and shape of the transtethered biopolymer. Enzymes that interact directly with the biopolymer can alter the structure of the biopolymer at any given time. For example, for DNA and RNA, the volume occupied by the attached particles varies depending on DNA deformation (e.g., DNA circularization or DNA elongation). By observing and interpreting the changes in particle position over time, the dynamics and biochemical dynamics of the interaction between biopolymers and enzymes in solution can be described (for example).

[0006] The transtethered biopolymer can be, for example, a nucleotide sequence of a DNA fragment. Binding events typically alter the molecular dynamics of the receptor. Before incorporation of a complementary nucleotide, the DNA fragment may take on a coiled or U-shaped (ring) configuration (e.g., due to the presence of (partial) palindromes in the nucleotide sequence), and then a more linear or stretched configuration upon incorporation of the complementary nucleotide. This configurational change affects the Brownian motion volume occupied by the transtethered biopolymer. In TPM, this volume change can be detected by attaching particles (sometimes referred to as tags) to the receptor and observing the movement of the particles using optical techniques.

[0007] Data acquisition in TPM systems typically employs high-resolution, high-speed video microscopy to track and record nanoscale changes in particle average velocity and range of motion caused by regional alterations in the microenvironment. This single-molecule analysis technique has been implemented (for example) for dynamic in vitro monitoring of DNA-protein interactions and for detecting biochemically induced conformational changes in proteins, DNA, and RNA.

[0008] Because TPM relies on the ability to handle small variations in random motion patterns, image contrast must be sufficient and frame acquisition rates high enough to enable particle tracking and subsequent analysis. Current state-of-the-art TPM systems can optically track nanoscale particles attached to short (e.g., approximately 50 nm) chains with a positioning accuracy of 1–2 nm. While the high resolution is impressive, the number of particles that can be simultaneously tracked and analyzed within a small field of view is limited to a few hundred. Therefore, the throughput of such systems is limited. Increasing the field of view to allow monitoring of 10,000 nanoparticles degrades the positioning accuracy to greater than approximately 100 nm. This limitation, along with the technical complexity of high-throughput real-time motion tracking at the nanoscale, has currently confined the use of TPM to academic scientific curiosity and has prevented its widespread adoption in commercial applications such as diagnostics and drug discovery.

[0009] Particle size plays a crucial role in TPM measurements. Larger particles are easier to observe and track than smaller ones, but their random motion is only weakly affected by single-molecule processes due to the large size difference between the particle and the receptor. Furthermore, the proximity of large, wire-tied particles to rigid surfaces (e.g., where receptors are attached) generates tensile forces on biopolymers, altering their biophysical properties and potentially causing significant changes in binding equilibrium when molecules participate in biomarker binding reactions. Therefore, to accurately replicate in vivo processes, it is desirable to make wire-tied particles as small as possible. The random motion patterns of smaller particles are also more sensitive to perturbations caused by the binding of individual biomolecules. However, a problem with small particles is that they are more difficult to observe using optical systems. Strongly scattering 10 nm gold nanoparticles confined within 2D biological films have been optically observed and tracked. Larger sizes (typically greater than 40 nm in diameter) are preferred for reliable tracking when particles are tied to surfaces by biopolymers and allowed to move in and out of the focal plane. However, these sizes make the particles much larger than the molecules involved in many biologically relevant processes. Since the amount of light scattered at these length scales is proportional to the sixth power of the diameter, further reducing the particle size to match the molecular size would make it untrackable even with the most advanced optical systems available today.

[0010] Therefore, there is a need to improve single-molecule devices, systems, and methods to monitor and / or quantify interactions between biomolecules. Summary of the Invention

[0011] The content of this invention represents non-limiting embodiments of the invention.

[0012] This document discloses apparatus, systems, and methods for monitoring single-molecule processes using magnetic sensors. In some embodiments, magnetic particles (e.g., magnetic nanoparticles), referred to herein as MNPs, are attached to biopolymers (e.g., nucleic acids, proteins, etc.), also known as bonded polymers, to detect the movement of the MNPs. For example, the binding of individual molecules, antibody / antigen reactions, and / or structural changes of proteins or nucleic acids can be detected by observing, following, or tracking the position and / or movement of the MNPs using magnetic sensors. The MNPs are small (e.g., their size is comparable to that of the molecule being monitored) and bonded to the biopolymer, and the Brownian motion volume of the MNP in solution changes due to the bombardment of the MNP by molecules in the solution, thereby changing the position of the MNP and allowing the movement of the MNP, and the bonded biopolymer can be observed and / or monitored by inference. Changes in the position and / or movement of the MNPs can be inferred from changes in signals obtained from the magnetic sensor. For example, analysis of the autocorrelation function or power spectral density of the signal obtained from the magnetic sensor can reveal the presence, position, and / or movement of the MNPs.

[0013] A magnetic sensor (e.g., nanoscale or approximately the size of the MNP and / or the biopolymer) can be used to detect even small changes in the position of the MNP within the sensing region of the magnetic sensor. A baseline response (e.g., signal) of the magnetic sensor can be determined in the absence of any MNP, and then, after the MNP has been attached to the biopolymer within the sensing region of the magnetic sensor, the signal provided by the magnetic sensor is a superposition of the Brownian motion of the MNP and the baseline sensor response. Therefore, the effect of the MNP moving according to a random process is to add noise to the baseline sensor response. Conclusions about the presence, location, and / or movement of the MNP can be drawn by detecting and / or analyzing noise-contributing factors from the MNP in the sensor signal in either or both of the time and frequency domains (e.g., by detecting fluctuations near the average value, examining / processing / analyzing the autocorrelation function or power spectral density, etc.). In this way, the MNP can be a reporter of biopolymer activity (e.g., structural changes).

[0014] Because the disclosed apparatus, system, and method are imaging-independent, the MNP can be substantially smaller than that used in TPM systems, thereby providing higher resolution and allowing for higher throughput from devices of chosen size. Furthermore, the magnetic sensor and MNP can be used to reliably detect nanoscale motion (e.g., movement of approximately a few nanometers) with high accuracy. The disclosed apparatus, system, and method can be used in a variety of single-molecule applications, including but not limited to diagnostics, screening, disease staging, forensic analysis, pregnancy testing, drug development and testing, immunoassays, nucleic acid sequencing, and scientific and medical research. Compared to conventional TPM or traditional ELISA methods that rely on optics, the disclosed apparatus, system, and method offer potentially higher throughput as well as higher sensitivity and accuracy. Attached Figure Description

[0015] The objectives, features, and advantages of the present invention will readily become apparent from the following description of specific embodiments in conjunction with the accompanying drawings, in which:

[0016] Figure 1A This is a schematic representation of nanoscale monitoring of the movement of MNPs attached to a biopolymer, according to some embodiments.

[0017] Figure 1B The illustration shows examples of recorded sensor signals according to some embodiments.

[0018] Figure 2A , 2B The 2C and 2D diagrams illustrate examples of four reversible biomolecular single-molecule processes that affect the velocity and range of motion of MNPs according to some embodiments.

[0019] Figure 3 The illustration shows a portion of a magnetic sensor according to some embodiments.

[0020] Figure 4A and 4B The diagram illustrates the resistance of a magnetoresistive (MR) sensor that can be used according to some embodiments.

[0021] Figure 5A The illustration shows a spin torque oscillator (STO) sensor that can be used according to some embodiments.

[0022] Figure 5B The experimental response of STO is shown under instanced conditions.

[0023] Figure 5C and 5D The illustration shows the short nanosecond field pulse of STO that can be used according to some embodiments.

[0024] Figure 6This is a diagram of a sample readhead containing a magnetic sensor used in perpendicular magnetic recording (PMR) applications.

[0025] Figure 7A The illustration shows a magnetic sensor according to some embodiments, which does not have any MNPs nearby.

[0026] Figure 7B The illustration shows a magnetic sensor of an MNP located directly above it, according to some embodiments.

[0027] Figure 7C The illustration shows an MNP and its lateral offset magnetic sensor according to some embodiments.

[0028] Figure 8 The illustration shows the results of nanomagnetic simulations of an exemplary magnetic sensor in the presence of MNPs at various locations relative to the magnetic sensor, according to some embodiments.

[0029] Figure 9A These are planar scanning electron microscopy (SEM) images of exemplary magnetic sensors having an MNP within their sensing area, according to some embodiments.

[0030] Figure 9B and 9C Illustrated description of some embodiments Figure 9A The behavior of an exemplary magnetic sensor.

[0031] Figure 10A An instance model is presented, based on some embodiments, for analyzing the motion of an MNP.

[0032] Figure 10B It is a graphical representation of a single particle diffusing with a harmonic potential applied by the DNA strand.

[0033] Figure 11A and 11B Illustrated explanation of thought experiments.

[0034] Figure 12A The illustration shows an exemplary magnetic sensor according to some embodiments.

[0035] Figure 12B Plot the expected noise power spectral density (PSD) of an example magnetic sensor and the Lorentz function characterizing the PSD of the MNP with confined Brownian motion.

[0036] Figure 13 It is a graphic illustration of the experiments conducted by the inventor.

[0037] Figure 14 The diagram illustrates the measured PSD of the three tested magnetic sensors.

[0038] Figure 15A , 15B The diagrams for 15C, 15D, and 15E illustrate the test results of the study on the influence of the bias voltage of the magnetic sensor.

[0039] Figure 16 The diagram illustrates a one-dimensional model that includes force components due to a magnetic sensor.

[0040] Figure 17A , 17B The diagram 17C illustrates the three states of a system according to some embodiments.

[0041] Figure 18A , 18B The 18C diagram illustrates exemplary recorded current fluctuations and corresponding autocorrelation functions of two exemplary magnetic sensors according to some embodiments.

[0042] Figure 19A This is a block diagram illustrating the components of an exemplary monitoring system according to some embodiments.

[0043] Figure 19B , 19C The diagram 19D illustrates the various parts of an exemplary monitoring system according to some embodiments.

[0044] Figure 19E The illustration shows a pattern of a magnetic sensor in a sensor array according to some embodiments.

[0045] Figure 20 This is a flowchart of an exemplary method for sensing motion via a connected MNP according to some embodiments.

[0046] Figure 21 The diagram illustrates several components involved in a multiplexed magnetic digital homogenized non-enzymatic (HoNon) ELISA according to some embodiments.

[0047] Figure 22A and 22B The illustration shows a portion of an exemplary procedure for a multiplexed magnetic digital HoNon ELISA according to some embodiments.

[0048] Figure 23 The illustrations depict additional steps of an exemplary procedure for a multiplexed magnetic digital HoNon ELISA according to some embodiments.

[0049] Figure 24A The illustration shows the addition of a complex biological solution containing multiple biomarkers according to some embodiments.

[0050] Figure 24BThis is a depiction of the appearance that a sensor array may appear after the addition of a complex biological solution containing multiple biomarkers, according to some embodiments.

[0051] Figure 25 The illustrations illustrate how, according to some embodiments, the binding of biomarkers can be detected based on the noise PSD detected by a specific magnetic sensor.

[0052] Figure 26 This is a flowchart illustrating a method of using a magnetic sensor array according to some embodiments.

[0053] To facilitate understanding, the same element symbols have been used where possible to designate common elements across the figures. It is anticipated that, in one embodiment, the disclosed elements may be advantageously used in other embodiments without specific description. Furthermore, the description of an element in the context of one drawing may be applicable to other drawings illustrating that element. Detailed Implementation

[0054] The random motion of freely diffused or tethered particles embedded in biological systems reveals a wealth of information. Statistical analysis of particle motion can enhance our understanding of important in vivo processes through its in vivo results. While tracking freely diffused strongly scattering particles as small as 10 nm is a powerful tool for studying biological thin films, tracking tethered particles reveals a much broader range of single-molecule behaviors. TPM experiments use biopolymers (e.g., DNA, RNA, proteins) with one end anchored to a rigid surface and the other end attached to a particle to monitor a variety of biophysical and biochemical processes; however, the throughput and accuracy of traditional TPM systems are limited by their dependence on the optical techniques used to track the particles.

[0055] This document discloses apparatus, systems, and methods for dynamically sensing biochemically induced alterations in the motion patterns of tethered nanoparticles without involving imaging. Alternatively, embodiments disclosed herein use magnetic sensors and monitor the responses of those magnetic sensors to detect the confined diffusion of tethered magnetic particles, since the tethered magnetic particles move randomly within or to the corresponding detection region of the magnetic sensor. For example, the magnetic sensor may be a nanoscale magnetic field sensor (MFS). For example, the detected response or characteristic of the magnetic sensor may be a detected tunneling current, voltage, or resistance in the time or frequency domain, or any other characteristic of the detectable magnetic sensor. The detection region of the magnetic sensor may have (for example) between about 10... 5 nm 3 With 5×10 5 nm 3 The volume between.

[0056] For example, magnetic particles can be or include magnetic nanoparticles (MNPs), such as molecules, superparamagnetic nanoparticles, or ferromagnetic particles. As those skilled in the art will understand, magnetic nanoparticles are generally considered to be particles of matter with diameters between 1 and 100 nanometers (nm). Magnetic particles can be nanoparticles with high magnetic anisotropy. Examples of magnetic particles with high magnetic anisotropy include, but are not limited to, Fe3O4, FePt, FePd, and CoPt. In some applications involving nucleotides, magnetic particles may be synthesized and coated with, for example, SiO2. See, for example, M. Aslam, L. Fu, S. Li, and VPDravid, “Silica encapsulation and magnetic properties of FePt nanoparticles” (Journal of Colloid and Interface Science, Vol. 290, No. 2, October 15, 2005, pp. 444-449).

[0057] For example, magnetic particles can be or include organometallic compounds. As will be understood, organometallic compounds are any number of substances containing at least one metal bonded to carbon (wherein carbon is part of an organic group). Examples of organometallic compounds include Gilman reagents (containing lithium and copper), Grignard reagents (containing magnesium), nickel tetracarbonyl and ferrocene (containing transition metals), organolithium compounds (e.g., n-butyllithium (n-BuLi)), organozinc compounds (e.g., diethylzinc (Et2Zn)), organotin compounds (e.g., tributyltin hydride (Bu3SnH)), organoboron compounds (e.g., triethylboron (Et3B)), and organoaluminum compounds (e.g., trimethylaluminum (Me3Al)).

[0058] For example, magnetic particles can be or include charged molecules or any other functional molecular groups that can be detected by nanoscale magnetic sensors. In other words, if a magnetic sensor can detect the presence of a candidate magnetic particle, and said candidate magnetic particle can attach to the biopolymer of interest, then that candidate magnetic particle is suitable for use in the apparatus, systems, and methods described herein.

[0059] While it is anticipated that the magnetic particles used in many applications will likely be nanoparticles with sizes comparable to the observed biopolymers, the systems, apparatus, and methods described herein are generally applicable to magnetic particles. Therefore, it should be understood that the abbreviation “MNP” is used herein for convenience, and “MNP” generally refers to magnetic particles. Thus, unless the context otherwise indicates, the disclosure of MNPs mentioned or illustrated herein is not necessarily limited to nanoparticles. Similarly, although MNPs are anticipated to be superparamagnetic, the invention is not limited to their use with superparamagnetic MNPs.

[0060] Figure 1A and 1B The diagram illustrates the principle of using a magnetic sensor to monitor the motion of an MNP at the nanometer level, according to some embodiments. For example... Figure 1A As shown, the MNP 102 is bound to the rigid surface 117 of the monitoring device via a biopolymer 101 (e.g., ssDNA, dsDNA, RNA, protein, etc.). The biopolymer 101 can also be referred to as a "tether." Due to interactions with the molecules of the surrounding fluid, the MNP 102 undergoes a process within the constrained motion region 203... Figure 1A Arrow 103 represents random (irregular) motion, and the constrained motion region 203 is around a certain average distance from the magnetic sensor 105. <r>The volume of the MNP 102 is within or outside the sensing region 206 of the magnetic sensor 105. For some biosensing applications, the sensing region 206 may have (for example) a volume between approximately 10. 5 nm 3 With approximately 5×10 5 nm 3 The volume between. Of course, the volume of sensing region 206 can be selected to suit a particular application and can be larger or smaller than these values. Depending on the design of magnetic sensor 105 (e.g., its sensitivity), the bias voltage applied to magnetic sensor 105, the characteristics of MNP 102 (e.g., its size), the characteristics of biopolymer 101 (e.g., its length), and the position of biopolymer 101 relative to magnetic sensor 105 bound to surface 117, constrained motion region 203 and sensing region 206 may substantially overlap, or may be offset, such as Figure 1A As illustrated in the example. Similarly, the volumes of the constrained motion region 203 and the sensing region 206 may be the same or different. Figure 1A In the example illustrated in the figure, the constrained motion region 203 is larger than the sensing region 206, and the constrained motion region 203 is offset from the sensing region 206 in the lateral direction ρ.

[0061] Figure 1B The illustration depicts an example of a recorded sensor signal 207 according to some embodiments. In this example, the sensor signal 207 is recorded as a statistically fixed fluctuation of a detectable characteristic of a magnetic sensor 105, which may be, for example, a measured current, voltage, resistance, oscillation frequency, phase noise, frequency noise, or any other characteristic of the magnetic sensor 105 indicating a detected change in the magnetic environment of the magnetic sensor 105 (e.g., within the sensing area 206, due to the presence, absence, and / or movement of the MNP 102), as further described below. One advantage of using the magnetic sensor 105 is that the MNP 102 can be much smaller than the particles used in TPM systems that rely on optical tracking. In some embodiments, for example, the MNP 102 has a biomolecular size (e.g., its size may be about 5 nm or less).

[0062] To allow the detection of MNP 102, the response of the magnetic sensor 105, represented by sensor signal 207, should change because the mobility of MNP 102 is affected by interactions with individual monomolecules (e.g., the surrounding solution). Therefore, it is desirable that MNP 102 be small enough that its mobility is affected by other molecules. For example, when a biomolecule of a considerable size binds to the molecule attached to MNP 102, or when the attached molecule (biopolymer 101) changes its structure, the sensor signal 207 (e.g., a noise component of the sensor signal 207 due to the movement of MNP 102) should change, as described below for (for example) Figure 18A , 18B As described in the discussion of 18C. In both cases, the effective hydrodynamic radius of the tied MNP 102 changes, as do its statistical velocity and range of motion. Therefore, when the tied MNP 102 is fixed to or near the surface of the magnetic sensor 105 by a specific target and when the structural state of the chain / biopolymer 101 (e.g., dsDNA, ssDNA, RNA, protein) changes, both the amplitude and noise of the sensor signal 207 should change.

[0063] The systems, apparatus, and methods disclosed herein can be used to detect and / or monitor various alterations in biomolecular processes, such as (for example) protein cyclization (linking and unlinking), structural dynamics of folding and unfolding, antibody / antigen interactions, and their strength. Figure 2A , 2B The 2C and 2D diagrams illustrate examples of four reversible single-molecule processes that affect the velocity and range of motion of MNP 102 according to some embodiments. Figure 2A , 2B Each of 2C and 2D illustrates a magnetic sensor 105 and a biopolymer 101, one end of which is attached to the surface 117 of the monitoring device in the vicinity of the magnetic sensor 105 (e.g., at the binding site 116, discussed below), and the other end of the biopolymer 101 is attached to the MNP 102. Figure 2A and 2C The diagram illustrates a demonstrative antibody-antigen reaction, and Figure 2B and 2D Illustrated explanation of the exemplary structural changes. Figure 2A The diagram illustrates how binding large biomolecules, such as proteins, DNA, or RNA, to MNP 102 increases its mass and effective hydrodynamic radius, leading to changes in detectable confined diffusion. (As described further below, the binding of molecules of comparable size to MNP 102 can be detected by detecting changes in the corner frequency of the Lorentz function of the noisy PSD characterizing the confined Brownian motion of MNP 102.) Figure 2B The diagram illustrates, for example, the significant structural changes in protein or nucleic acid folding and unfolding that alter the detectable effective hydrodynamic radius of MNP 102. Similar to... Figure 2A of Figure 2C The diagram illustrates how MNP 102 can bind to molecules fixed on surface 117 of the monitoring device (in...). Figure 2C (The example is illustrated as an antigen). The strength of the interaction can be studied according to some examples. Figure 2D The diagram illustrates how structural changes in the hairpin structure of DNA or RNA (biopolymer 101) also restrict the movement of MNP 102. How nucleic acids behave (e.g., encapsulation and unencapsulation) with temperature changes is of interest. The devices, systems, and methods disclosed herein can be used to detect and / or monitor changes, including but not limited to… Figure 2A , 2B The changes illustrated in 2C and 2D.

[0064] Magnetic sensor

[0065] The embodiments disclosed herein use at least one magnetic sensor 105 (e.g., a magnetoresistive nanoscale sensor or any other type of magnetic sensor) to detect the presence of one or more MNPs 102 (e.g., magnetic nanoparticles, organometallic complexes, charged molecules, etc.) coupled to the biopolymer 101. Figure 3 The illustration shows a portion of an exemplary magnetic sensor 105 according to some embodiments. Figure 3 An exemplary magnetic sensor 105 has a bottom surface 108 and a top surface 109, and comprises three layers: a first ferromagnetic layer 106A, a second ferromagnetic layer 106B, and a nonmagnetic spacer layer 107 located between the first ferromagnetic layer 106A and the second ferromagnetic layer 106B. For example, suitable materials for the first ferromagnetic layer 106A and the second ferromagnetic layer 106B include alloys of Co, Ni, and Fe (sometimes mixed with other elements). In some embodiments, the magnetic sensor 105 is implemented using thin-film technology, and the first ferromagnetic layer 106A and the second ferromagnetic layer 106B are engineered to orient their magnetic moments in or perpendicular to the plane of the film. For example, the nonmagnetic spacer layer 107 may be a metallic material such as copper or silver, in which case the structure is referred to as a spin valve (SV), or the nonmagnetic spacer layer 107 may be an insulator such as aluminum oxide or magnesium oxide, in which case the structure is referred to as a magnetic tunnel junction (MTJ).

[0066] Additional materials can be deposited in Figure 3 The first ferromagnetic layer 106A, the second ferromagnetic layer 106B, and the non-magnetic spacer layer 107 shown are positioned below and above for purposes such as interface smoothing, texturing, and / or protection against processing used to pattern the device in which the magnetic sensor 105 is incorporated. Furthermore, as further described below, the magnetic sensor 105 may be encapsulated in or covered by a material to protect it from the influence of fluids used in single-molecule analysis. However, the active area of ​​the magnetic sensor 105 is located in… Figure 3 In the three-layer structure illustrated in the diagram, the components that come into contact with the magnetic sensor 105 (e.g., the reading circuitry) may contact one of the first ferromagnetic layer 106A, the second ferromagnetic layer 106B, or the non-magnetic spacer layer 107, or the components may contact another part of the magnetic sensor 105.

[0067] like Figure 4A and 4B As shown, the resistance of a magnetoresistive sensor (e.g., a magnetic sensor 105 of one possible type) is proportional to 1-cos(θ), where θ is... Figure 3 The angle between the torque of the first ferromagnetic layer 106A and the torque of the second ferromagnetic layer 106B is shown in the diagram. To maximize the signal generated by the magnetic field and provide a linear response of the magnetic sensor 105 to the applied magnetic field, the magnetic sensor 105 can be designed such that the torques of the first ferromagnetic layer 106A and the second ferromagnetic layer 106B are oriented relative to each other by π / 2 radians or 90 degrees in the absence of a magnetic field. This orientation can be achieved by any number of methods known in the art. For example, one solution is to use an antiferromagnet to "pin" the magnetization direction of one of the ferromagnetic layers (first ferromagnetic layer 106A or second ferromagnetic layer 106B, designated "FM1") through an effect called exchange bias, and then coat the magnetic sensor 105 with a double layer having an insulating layer and a permanent magnet. The insulating layer prevents electrical short circuits in the magnetic sensor 105, and the permanent magnet supplies a "hard bias" magnetic field perpendicular to the pinning direction of FM1. This "hard bias" magnetic field then rotates the second ferromagnet (either the second ferromagnetic layer 106B or the first ferromagnetic layer 106A, designated "FM2") to produce the desired configuration. The magnetic field parallel to FM1 then rotates FM2 about this 90-degree configuration, and the change in resistance of the magnetic sensor 105 results in a voltage (or current) signal (e.g., sensor signal 207) that can be calibrated to measure the field acting on the magnetic sensor 105. In this way, the magnetic sensor 105 functions as a field-to-voltage transducer.

[0068] For biosensing applications, the magnetic sensor 105 should be designed such that FM1 and FM2 are weakly coupled, and that perturbations to the position of FM2 caused by the presence of MNP 102 can be detected in the sensor signal 207. If the coupling between FM1 and FM2 is too strong, the presence of MNP 102 will not generate much perturbation in the sensor signal 207 to be detected. On the other hand, if the coupling between FM1 and FM2 is too weak, the magnetic sensor 105 may be thermally unstable, allowing thermal fluctuations to dominate and reducing the signal-to-noise ratio (SNR). As will be further explained below, a particular magnetic sensor 105 designed for use in magnetic recording has characteristics that allow it to be used in a particular biosensing application.

[0069] Note that although the examples described above use ferromagnets whose torques are oriented 90 degrees relative to each other in the plane of the film, a vertical configuration can be achieved alternatively by orienting the torques of one of the ferromagnetic layers (first ferromagnetic layer 106A or second ferromagnetic layer 106B) outside the plane of the film, which can be accomplished using so-called vertical magnetic anisotropy (PMA).

[0070] In some embodiments, the magnetic sensor 105 utilizes a quantum mechanical effect known as spin torque. In such a magnetic sensor 105, a current passing through a first ferromagnetic layer 106A (or alternatively, a second ferromagnetic layer 106B) in the SV or MTJ preferentially allows electrons with spin parallel to the layer's torque to transmit through, while electrons with antiparallel spins are more likely to be reflected. In this way, the current becomes spin-polarized, with more electrons of one spin type than the other. This spin-polarized current then interacts with the second ferromagnetic layer 106B (or the first ferromagnetic layer 106A), thereby applying a torque to the torque of that layer. This torque may, in different cases, cause the torque of the second ferromagnetic layer 106B (or the first ferromagnetic layer 106A) to precess around an effective magnetic field acting on the ferromagnet, or the torque may cause the torque to reversibly switch between two orientations defined by a uniaxial anisotropy induced in the system. The resulting spin torque oscillator (STO) is frequency-tunable by changing the magnetic field acting upon it. Therefore, the resulting spin torque oscillator has the ability to act as a magnetic field frequency (or phase) transducer (thereby generating an AC signal with frequency), such as Figure 5A As shown in the image, Figure 5A This diagram illustrates the concept of using an STO sensor in magnetic recording. Figure 5B This demonstrates the experimental response of a transdomain STO (Synchronous Transient Tolling) obtained through a delay detection circuit when an AC magnetic field with a frequency of 1 GHz and an interpeak amplitude of 5 mT is applied. This result, within a short nanosecond field pulse, and... Figure 5C and 5D The results shown in the figure illustrate how these oscillators can be used as nanoscale magnetic field detectors. Additional details can be found in "Delay detection of frequency modulation signal from a spin-torqueoscillator under a nanosecond-pulsed magnetic field" by T. Nagasawa, H. Suto, K. Kudo, T. Yang, K. Mizushima and R. Sato (Journal of Applied Physics, Vol. 111, 07C908 (2012)), which is incorporated herein by reference in its entirety for all purposes.

[0071] In some embodiments, the magnetic sensor 105 includes an STO to sense the magnetic field induced by the MNP 102 coupled to the biopolymer 101. The magnetic sensor 105 is configured to detect changes in the precession frequency of the magnetization of the magnetic layer of the magnetic sensor 105, or its presence or absence, to sense the magnetic field of the MNP 102. The magnetic sensor 105 may include a magnetic free layer (e.g., a first ferromagnetic layer 106A or a second ferromagnetic layer 106B), a magnetic pinning layer (e.g., a second ferromagnetic layer 106B or a first ferromagnetic layer 106A), and a non-magnetic layer (e.g., a non-magnetic spacer layer 107) between the free layer and the pinning layer, as described above in the section on... Figure 3 As described in the discussion. In some embodiments, during operation, a detection circuit system coupled to the magnetic sensor 105 senses a (DC) current passing through the layers of the magnetic sensor 105. The spin polarization of electrons traveling through the magnetic sensor 105 causes the spin torque of one or more magnetized electrons in the layers to induce precession. The frequency of this oscillation changes in response to a magnetic field generated near the magnetic sensor 105 by the MNP 102. In some embodiments, changes in the oscillation frequency of the sensor or noise in the oscillation frequency (referred to as phase noise or frequency noise) can be used to detect the magnetic field and therefore the presence, absence, or change of the MNP 102.

[0072] In some embodiments, the magnetic sensor 105 includes an MTJ, and changes in the resistance, through current, or across voltage of the magnetic sensor 105 are used to detect the presence, absence, or movement of the MNP 102 within the sensing region 206 of the magnetic sensor 105. For example, an MTJ similar to those used in hard disk drives is an example suitable for use with the magnetic sensor 105 in the devices, systems, and methods described herein. Such a magnetic sensor 105 can be used to monitor, for example, nanoscale changes in the movement patterns of 20 nm superparamagnetic iron oxide nanoparticles suitable for the MNP 102, as further described below. It should be understood that other MNPs 102, such as Fe3O4 and FePt, can also be used, but the experimental results below pertain to iron oxide nanoparticles because functionalizing other particles (e.g., Fe3O4 and FePt) for the junction may be more challenging, and imaging with scanning electron microscopy to confirm the presence of the MNP 102 in the sensing region 206 may be difficult or impossible. Similarly, MNP 102 with a wavelength greater than or less than 20 nm can be used.

[0073] To illustrate the specific concepts of the magnetic sensor 105 applicable to the apparatus, systems, and methods described herein, Figure 6 The diagram illustrates the operation of a magnetic sensor capable of reading data previously recorded on a magnetic recording medium. Specifically, Figure 6 This is a diagram of a portion of an exemplary readhead 240 containing a magnetic sensor, used in a perpendicular magnetic recording (PMR) application. The surface of the recording medium 250 is in the xz plane, similar to the air bearing surface (ABS) of the exemplary readhead 240 for reading information stored on the recording medium 250. The recording medium 250 may have multiple concentric magnetic tracks on which information can be recorded, including magnetic track 251 (which is in... Figure 6 The magnetic track being read). An exemplary read head 240 is contained within the wafer plane (where it is used). Figure 6 The coordinates shown are in the xy plane. These multiple layers include a free layer 260, a reference layer 262, and a pinning layer 264. The free layer 260, reference layer 262, and pinning layer 264 may correspond to the first ferromagnetic layer 106A, the non-magnetic spacer layer 107, and the second ferromagnetic layer 106B (or equivalently to the second ferromagnetic layer 106B, the non-magnetic spacer layer 107, and the first ferromagnetic layer 106A) described above, respectively. The magnetic moment 263 of the reference layer 262 is in a specific direction... Figure 6 The image shows the magnetic moment 265 of the pinning layer 264 in the positive y-direction. The magnetic moment 265 can be pinned (fixed in a specific direction) by the antiferromagnetic element 266, as described above. Figure 6 In this configuration, the magnetic moment 265 of the pinned layer 264 is pinned in the negative y-direction. The magnetic moment 261 of the free layer 260 rotates freely in response to the applied or induced magnetic field. Hard bias regions 268A and 268B may be situated laterally (in the so-called lateral magnetic track direction) with the free layer 260, the reference layer 262, and / or the pinned layer 264 to supply a magnetic field perpendicular to the magnetic moment 265 of the pinned layer 264. Figure 6 In the middle, the torques 269A and 269B of the hard bias regions 268A and 268B are oriented in the positive x-direction on the right side of the page. The circuit system 270 coupled to the layer provides a bias voltage (or equivalently, a bias current) to read the information stored on the recording medium 250.

[0074] like Figure 6 As shown, the magnetic moment 261 of the free layer 260 is oriented in a predetermined or equilibrium direction (in Figure 6 In the middle, it is located on the right side of the page, along the x-axis, perpendicular to the magnetic moment 263 of the reference layer 262 and perpendicular to the magnetic moment 265 of the pinning layer 264. For example... Figure 6 As shown, when a "bit" on the recording medium 250 causes an upward-pointing magnetic field toward the exemplary read head 240, the magnetic moment 261 of the free layer 260 rotates upward, thereby constructively adding a component to the magnetic field generated by the bias applied to the exemplary read head 240 through the circuit system 270. Therefore, the resistance of the exemplary read head 240 decreases. Conversely, when a "bit" on the recording medium 250 causes a downward-pointing magnetic field away from the exemplary read head 240, the magnetic moment 261 of the free layer 260 rotates downward in the opposite direction, thereby adding a destructive component to the magnetic field generated by the bias applied through the circuit system 270. Therefore, the resistance of the exemplary read head 240 increases. This change in resistance thus indicates which of the two possible "bits" (upward or downward, which can be interpreted as 0 or 1 (or vice versa)) on the recording medium 250 has been detected.

[0075] Figure 7A , 7B The 7C diagram illustrates how these principles, based on some embodiments disclosed herein, can be applied to single-molecule sensing devices, systems, and methods. Figure 7A The diagram illustrates the portions of a magnetic sensor 105 that has no MNP 102 in its vicinity. In the presence of an applied magnetic field H oriented in the positive z-direction (e.g., caused by a bias voltage), the magnetic moment 261 of the free layer 260 forms an angle with the x-axis. Oriented at Figure 7A The direction shown in the upper panel. If the applied magnetic field H is oriented in the negative z direction, then the magnetic moment 261 of the free layer 260 is at an angle to the x-axis. Oriented at Figure 7A The direction is shown in the lower panel. Therefore, the peak-to-peak current sensed by the magnetic sensor 105 under the illustrated conditions (e.g., the amplitude difference under these conditions when the direction of the applied magnetic field is reversed) is given by ΔI0. Thus, ΔI0 provides the baseline peak positive and negative current amplitudes for the magnetic sensor 105 in the absence of any MNP 102.

[0076] Figure 7B The diagram illustrates the magnetic sensor 105 with the MNP 102 positioned directly above (in the z-direction) the free layer 260 of the magnetic sensor 105. As shown in the upper panel, the applied magnetic field H in the positive z-direction causes the magnetic moment of the MNP 102 to become substantially oriented in the same direction as the applied magnetic field H. Therefore, at the location of the free layer 260, the magnetic field caused by the MNP 102 constructively adds to the applied magnetic field H, and the magnetic moment 261 of the free layer 260 is now oriented at an angle to the x-axis. The rotation is made closer to the direction of the applied magnetic field H. If the applied magnetic field H is oriented in the negative z-direction, then the magnetic moment 261 of the free layer 260 is at an angle to the x-axis. Rotate to Figure 7B The direction shown in the lower panel is due to the magnetic field constructively added to the applied magnetic field H by the MNP 102. The peak-to-peak amplitude of the current sensed by the magnetic sensor 105 under these conditions is determined by... (Where "MP" stands for "magnetic particle") is given. Due to... Figure 7A In the case illustrated, the magnetic moment 261 of the free layer 260 is more closely aligned with the applied magnetic field H, therefore the resistance of the magnetic sensor 105 is relatively... Figure 7A Its value decreases, and

[0077] Figure 7C The diagram illustrates the magnetic sensor 105 with MNP 102 laterally offset (specifically, offset in the x-direction) from the free layer 260 of the magnetic sensor 105. For example... Figure 7C As shown in the upper panel, the applied magnetic field H in the positive z-direction causes the magnetic moment of MNP 102 to become substantially oriented in the same direction as the applied magnetic field H. However, now, because MNP 102 is laterally offset from the free layer 260, the magnetic field caused by MNP 102 is located in the opposite direction to the applied magnetic field H at the position of the free layer 260. Therefore, the magnetic field caused by MNP 102 reduces the effect of the applied magnetic field H on the free layer 260, and the magnetic moment 261 of the free layer 260 rotates away from it. Figure 7B Its direction. Now, the magnetic moment 261 of free layer 260 is at an angle to the x-axis. Similarly, when the applied magnetic field H is oriented in the negative z-direction, the magnetic field of MNP 102 reduces the applied magnetic field H at the location of the free layer 260, therefore the magnetic moment 261 of the free layer 260 forms an angle with the x-axis. Rotation, such as Figure 7B As shown in the lower panel. In this case, the peak-to-peak current amplitude sensed by the magnetic sensor 105 is reduced to in

[0078] Therefore, by monitoring the current passing through the magnetic sensor 105 (or any representation of the current, such as resistance or voltage; or, in the case of different types of magnetic sensors 105, representing some other characteristic of the magnetic environment sensed by the magnetic sensor 105), the presence of the MNP 102 and the position of the MNP 102 relative to the free layer 260 (and thus the magnetic sensor 105) can be detected and monitored, as further described below. Figure 8 The diagram illustrates the results of nanomagnetic simulations of the magnetic sensor 105 with MNP 102 present at various locations relative to the exemplary magnetic sensor 105, according to some embodiments. Profiling graph 402 illustrates the effect of MNP 102 in... Figure 7A , 7B The magnetic field acting on the magnetic sensor 105 at various positions of MNP 102 in the xy plane is calculated when MNP 102 is 10 nm above the xy plane (at a z-value of 10 nm). As indicated by cross section 406, the magnetic sensor 105 is centered at coordinates (0, 0) in the xy plane, indicated by position 404. Cross section 406 shows the magnetic field magnitude at the position y = 0 (indicated by dashed line 416 in profile graph 402) and at various positions along the z-axis, varying with the lateral position of MNP 102 along the x-axis, ranging from 10 nm to 60 nm from the surface of the magnetic sensor 105. Graph 408 shows the magnetic field magnitude along dashed line 420 in cross section 406. As shown, when MNP 102 is 10 nm directly above the magnetic sensor 105, the magnetic field amplitude is approximately 100 Oersted, and when MNP 102 is 60 nm above the magnetic sensor 105, the magnetic field amplitude is close to 0.

[0079] Cross section 412 shows the magnetic field magnitude at the position x=0 (indicated by the dashed line 418 in profile graph 402) and at various positions along the z-axis, varying with the lateral position of MNP 102 along the y-axis, ranging from 10 nm to 60 nm from the surface of magnetic sensor 105. Graph 414 shows the magnetic field magnitude at position 410 in profile graph 402 (which is a lateral offset of 39 nm along the y-axis) along the dashed line 422 in cross section 412. As shown, when MNP 102 is 10 nm above the surface of magnetic sensor 105 and laterally offset by 39 nm, the magnetic field amplitude is approximately -4 Oersted, and when MNP 102 is 60 nm above the magnetic sensor 105 and laterally offset by 39 nm, the magnetic field amplitude is close to 0. Therefore, Figure 8 The diagram illustrates that the magnitude of the magnetic field essentially changes as the MNP 102 changes position in three-dimensional space. Even a slight change in position results in a change in the detected magnetic field. Both its amplitude and direction change, and these changes can be detected by the free layer 260 of the magnetic sensor 105. Therefore, the position of the MNP 102 can be inferred by interpreting the signal from the magnetic sensor 105 rather than by directly observing it using an imaging system.

[0080] Figure 9A The images show planar scanning electron microscopy (SEM) images of an exemplary magnetic sensor 105 with the MNP 102 confined within a sensing region 206 (the dashed lines indicate the estimated or approximate boundary of the sensing region 206 in the xy plane). The exemplary magnetic sensor 105 has a diameter of approximately 30 × 40 nm in the xy plane. 2 The surface area of ​​the MTJ. In the exemplary embodiment shown, the junction region is parallel to the xz plane (outside the page), and the tunneling current flows in the y-axis direction. Figure 9A A single 20nm MNP 102 is shown within the sensing region 206. The effective sensing region 206 of the exemplary magnetic sensor 105, originally developed for magnetic recording applications, is designed to be extremely small (e.g., between approximately 10 nm). 5 nm 3 With approximately 5×10 5 nm 3 (between) to detect the magnetization orientation of small magnetic domains in the recording medium and maximize the density of magnetic recording. Therefore, the effective sensing area is well-suited for detecting random motion of MNP 102 as described herein. It should be understood that the volume of the sensing area 206 can be any suitable value, and the ranges given above are merely examples.

[0081] Figure 9B and 9C The illustration shows a cross-sectional view of the magnetic sensor 105 under an external magnetic field H applied perpendicular to its surface. Figure 9B In the diagram, MNP 102 (depicted as a circle but not labeled to avoid obscuring the diagram) is fixed above magnetic sensor 105 (also unlabeled but shown with diagonal fill), and near magnetic sensor 105, the magnetic field lines are aligned with the external field (shown as thick arrows in the sensor area). As described above, the effective field measured by magnetic sensor 105 increases when MNP 102 is present, due to a constructive increase in the magnetic field.

[0082] exist Figure 9C In this diagram, MNP 102 (depicted as a circle but not labeled to avoid obscuring the diagram) is positioned at a laterally greater distance from the magnetic sensor 105 (again, not labeled but shown by diagonal fill), and the magnetic field lines affecting the free layer 260 point in the opposite direction to the external field. In this case, as described above, the effective magnetic field measured by the magnetic sensor 105 decreases. As MNP 102 moves laterally away from the magnetic sensor 105, the perturbation to the sensor signal 207 due to the presence of MNP 102 thus rapidly changes from positive to negative. Figure 9B and 9C As shown, magnetic field disturbances are extremely sensitive to the position of MNP 102 relative to magnetic sensor 105. When MNP 102... Figure 9B As shown in the diagram, when the magnetic field lines of MNP 102 are above the magnetic sensor 105, they are aligned with the external magnetic field. However, when MNP 102 is positioned as shown in the diagram, the magnetic field lines of MNP 102 are aligned with the external magnetic field. Figure 9C When the object is shifted laterally as shown in the diagram, it points in the opposite direction.

[0083] exist Figure 9B and 9C The effect of the movement of MNP 102 on sensor signal 207 is schematically illustrated by curve 209. When MNP 102, attached near magnetic sensor 105, moves around while an external magnetic field is applied to fix the magnetic moment of MNP 102 in a specific direction, MNP 102 induces dynamic random perturbations in sensor signal 207. The response of magnetic sensor 105 is affected by both the in-plane (in the xy plane) and out-of-plane (along the z-axis) movement of MNP 102. Even when no external field is applied, the presence of MNP 102 with a sufficiently high magnetic moment can be detected by magnetic sensor 105. In other words, the disclosed embodiments can be used with (for example) superparamagnetic MNPs and ferromagnetic MNPs.

[0084] In video imaging systems used in conventional TPM systems, the time averaging results (exposure time) and the frequency of observation (frame rate) are readily understood. While exposure time and frame rate do not limit the tracking of freely diffusing Brownian particles, they do significantly impact the observation of particles undergoing anomalous (or confined) diffusion (e.g., tied nanoparticles in biological systems). Time averaging when imaging such particles can have serious consequences for the apparent properties of the reported motion, as the observed velocity depends on the duration of observation. In extreme cases where the exposure time is too long, the particles will be blurred and will appear stationary at a certain equilibrium position. These drawbacks can be mitigated or overcome by using the magnetic sensor 105 described herein through systems, devices, and methods.

[0085] The ability of the magnetic sensor 105 to detect changes in the sensor signal 207 depends on the responsiveness of the detection circuitry (e.g., detection amplifier circuitry, other detection electronics, as described below). For example, if the magnetic sensor 105 responds too slowly (e.g., due to limitations of the detection circuitry, such as the sampling rate), the monitoring device or system may be unable to detect changes. Figure 2C and 2D The process illustrated in the diagram detects when MNP 102 moves to different equilibrium positions, but it may not be able to detect statistical velocities of MNP 102 that do not affect the equilibrium positions (e.g., for example). Figure 2A and 2B The process of molecular binding and structural changes shown in the figure.

[0086] Unlike video imaging systems that generate a series of particle images to track particle positions in both space and time, the magnetic sensor 105 generates a temporal response to a series of random, similar (but not identical) impacts or pulses caused by the bombardment of the MNP 102 by molecules in the solution. The freely diffused MNP 102 can be considered as an estimate of the response time and sampling rate of the magnetic sensor 105, which can detect the motion of the MNP 102. For the case where the MNP 102 is bound to the surface of the magnetic sensor 105 by a long, flexible polymer (e.g., biopolymer 101), the freely diffused MNP 102 is a good first estimate. It is assumed that the polymer length is much longer than the size of the sensing region 206. This constraint increases the probability of detection by preventing the MNP 102 from diffusing too far away from the magnetic sensor 105 (e.g., away from the sensing region 206 for an extended time period), but does not otherwise constrain its motion, which can still be considered simple Brownian motion.

[0087] The random movement of particles in a fluid due to collisions with the fluid's molecules can be mathematically described by solving the Langevin equations. Equations of motion with velocity damping terms explain velocity or friction. The mean square displacement (MSD) of particles on short timescales is given by: Where k B Here, is the Boltzmann constant, T is the temperature, m is the particle mass, and t is the observation time. This essentially describes the state of approximately under thermal dynamic equilibrium. The average velocity of a free particle's motion. k B The value of T at room temperature (RT) (298K) is 4.11 × 10⁻⁶. -21 J, and the exemplary MNP 102 of iron oxide has about 5 g / cm³ 3 The density. This makes the mass of a 20nm spherical particle approximately 2 × 10⁻⁶. -20 kg, thus giving an average particle velocity of approximately 0.8 m / s. This is much larger than the visually observable velocity of colloidal nanoparticles of this size. Only particles with sub-nanometer spatial resolution and an average drag force imparted by the surrounding liquid can be used with a relaxation time (τ). B This velocity is measured using instruments with limited response times. The initial velocity of the particle will change with... The decrease and relaxation time are related to the fluid viscosity (η) in the following way: Where 'a' is the particle radius. (Alternative water velocity at room temperature) This produces a relaxation time of approximately 0.1 ns, which is lower than the response time of video imaging systems but within the range of some magnetic sensors 105. On longer timescales (t >> τ)... B Under these conditions, the particle MSD increases linearly over time: This describes random diffusion due to collisions with water molecules. D is the microscopic diffusion coefficient derived from the Stokes-Einstein equations. Brownian motion of 20 nm iron oxide MNP 102. It is quite fast (approximately 0.25 mm / s), and the particles will take an average of about 0.2 ms to diffuse over an effective sensing area of ​​approximately 100 × 130 nm. This is well within the range of properly designed commercial magnetic sensors 105 that can operate in gigahertz configurations, for example, where the response time is in nanometers.

[0088] The response of the magnetic sensor 105 to the motion of strictly confined nanoparticles (e.g., chain length ≈ sensing region 206 of magnetic sensor 105 ≈ MNP 102 size) is quite difficult to interpret. The MNP 102 diffuses only regionally within the sensing region 206, and its apparent diffusion coefficient (free diffusion equivalent) is significantly affected by time averaging. The arriving signal pulses (e.g., of sensor signal 207) generated by the motion of the MNP 102 are neither discrete nor well-defined. The motion of the MNP 102 generates another random noise source that adds to the intrinsic noise of the magnetic sensor 105 and alters the noise characteristics of the detected sensor signal 207. To detect changes in the motion of the MNP 102, the difference between the signal spectrum and the noise spectrum within the sensing bandwidth can be utilized, as further described below. Various advanced sensing schemes, such as energy detection or autocorrelation, have been developed and implemented as described below to improve detection under low signal-to-noise ratio (SNR) conditions.

[0089] The physical problem can be defined as helping to understand how the presence and location of MNP 102 affect magnetic sensor 105. Figure 10A An example model is presented. MNP 102 is attached to the surface of magnetic sensor 105 via a tether. (It should be understood, and further explained elsewhere herein, that the surface of magnetic sensor 105 itself may be physically separated from the tether (e.g., biopolymer 101), MNP 102, and any fluid acting on MNP 102 by some protective barrier (e.g., an insulator). It should be understood that when this document refers to "the surface of magnetic sensor 105," it is for simplicity, and the surface of magnetic sensor 105 may not be exposed but is physically nearby.) For example, the tether (biopolymer 101) may include, for instance, such as... Figure 10A The example shown is polyethylene glycol / biotin / avidin. When molecules in the surrounding solution collide with MNP 102, MNP 102 moves via random Brownian perturbations. This motion can be approximated as a one-dimensional harmonic potential. Specifically, as... Figure 10A As shown, MNP 102 can be considered as a mass on a spring (e.g., biopolymer 101). Neglecting gravity, the driving force resulting from collisions between the molecules of the surrounding solution and MNP 102 is Brownian and random. The Brownian driving force varies with the diameter of MNP 102 and the temperature in Kjeldahl terms, and can be expressed as... The spring restoring force and the fluid damping force (both of which are deterministic) counteract the driving force. The spring restoring force can be expressed as... Where K is the spring constant of the molecular chain (e.g., biopolymer 101), and x is the position of MNP 102. The deterministic fluid damping force can be expressed as... Where η is the dynamic velocity of the surrounding liquid (for water at room temperature, it is approximately...). ), and d is the diameter of MNP 102.

[0090] The one-dimensional time evolution of the probability distribution P of the diffuse spherical particle at position x and time t (given the initial position x0 at time t0 in the harmonic potential field) is given by the equation of motion:

[0091]

[0092] It has the following solution

[0093]

[0094] in And the relaxation time τ is In the power spectral density (PSD), the relaxation time is related to the so-called corner frequency f in this paper. c Related to f c = 1 / πτ. Therefore, the corner frequency can be approximated as 1 / πτ.

[0095]

[0096] Figure 10B This is a copy of Figure 1 from the paper by M. Lindner et al. entitled "Dynamic analysis of a diffusing particle in a trapping potential". (See M. Lindner et al., "Dynamic analysis of adiffusing particle in a trapping potential", Physical Review E 87, 022716 (2013).) Figure 10B This is a graphical representation of a single particle diffusing with a harmonic potential imposed by the DNA strand. The upper panel shows two constructs, and the lower panel shows the Boltzmann steady-state distribution and probability distribution for values ​​Δt≡(t-t0) at x0 = -650 nm for 0.01τ, 0.1τ, and 10τ. Therefore, the lower panel provides the probability that MNP 102 will occupy a specific location at a given time.

[0097] To illustrate how the presence and movement of MNP 102 affect the sensor signal 207 provided by the magnetic sensor 105, we first consider a thought experiment using optical methods, such as... Figure 11A The diagram illustrates this. Assume MNP 102 has a diameter of 20 nm and is bonded to the surface of the device via a chain (e.g., polyethylene glycol / biotin / avidin). Further assume the existence of a light source capable of generating light with a wavelength comparable to the diameter of MNP 102, and that photodiode 502 detects photons reflected in a specific direction by MNP 102 bonded to the surface of the device. If MNP 102 is stationary and illuminated by the light source, the intensity of the reflected light will remain constant over time. Therefore, the PSD of photodiode 502 signal 505 will provide an indication of the noise contributed by photodiode 502. In other words, as long as MNP 102 does not move, the noise in the photodiode 502 signal will be entirely due to the characteristics of photodiode 502. Assuming the noise substrate of photodiode 502 is white (e.g., thermal noise or Johnson-Nyquist noise), the noise spectrum is approximately flat at a certain low level, as shown by... Figure 11B The short dashed line in the diagram illustrates this. When MNP 102 is allowed to move, a random perturbation causes MNP 102 to move through restricted Brownian motion (because the tether prevents it from drifting away). The PSD of the restricted Brownian motion is a Lorentz function, which has a PSD of the following form.

[0098]

[0099] As explained above, the corner frequency Refer again Figure 11B When MNP 102 is allowed to move in restricted Brownian motion, the overall PSD of photodiode 502 signal 505 is the sum of the white noise of photodiode 502 itself and the Lorentz function due to the restricted Brownian motion of MNP 102. The overall noise PSD has a lower frequency shoulder (corner frequency) of approximately 10 kHz and a higher frequency shoulder of approximately 300 kHz, where the noise floor of photodiode 502 begins to dominate the overall noise PSD.

[0100] Since it is known that the PSD of restricted Brownian motion (which can be regarded as a characteristic) is a Lorentz function, the expected PSD of the sensor signal 207 from the magnetic sensor 105 in the absence of the moving MNP 102 and in the presence of the moving MNP 102 can be determined in a similar manner by first considering the noise PSD of the magnetic sensor 105 which does not have any MNP 102 nearby, and then evaluating the effect of the MNP 102 on that noise PSD. Figure 12A The diagram illustrates the similarities to those previously discussed. Figure 6 The exemplary magnetic sensor 105, with a configuration similar to that described in the discussion, is also mentioned. Figure 12A Shown in Figure 6 The explanation of the components is applicable Figure 12A And it will not be repeated.

[0101] The noise PSD of the perfect MTJ exhibits 1 / f behavior (which is reduced by 10 dB / tenfold). Figure 12B The plots show the expected noise PSD of the exemplary magnetic sensor 105 for a perfect MTJ driven by the selected bias voltage (discussed further below) and the Lorentz function characterizing the PSD of the MNP 102 after restricted Brownian motion. Figure 12B In this example, the Lorentz function exceeds the noise PSD of the magnetic sensor 105 in the frequency range between approximately 2 kHz and approximately 70 kHz. Therefore, on a heavy logarithmic scale, the overall PSD in this frequency range has a discernible "bulge" labeled 140. Thus, if the magnetic sensor 105 is sensitive to the presence of MNP 102, that sensitivity will manifest as a discernible bulge 140 in the PSD of the sensor signal 207. As discussed further below, whether and in what frequency range the Lorentz function exceeds the noise PSD of the magnetic sensor 105 depends on various factors, including the design of the magnetic sensor 105 and the bias voltage (or current) used to drive it, as well as the factors discussed above that determine the corner frequency of the Lorentz function (e.g., the spring constant of the molecular chain, the diameter of MNP 102, the dynamic velocity of the liquid surrounding MNP 102).

[0102] To verify the theoretical analysis presented above, the inventors used a magnetic sensor 105 in the form of an MTJ to perform experiments to determine whether the PSD of the collected sensor signal 207 actually exhibits the behavior derived above. Figure 13 This is a graphical illustration of the experiment. First, as shown in the leftmost panel, an external magnetic field is applied, and sensor signal 207 is captured to determine the noise PSD of magnetic sensor 105 in the absence of any MNP 102 (ideally having a 1 / f variation curve as described above). Next, the external magnetic field is turned off, and MNP 102 (20 nm diameter) is tied to surface 117 using polyethylene glycol / biotin / avidin as described above. A bias voltage is applied to magnetic sensor 105, resulting in a magnetic field near magnetic sensor 105. In response to this magnetic field, the magnetization of MNP 102 is oriented to align itself with the magnetic field and then moves in a constrained Brownian motion, as described above. Sensor signal 207 is captured as MNP 102 moves to capture the dipole interaction between the magnetic moment of magnetic sensor 105 and the magnetic moment 261 of free layer 260 of magnetic sensor 105, as described above. Figure 13 The center and far right panels are illustrated with graphics.

[0103] Figure 14 The diagram illustrates the measured PSDs of the three tested magnetic sensors 105. Each dashed line with a circle (labeled 161) represents the noise PSD of one of the tested magnetic sensors 105 (without any MNP 102), and each solid line with a diamond shape (labeled 162) represents the combined PSD of MNP 102 and magnetic sensor 105. Figure 14 As shown in the graph, each component of the combined PSD exhibits a characteristic bulge of 140° when MNP 102 is detected. Therefore, experiments confirm that, for a bias voltage of approximately 10 mV, the bounded MNP 102 behaves like a particle confined within a harmonic potential. Furthermore, its PSD can be represented by the Lorentz function in the range of approximately 488 Hz to 120 kHz, as... Figure 14 As shown in the image. Figure 14 As indicated, the corner frequency of each of the Lorentz functions is slightly different for the different magnetic sensors 105, but all corner frequencies are approximately 45 kHz. Although Figure 14 Data from only three exemplary magnetic sensors 105 are presented, but the other tested magnetic sensors 105 behave similarly. In all experiments, the corner frequency of the Lorentz function of the restricted Brownian motion of MNP 102 was found to be approximately 45 kHz.

[0104] As explained above, the corner frequency depends on the chosen chain system (e.g., biopolymer 101) and specifically on its spring constant. A polymer chain can be viewed as an "entropy" spring, as described by P.G. de Gennes in "Scaling Concepts in Polymer Physics" (Cornell University Press, Ithaca, 1979). Stretching or compressing the coil away from its equilibrium size reduces the number of possible configurations and thus reduces entropy. Consequently, the free energy increases. The free energy is the square of the change in chain size, and the spring constant is given by the following equation:

[0105]

[0106] Where R is the size of the coil, T is the temperature, and k B This is the Boltzmann constant. In some embodiments, it is desirable to use both soft and short molecular chains to hold the MNP 102 within the sensing region 206 of the magnetic sensor 105, which also makes the corner frequency (and thus the sampling rate and associated modulus / number complexity of the system) reasonable for a small MNP 102. In addition to the previously described polyethylene glycol / biotin / avidin chains, RNA, neutrophilic microvilli, PEG... 3300 PEG 6260 Poly(styrene) is suitable for all instances of chaining.

[0107] As stated above, when MNP 102 is present, the bias voltage applied to the magnetic sensor 105 affects whether and to what extent the characteristic bulge 140 in the overall PSD is significant in the measured sensor signal 207. In order to detect the presence and movement of MNP 102, it is desirable to find a noise PSD that can be added to the magnetic sensor 105 to generate the Lorentz function of the detected overall PSD. Figure 15A , 15B The diagrams 15C, 15D, and 15E illustrate the results of experiments conducted to investigate the effect of bias voltage on this procedure. Figure 15A The results are shown when the bias voltage is 11mV; Figure 15B The results are shown when the bias voltage is 25mV; Figure 15C The results are shown when the bias voltage is 50mV; Figure 15D The results are shown when the bias voltage is 75mV; and Figure 15E The results are shown when the bias voltage is 100mV.

[0108] like Figure 15A , 15B A comparison between 15C, 15D, and 15E indicates that at higher bias voltages, fitting the Lorentz function representing the confined Brownian motion of MNP 102 to the measured data becomes increasingly difficult. Using higher bias voltages may trigger superdiffusion, in which case the motion of MNP 102 will no longer be confined Brownian motion, but driven motion (e.g., MNP 102 will be affected by an additional force and will move faster than it would in confined Brownian motion). Superdiffusion can result if the magnetic sensor 105 influences (drives) the motion of MNP 102 rather than merely observes it. The result of higher bias voltages is a slope greater than 2 at the high-frequency tail of the overall PSD, a characteristic of superdiffusion. In the inventors' experiments, it was found that for higher bias voltages, the PSD of MNP 102 cannot be represented by the Lorentz function but by the following function:

[0109]

[0110] Where β is a value greater than 2. The figures show the values ​​for... Figure 15A , 15B The value β of the bias voltage in 15C, 15D, and 15E. In other words, Figure 15A , 15B The experimental results presented in 15C, 15D and 15E indicate that the system becomes nonlinear and unpredictable when the bias voltage is too large.

[0111] To adjust the mathematical model to account for superdiffusion, the one-dimensional harmonic potential approximation derived above can be modified to include a component representing the magnetic force caused by the bias voltage of the magnetic sensor 105. Figure 16 The diagram illustrates how the model can be modified to include components arising from the motion of MNP 102 influenced by the magnetic sensor 105. Again, MNP 102 is considered as a mass on a spring, which is a chain (e.g., biopolymer 101). The Brownian driving force, the fluid damping force, and the spring restoring force are identical, as in... Figure 10A As shown in the diagram and described in the discussion of that diagram above. Besides those forces, Figure 16 The model also incorporates a magnetic force caused by the magnetic sensor 105, which is represented as

[0112]

[0113] in It is the magnetic moment of MNP 102, and This is the magnetic field at position MNP 102. The one-dimensional time evolution of the probability distribution P of the diffusing spherical particle at position x and time t (given its initial position x0 in the harmonic potential field within the magnetic field gradient) is given by the equations of motion:

[0114]

[0115] This equation does not have a known analytical solution. Therefore, the relationship between the hydrodynamic radius and the corner frequency is unknown in these cases.

[0116] To avoid the onset of overdiffusion and to allow MNP 102 to move in confined Brownian motion without substantially affecting the motion of magnetic sensor 105, the bias voltage of magnetic sensor 105 should be kept sufficiently low such that the characteristic bulge 140 due to the presence of MNP 102 exists in the overall PSD and can be fitted with a Lorentz function representing the confined Brownian motion of MNP 102 as described above. In other words, if it is not possible to fit the measured PSD data to the Lorentz function, then the bias voltage used to drive magnetic sensor 105 may be too high and may not need to be reduced.

[0117] Although the above discussion focuses primarily on the MTJ sensor and provides some explanation of the SV sensor, it should be understood that the magnetic sensor 105 can be any type of magnetic sensor. The use of the MTJ in the experiment and as an example is not intended to be limiting. Suitable magnetic sensors 105 include, but are not limited to, large magnetoresistive (GMR) sensors, Hall effect devices, spin valves, and spin accumulation sensors. Generally, the magnetic sensor 105 can be any magnetic sensor that allows the detection of the presence / absence and / or movement of the MNP 102 based on the sensor signal 207.

[0118] Additional working instances

[0119] To demonstrate the feasibility and implementation of the dynamic spectrum biosensing technology described herein, a magnetic sensor 105 located in a flow cell was used to monitor structural changes in an exemplary biopolymer 101 (ssDNA) caused by alterations in the ionic strength of the buffer solution.

[0120] exist Figure 17A , 17B And 17C schematically illustrates the three stages of the experiment. First, as Figure 17A The diagram illustrates that, firstly, a copper-catalyzed azide-alkyne click chemistry process is used to attach the 5' end of a 150 nucleotide (nt) ssDNA to the surface 117 of the device in the sensing region 206 of the magnetic sensor 105. Then, a 3'-terminated biotinylated 20-mer is hybridized to the 3' end of the ssDNA. Therefore, Figure 17A The diagram illustrates an exemplary 150 nt ssDNA bound to surface 117 near magnetic sensor 105 before MNP 102 is attached. The ssDNA binds to surface 117 near magnetic sensor 105, allowing magnetic sensor 105 to detect MNP 102 bound to the other end of the ssDNA. In the experiment, then in a direction perpendicular to the exposed surface of magnetic sensor 105 (along...) Figure 17A A uniform 15 Oersted external magnetic field is applied along the z-axis in both positive and negative directions, and sensor signals 207 are recorded in the absence of any MNP 102.

[0121] Next, the avidin-coated 20nm MNP 102 was attached to the end of the ssDNA lineage (biopolymer 101). Figure 17B The diagram illustrates the ssDNA lineage of 20-m MNP 102 with avidin coating. (Example) Figure 17B As shown, a 20-nm MNP 102 is ligated near the magnetic sensor 105 (e.g., typically within its sensing region 206). The MNP 102 is coated with avidin to allow it to bind tightly to the ssDNA line. Arrows overlaid on the MNP 102 indicate the degree of random movement of the MNP 102. Sensor signals 207 are recorded in 10 mM Tris buffer.

[0122] Add (for example) Mg 2+ Ions cause ssDNA compression. Therefore, the addition of Mg... 2+ Following the addition of ions, the restricted random motion of MNP 102 attached to ssDNA should become decayed. (Similar behavior has been observed in TPM based on polyuridine (U) messenger (m)RNA.) Therefore, magnesium ions are added to the solution in the test. Figure 17C The diagram illustrates the exemplary state after the addition of magnesium ions and subsequent compression of the ssDNA lineage. (Relative to...) Figure 17B The random motion of MNP 102 decays, as indicated by the shorter arrows overlaid on MNP 102. Sensor signal 207 was recorded in 15 mM Tris-MgCl2 buffer.

[0123] Despite the above text Figure 17A , 17B The description in section 17C uses only one MNP 102 and only one magnetic sensor 105, but an array using magnetic sensor 105, multiple MNPs 102, and multiple ssDNA fragments (biopolymer 101) was tested. In this test, the density of ssDNA immobilized on the surface of the flow cell was not controlled, and a particular observed MNP 102 may have been attached to the surface by more than one DNA strand. (See below for examples) Figure 19A , 19B (A single-molecule system for mitigating or eliminating this probability is described in the context of 19C, 19D, and 19E.) Therefore, the density of the attached MNPs 102 was adjusted to ensure the presence of a magnetic sensor 105, wherein one or only a few MNPs 102 were attached near the magnetic sensor 105 to ensure that only one MNP 102 was present within the sensing region 206. Several such magnetic sensors 105 were identified, and the recorded sensor signals 207 of those magnetic sensors 105 were sampled at a moderate sampling rate of 6 kHz. Figure 18A , 18B And 18C presents the recorded sensor signals 207 and corresponding autocorrelation functions for two representative magnetic sensors 105 of this kind.

[0124] Figure 18A The illustration depicts exemplary current fluctuations (e.g., sensor signals 207) recorded over a two-second period by two distinct exemplary magnetic sensors 105, designated "Sensor 1" and "Sensor 2," after 150 nt ssDNA (e.g., biopolymer 101, respectively) has been attached (fixed) to surface 117 near each of the two magnetic sensors 105 in the presence of an applied external magnetic field H, but before the attachment of any MNP 102. Figure 18A The topmost (non-curved) section of the diagram illustrates the described state. In other words, in Figure 17A The phases depicted, shown by the intensity versus time graph, show the recorded current fluctuations of each of the two magnetic sensors 105 as the background or baseline sensor signal 207 for both magnetic sensors 105 (sensor 1 and sensor 2). Also for each of sensor 1 and sensor 2... Figure 18A The graph shows the positive and negative autocorrelation functions of the measured sensor signal 207. The smooth, short dashed curve in each autocorrelation graph is the average autocorrelation of the sensor signal 207 measured at the corresponding baseline.

[0125] Figure 18B The diagram illustrates the measured sensor signals 207 (intensity versus time) of sensors 1 and 2 and their autocorrelation functions after attaching MNP 102 (in the test, these are the corresponding 20 nm Fe3O4 particles attached to the ends of each of the DNA strands) and after adding Tris buffer. Figure 18B Provided when ssDNA is in its elongated form and corresponding to Figure 17B The results of the stages described in the text. Figure 18B The topmost (non-curved) section of the diagram illustrates the aforementioned stage. The introduction of MNP 102 will cause both the recorded current fluctuations and the autocorrelation function in the corresponding sensor signal 207 to change relative to... Figure 18A And change. For example, as Figure 18A Instructions and Figure 18B The comparison indicates that the positive and negative autocorrelation functions of sensor 1 shift upward relative to the baseline sensor signal 207 within a lag time between approximately 1 ms and 200 to 300 ms, while the autocorrelation function of sensor 2 generally shifts downward relative to the baseline sensor signal 207 within a lag time between approximately 1 ms and approximately 50 ms. Therefore, it is possible to determine the autocorrelation function based on the comparison with... Figure 18A The shift inference in the autocorrelation function of the baseline (when MNP 102 is not present) is within the sensing region 206 where MNP 102 is present.

[0126] Figure 18C Diagram illustrating the effect of introducing Mg 2+ The measured sensor signals 207 (intensity versus time) and their autocorrelation functions of sensors 1 and 2 when ions compress DNA strands (e.g., biopolymer 101). In other words, Figure 18C Corresponding to Figure 17C The stages described in the text. Figure 18C The topmost (non-curved) section of the diagram illustrates the described stage. Figure 18B autocorrelation function and Figure 18C and / or Figure 18A Comparison of autocorrelation functions reveals that structural changes can be detected within the autocorrelation function. For example, regarding... Figure 18B Regarding the autocorrelation function exhibited by sensor 1, both positive and negative autocorrelation functions after adding Mg 2+ The ion then shifts slightly downwards within a lag time between 1 ms and approximately 60 to 70 ms, and also maintains a position closer to the average autocorrelation function within a lag time greater than approximately 300 ms. Similarly, regarding... Figure 18B Regarding the autocorrelation function shown by sensor 2, the addition of Mg... 2+ The structural changes in ssDNA caused by ions manifest as a downward shift in both positive and negative autocorrelation functions within a lag time between approximately 1 ms and approximately 50 ms, and an upward shift within a lag time of approximately 200 to 300 ms. Therefore, as from... Figure 18A , 18B As illustrated in Figure 18C, a significant change in the noise autocorrelation function can be observed between the three states, thereby allowing the presence / absence and motion of the MNP 102 to be detected and / or monitored within the sensing areas 206 of sensors 1 and 2.

[0127] Figure 18A , 18B The results described and demonstrated in 18C confirm that the magnetic sensor 105 can detect not only changes in the average equilibrium position of the MNP 102, but also small, reversible changes in noise fluctuations caused by single-molecule processes. Billions of such magnetic sensors 105 with single-molecule sensitivity could be integrated onto CMOS platforms (e.g., similar to Toshiba's 4-Gbit density STT-MRAM chips) to create next-generation high-throughput systems for diagnostics and drug discovery, leveraging existing mature technologies and high-volume manufacturing capabilities developed by the semiconductor and data storage industries.

[0128] The coupling between the pinned and free layers of the specific magnetic sensor 105 tested is suitable for biosensing, as indicated by the experiments described herein. These magnetic sensors 105 are one example of suitable magnetic sensors 105. Other magnetic sensors 105 with coupling between FM1 and FM2 (optimized for biosensing applications or for a specific class of MNP 102) may also be used, and these other magnetic sensors 105 may perform better than the exemplary magnetic recording sensor used in the experiments.

[0129] Monitoring devices and systems

[0130] As further described below, in some embodiments, a system 100 for monitoring the movement of an MNP 102 coupled to a biopolymer 101 may include a fluid chamber 115, at least one processor 130, and a magnetic sensor 105. The fluid chamber includes binding sites 116 configured to attach one end of the biopolymer 101 to the surface of the fluid chamber 115 and allow the MNP 102 to move (e.g., due to molecular bombardment by the surrounding fluid). Binding sites 116 may include structures (e.g., cavities or ridges) configured to anchor the biopolymer 101 to the binding sites 116.

[0131] The magnetic sensor 105 may include, for example, an MTJ or an STO. The magnetic sensor 105 has a sensing region 206 within a fluid chamber 115, in which it can detect MNP 102. The sensing region 206 may have, for example, a depth between approximately 10... 5 nm 3 With approximately 5×10 5 nm 3 The volume between. The sensing region 206 includes binding sites 116. The magnetic sensor 105 is configured to generate a sensor signal 207 characterizing the magnetic environment within the sensing region 206 (e.g., the presence, absence, and / or location of MNP 102) and to provide the sensor signal 207 to at least one processor 130. The sensor signal 207 may convey (e.g., report) one or more of the following: current, voltage, resistance, noise (e.g., frequency noise or phase noise), frequency or frequency (e.g., oscillation frequency or Lorentz corner angular frequency).

[0132] In some embodiments, at least one processor 130 is configured to execute machine-executable instructions that allow it to: (a) acquire a first portion of a sensor signal 207 representing the magnetic environment within the sensing region 206 during a first detection cycle; (b) acquire a second portion of the sensor signal 207 representing the magnetic environment within the sensing region 206 during a second detection cycle following the first detection cycle; and (c) analyze the first and second portions of the sensor signal 207 to detect motion of the connected MNP 102. For example, as further described below, at least one processor 130 may determine a first autocorrelation function of the first portion of the signal, determine a second autocorrelation function of the second portion of the signal, and analyze the first and second autocorrelation functions (e.g., compare the first and second autocorrelation functions) to detect motion of the connected MNP 102. At least one processor 130 may process the sensor signal 207 or portions thereof in the time domain, frequency domain, or both. In some embodiments, at least one processor 130 is configured to determine a Lorentz function characterizing the restricted Brownian motion of the MNP 102.

[0133] System 100 may further include a detection circuitry system 120 coupled to the magnetic sensor 105 and to at least one processor 130. For example, circuitry system 120 may include one or more lines that allow at least one processor 130 to read or interrogate the magnetic sensor 105. Circuitry system 120 may include components such as analog-to-digital converters and / or amplifiers.

[0134] In some embodiments, the monitoring system 100 includes a plurality of magnetic sensors 105, each functionalized with an individual single biomolecule in use, such that the monitoring system 100 is able to detect single-molecule processes at each magnetic sensor 105. Figure 19A This is a block diagram illustrating components of an exemplary monitoring system 100 according to some embodiments. As illustrated, the exemplary monitoring system 100 includes a sensor array 110 coupled to a circuit system 120, which is coupled to at least one processor 130. The sensor array 110 includes a plurality of magnetic sensors 105 that can be arranged in any suitable manner, as further described below. (It should be understood that the sensor array 110 includes at least one magnetic sensor 105.)

[0135] Circuit system 120 may include (for example) one or more lines that allow magnetic sensors 105 in sensor array 110 to be queried by at least one processor 130 (e.g., by means of other components well known in the art, such as current or voltage sources, amplifiers, analog-to-digital converters, etc.). For example, in operation, processor 130 may cause circuit system 120 to apply a bias voltage or current to such lines to detect sensor signal 207 that reports the magnetic environment of at least one magnetic sensor 105 in sensor array 110. Sensor signal 207 indicates the presence, absence, position, and / or movement of MNP 102 within sensing area 206. In other words, sensor signal 207 indicates a characteristic of magnetic sensor 105 (e.g., magnetic field, resistance, voltage, current, oscillation frequency, signal level, noise level, frequency noise, phase noise, etc.). Sensor signal 207 may be examined and / or processed to determine whether magnetic sensor 105 has detected MNP 102 over time or whether the movement (e.g., change of position) of MNP 102 has occurred. For example, at least one processor 130 can monitor one or more time-domain, frequency-domain, deterministic, and / or statistical properties of the sensor signal 207 (e.g., peak or average amplitude, fluctuation, shift from average or expected peak, autocorrelation, power spectral density, etc.) and determine whether MNP 102 or movement of MNP 102 is detected (or not detected). As a specific example, at least one processor 130 can compare the form of the sensor signal 207 of the magnetic sensor 105 at a selected time or within a selected time period (e.g., autocorrelation, PSD, etc.) with the form of the sensor signal 207 at an earlier time or within an earlier or different time period (e.g., baseline autocorrelation, as described above in the section on...). Figure 17A , 17B And as described in 17C or as follows in the text, for example Figure 21-26 The processor 130 determines whether the MNP 102 is detected or has moved based on changes in the sensor signal 207, using the baseline noise PSD described in the discussion. For example, at least one processor 130 may determine a first overall noise PSD of the sensor signal 207 during a first detection cycle and a second overall noise PSD of the sensor signal 207 during a second detection cycle, and analyze whether the MNP 102 exists and / or has moved. In some embodiments, at least one processor 130 determines a Lorentz function that generates the overall noise PSD of the sensor signal 207 during one or both of the first and second detection cycles when added to the baseline noise PSD of the magnetic sensor 105.

[0136] The sensor signal 207 and the information it conveys characterizing the magnetic environment of the magnetic sensor 105 may depend on the type of magnetic sensor 105 used in the monitoring system 100. In some embodiments, the magnetic sensor 105 is a magnetoresistive (MR) sensor (e.g., MTJ, SV, etc.) capable of detecting, for example, magnetic fields or resistance, changes in magnetic fields or resistance, or noise levels. In some embodiments, each of the magnetic sensors 105 in the sensor array 110 is a thin-film device capable of using MR effects to detect MNP 102 attached to a biopolymer 101, which is bound to a corresponding binding site 116 associated with the magnetic sensor 105. The magnetic sensor 105 may operate as a potentiometer having resistance that varies with the strength and / or direction of the sensed magnetic field. In some embodiments, the magnetic sensor 105 includes a magnetic oscillator (e.g., STO), and the sensor signal 207 reports the frequency or frequency change, frequency noise, or phase noise generated by the magnetic oscillator.

[0137] In some embodiments, at least one processor 130, with the aid of circuitry 120, detects deviations or fluctuations in the magnetic environment of some or all of the magnetic sensors 105 in the sensor array 110. For example, an MR-type magnetic sensor 105 in the absence of an MNP 102 should have relatively low noise above a certain frequency compared to a magnetic sensor 105 in the presence of an MNP 102, because field fluctuations from the MNP 102 will cause fluctuations in the torque sensed by the ferromagnetic material. These fluctuations can be measured, for example, using heterodyne detection (e.g., by measuring noise power density) or by directly measuring the current or voltage of the magnetic sensor 105, and evaluated using comparator circuitry for comparison with another sensor element that does not sense the bonding site 116. In some embodiments, the magnetic sensor 105 includes an STO element, and the fluctuating magnetic field from the MNP 102 causes phase jumps in the magnetic sensor 105 due to instantaneous frequency changes (which can be detected using phase detection circuitry).

[0138] It should be understood that the examples of MNP 102 and magnetic sensor 105 provided herein are merely exemplary. In general, any type of MNP 102 that can be attached to biopolymer 101 can be used together with an array 110 of any type of magnetic sensor 105 capable of detecting that type of MNP 102.

[0139] It should also be understood that the components of the monitoring system 100 may be distributed or may be contained within a single physical device. For example, if at least one processor 130 contains more than one processor, then the first processor may be part of a device (e.g., a chip) containing at least one magnetic sensor 105, and the second processor may be located in a different physical location (e.g., outside the chip in a connected computer). As a specific example, the first processor within the monitoring system 100 may be configured to retrieve sensor signal 207 from the magnetic sensor 105, and the second processor within the monitoring system 100 (not necessarily part of the same physical device as the first processor) may process the sensor signal 207 (e.g., calculate the autocorrelation function, PSD, Lorentz function, etc., and / or perform signal processing and / or analysis, etc.) to detect the presence / absence and / or motion of the MNP 102. Therefore, Figure 19A The components illustrated herein can be co-located or distributed. Alternatively, the system can be comprised of components in a single physical device. Figure 19A The components illustrated in the diagram, or Figure 19A The components can be distributed. Similarly, the monitoring system 100 may include other components, such as (for example) a memory for storing sensor signal 207 or a sampled or processed version of sensor signal 207, or instructions for execution by at least one processor 130, and others.

[0140] Figure 19B , 19C The diagram 19D illustrates various parts of an exemplary monitoring system 100 for detecting and monitoring single-molecule processes according to some embodiments. Figure 19B This is a top view of part of the monitoring system 100. Figure 19C It is by the Figure 19B A cross-sectional view at the location indicated by the long dashed line marked "19C", and Figure 19D It is by the Figure 19B A cross-sectional view at the location indicated by the long dashed line marked "19D".

[0141] Figure 19B , 19C An exemplary portion of the monitoring system 100 shown in Figure 19D includes a sensor array 110 for sensing an MNP 102 within a fluid chamber 115 of the monitoring system 100. The sensor array 110 comprises a plurality of magnetic sensors 105, wherein... Figure 19B Sixteen magnetic sensors 105 are shown in array 110. It should be understood that implementations of the monitoring system 100 may include any number of magnetic sensors 105 (e.g., as few as one, or hundreds, thousands, millions, or even billions of magnetic sensors 105). To avoid obscuring the diagram, in Figure 19B The document refers to only seven magnetic sensors 105, namely magnetic sensors 105A, 105B, 105C, 105D, 105E, 105F, and 105G. (For simplicity, this document generally refers to magnetic sensors 105 by the component symbol 105. Individual magnetic sensors 105 are assigned the component symbol 105 followed by a letter.) As explained above, magnetic sensors 105 can detect the presence or absence of MNP 102 and the movement of MNP 102 within their respective sensing areas 206. In other words, each of the magnetic sensors 105 can detect the presence of MNP 102 in its vicinity (e.g., within sensing area 206), and the sensor signal 207 provided by the magnetic sensor 105 also provides an indication of whether and how MNP 102 has moved.

[0142] Now for reference Figure 19C and 19D Together Figure 19B Each magnetic sensor 105 in the exemplary embodiment of the monitoring system 100 is illustrated as having a cylindrical shape. However, it should be understood that the magnetic sensor 105 can generally have any suitable shape. For example, the magnetic sensor 105 can be cubic in three dimensions. Furthermore, different magnetic sensors 105 can have different shapes (e.g., some can be cubic and others cylindrical, etc.). It should be understood that the illustrations are merely exemplary.

[0143] like Figure 19C and 19D As shown, the monitoring system 100 includes a fluid chamber 115. The fluid chamber 115 includes a plurality of binding sites 116 on a surface 117. The fluid chamber 115 holds a fluid (e.g., a buffer solution, a nucleotide precursor, other fluid, or solution). In the illustrated embodiment, each magnetic sensor 105 is associated with a corresponding binding site 116. (For simplicity, this document generally refers to binding sites by the element symbol 116. Individual binding sites are assigned the element symbol 116 followed by a letter.) In other words, there is a one-to-one relationship between the magnetic sensor 105 and the binding site 116. Figure 19B As shown, magnetic sensor 105A is associated with binding site 116A, magnetic sensor 105B is associated with binding site 116B, magnetic sensor 105C is associated with binding site 116C, magnetic sensor 105D is associated with binding site 116D, magnetic sensor 105E is associated with binding site 116E, magnetic sensor 105F is associated with binding site 116F, and magnetic sensor 105G is associated with binding site 116G. Figure 19B Each of the other unlabeled magnetic sensors 105 shown is also associated with a corresponding binding site 116. Figure 19B , 19C In the exemplary embodiment of 19D, each magnetic sensor 105 is shown positioned below its corresponding binding site 116, but it should be understood that the binding site 116 may be in other locations relative to its corresponding magnetic sensor 105. For example, the binding site 116 may be on the side of its corresponding magnetic sensor 105.

[0144] Each of the binding sites 116 is configured to bind no more than one biopolymer 101 (e.g., ssDNA, RNA, protein, etc.) to a surface 117 within the fluid chamber 115. In other words, each binding site 116 has characteristics and / or features designed to allow one and only one biopolymer 101 to bind to it for sensing and monitoring by a corresponding magnetic sensor 105 (or multiple magnetic sensors 105, as discussed below), thereby making the system 100 a single-molecule system. The corresponding magnetic sensor 105 can then detect and monitor the movement of the MNP 102 of the biopolymer 101 attached to the binding site 116. In some embodiments, the binding site 116 has a structure (or multiple structures) configured to anchor the biopolymer 101 to the binding site 116. For example, the structure (or the structure) may include a cavity or a ridge. Figure 19C and 19D The diagram illustrates the bonding site 116 extending from the surface 117 of the fluid chamber 115, but it should be understood that the bonding site 116 may be flush with or etched into the surface 117 of the fluid chamber 115.

[0145] Binding sites 116 may have any suitable size and shape that facilitates the attachment of one and only one biopolymer 101 to each binding site 116. For example, the shape of binding sites 116 may be similar to or the same as the shape of magnetic sensor 105 (e.g., if magnetic sensor 105 is cylindrical in three dimensions, then binding sites 116 may also be cylindrical, protruding from or forming a fluid container within the surface 117 of fluid chamber 115, having a radius that is larger, smaller, or the same as the radius of the corresponding magnetic sensor 105; if magnetic sensor 105 is cubic in three dimensions, then binding sites 116 may also be cubic, and larger, smaller, or the same as the nearest portion of magnetic sensor 105, etc.). Generally, the binding sites 116 and surfaces 117 of the fluid chamber 115 may have any shape and characteristics that facilitate the attachment of individual biopolymers 101 to each binding site 116 and allow the magnetic sensor 105 to detect the presence and movement of MNPs 102 attached to the biopolymers 101 bound to their respective binding sites 116.

[0146] Figure 19C and 19D The illustration depicts an enclosed fluid chamber 115 having a top portion extending in the xy plane, but does not require the fluid chamber 115 to be enclosed. In some embodiments, the surface 117 of the fluid chamber 115 has properties that protect the sensor 105 from any fluid within the fluid chamber 115 while still allowing the biopolymer 101 to bind to the binding site 116 and allowing the magnetic sensor 105 to detect the MNP 102 of the biopolymer 101 attached to the binding site 116. The material of the fluid chamber 115 (and possibly the binding site 116) may be or include an insulator. In some embodiments, the surface 117 of the fluid chamber 115 comprises an organic polymer, a metal, or a silicate. For example, the surface 117 of the fluid chamber 115 may comprise a metal oxide, silica, polypropylene, gold, glass, or silicon. The thickness of the surface 117 of the fluid chamber 115 may be selected such that the magnetic sensor 105 can detect the MNP 102 of the biopolymer 101 attached to the binding site 116 within the fluid chamber 115. In some embodiments, surface 117 is approximately 3 to 20 nm thick, such that each magnetic sensor 105 is located between approximately 5 nm and approximately 50 nm from any MNP 102 attached to the biopolymer 101 bound to the corresponding binding site 116. It should be understood that these values ​​are merely exemplary. It will be appreciated that embodiments may include a fluid chamber 115 having a thicker or thinner surface 117, and that, as explained above, the sensing region 206 may be of any suitable size.

[0147] The circuitry 120 of the monitoring system 100 may include a sensor array 110 or be attached to the sensor array 110 via one or more lines 125. In some embodiments, each magnetic sensor 105 is coupled to at least one line 125. Figure 19B , 19C In the example shown in 19D, monitoring system 100 includes eight lines 125A, 125B, 125C, 125D, 125E, 125F, 125G, and 125H. (For simplicity, this document generally refers to these lines by the component symbol 125. Individual lines are assigned the component symbol 125 followed by a letter.) Figure 19B , 19C In the exemplary embodiment of 19D, several pairs of lines 125 can be used to access (e.g., read or query) individual magnetic sensors 105. Figure 19B , 19C In the exemplary embodiment shown in 19D, each magnetic sensor 105 of the sensor array 110 is coupled to two lines 125. For example, magnetic sensor 105A is coupled to lines 125A and 125H; magnetic sensor 105B is coupled to lines 125B and 125H; magnetic sensor 105C is coupled to lines 125C and 125H; magnetic sensor 105D is coupled to lines 125D and 125H; magnetic sensor 105E is coupled to lines 125D and 125E; magnetic sensor 105F is coupled to lines 125D and 125F; and magnetic sensor 105G is coupled to lines 125D and 125G. Figure 19B , 19C In the exemplary embodiments of 19D, lines 125A, 125B, 125C and 125D are shown residing below the magnetic sensor 105, and lines 125E, 125F, 125G and 125H are shown residing above the magnetic sensor 105. Figure 19C The magnetic sensor 105E is shown in relation to lines 125D and 125E, the magnetic sensor 105F is shown in relation to lines 125D and 125F, the magnetic sensor 105G is shown in relation to lines 125D and 125G, and the magnetic sensor 105D is shown in relation to lines 125D and 125H. Figure 19D The magnetic sensor 105D associated with lines 125D and 125H, the magnetic sensor 105C associated with lines 125C and 125H, the magnetic sensor 105B associated with lines 125B and 125H, and the magnetic sensor 105A associated with lines 125A and 125H are shown.

[0148] Figure 19B , 19C The magnetic sensors 105 of the exemplary monitoring system 100 shown in Figure 19D are arranged in a sensor array 110 with a rectangular pattern. (It should be understood that a square pattern is a special case of a rectangular pattern.) Each of the lines 125 identifies a row or column of the sensor array 110. For example, each of lines 125A, 125B, 125C, and 125D identifies a different row of the sensor array 110, and each of lines 125E, 125F, 125G, and 125H identifies a different column of the sensor array 110. Figure 19C As shown, each of lines 125E, 125F, 125G, and 125H contacts one of the magnetic sensors 105 along its cross-section (i.e., line 125E contacts the top of magnetic sensor 105E, line 125F contacts the top of magnetic sensor 105F, line 125G contacts the top of magnetic sensor 105G, and line 125H contacts the top of magnetic sensor 105D), and line 125D contacts the bottom of each of sensors 105E, 105F, 105G, and 105D. Similarly, and as... Figure 19D As shown, each of lines 125A, 125B, 125C, and 125D contacts the bottom of one of the sensors 105 along its cross-section (i.e., line 125A contacts the bottom of magnetic sensor 105A, line 125B contacts the bottom of magnetic sensor 105B, line 125C contacts the bottom of magnetic sensor 105C, and line 125D contacts the bottom of magnetic sensor 105D), and line 125H contacts the top of each of magnetic sensors 105D, 105C, 105B, and 105A.

[0149] exist Figure 19B The diagram illustrates the magnetic sensor 105 and the various parts of the line 125 connected to the sensor array 110. Figure 19B Dashed lines are used to indicate that the components can be embedded within the monitoring system 100. As explained above, the magnetic sensor 105 can be protected (e.g., by an insulator) from the contents of the fluid chamber 115, which itself can be enclosed. Therefore, it should be understood that the various illustrated components (e.g., line 125, magnetic sensor 105, bonding site 116, etc.) may not be visible in the physical instantiation of the monitoring system 100 (e.g., the components may be embedded in or covered by a protective material, such as an insulator).

[0150] In some embodiments, some or all of the binding sites 116 reside in nanowells or trenches in the line 125 extending beyond the magnetic sensor 105. For example, such as Figure 19D As shown in the example, line 125H may be thinner above magnetic sensor 105 than it is between magnetic sensors 105. For example, line 125H has a first thickness above magnetic sensor 105D, a second larger thickness between magnetic sensors 105D and 105C, and the first thickness above magnetic sensor 105C. This configuration can be advantageously fabricated using conventional thin-film fabrication methods (e.g., by depositing a material, applying a mask to the deposited material, and removing (e.g., by etching) some of the deposited material according to the mask). Both the binding site 116 and (if present) the nanowell can be fabricated using conventional techniques.

[0151] To simplify the explanation, Figure 19B , 19C The illustration in Figure 19D depicts an exemplary monitoring system 100 having only sixteen magnetic sensors 105, only sixteen corresponding binding sites 116, and eight lines 125 in a sensor array 110. It should be understood that the monitoring system 100 may have fewer or more magnetic sensors 105 in the sensor array 110, and therefore may have more or fewer binding sites 116. Similarly, embodiments including lines 125 may have more or fewer lines 125. Generally, any configuration of the magnetic sensors 105, binding sites 116, and circuitry 120 (e.g., including lines 125) that allows the magnetic sensors 105 to detect MNPs 102 attached to biopolymers 101 bound to binding sites 116 can be used. Similarly, any configuration that allows the retrieval of sensor signals 207 from the magnetic sensors 105 or any other mechanism can be used. The examples presented herein are not intended to be limiting.

[0152] Figure 19B , 19C The magnetic sensor 105 shown in 19D is closely adjacent to the binding site 116, and therefore also closely adjacent to the biopolymer 101 and the MNP 102 bound to the binding site 116.

[0153] although Figure 19B , 19C The diagram (19D) illustrates a one-to-one relationship between the magnetic sensors 105 and the binding sites 116, but it should be understood that each binding site 116 can be sensed by more than one magnetic sensor 105. For example, if the monitoring system 100 has more magnetic sensors 105 than binding sites 116, then at least some MNPs 102 can be sensed by multiple magnetic sensors 105 (e.g., to improve the detection accuracy of MNPs 102 and their motion). This approach can improve the SNR by providing observational diversity.

[0154] exist Figure 19B , 19C The exemplary sensor array 110 shown and described in the context of 19D is a rectangular array in which magnetic sensors 105 are arranged in rows and columns. In other words, the plurality of magnetic sensors 105 of sensor array 110 are arranged in a rectangular grid pattern. In some embodiments, adjacent rows and columns of the rectangular grid pattern are equidistant from each other, which results in the magnetic sensors 105 being arranged in a square grid (or lattice) pattern, such as Figure 19E The illustration is as follows. In an embodiment where the magnetic sensors 105 are arranged in a square grid pattern, each magnetic sensor 105 has up to four nearest neighbors. For example, as shown in the diagram... Figure 19E As shown, the magnetic sensor 105A has four nearest neighbors labeled 105B, 105C, 105D, and 105E. The nearest neighbor to sensor 105 is the one furthest away from it, such as... Figure 19E As shown in the diagram. Therefore, each of sensors 105B, 105C, 105D, and 105E is at least 112 distances from the nearest neighbor of magnetic sensor 105A.

[0155] According to some embodiments, the exemplary monitoring system 100 may utilize a high-precision nanoscale fabrication of a nanoscale magnetic sensor 105 capable of detecting individual MNPs 102 in a densely packed manner, as described above in the section on Figure 18A , 18B As described in the discussion of 18C. The size of the functionalized binding site 116 may be similar to (for example) the size of the biopolymer 101 with the MNP 102 attached, such that multiple biopolymers 101 cannot bind to the same binding site 116 or be sensed by the same magnetic sensor 105 (e.g., such that each magnetic sensor 105 detects / senses only one MNP 102). An appropriate value for the nearest neighbor distance 112 can be determined based on the properties of the magnetic sensor 105 (e.g., sensitivity, size, etc.), the properties of the biopolymer 101 that the monitoring system 100 is intended to monitor (e.g., length, flexibility, etc.), and the properties of the MNP 102 used (e.g., size, type, etc.). (This can then be used to determine the size of the sensor array 110 and / or the maximum number of magnetic sensors 105 that can be assembled within a sensor array 110 of a selected size). For example, the combined length of the biopolymers 101 and the size of the MNP 102 to be used can provide physical limitations on how close two magnetic sensors 105 in the sensor array 110 can be located. In some embodiments, the size of the magnetic sensor 105 may be limited by the nanoscale patterning capability of the process used to fabricate the sensor array 110. For example, using techniques available at write time, the size of each magnetic sensor 105 (e.g., the diameter of the sensor 105 in the xy plane, assuming a cylindrical sensor 105) may be approximately 20 nm. Assuming the type of biopolymer 101 to be monitored is ssDNA, and fragments up to 150 nt in length are expected to be monitored, the maximum length of the biopolymer 101 to be sequenced in the elongated state is approximately 50 nm, although the ssDNA structure may vary between elongation and coiling depending on the ionic strength of the buffer solution. Since the MNP 102 participates in a single-molecule reaction, the MNP 102 should have a molecular size. As explained above, the MNP 102 may be, for example, a superparamagnetic nanoparticle, an organometallic compound, or any other functional molecular group that can be detected by the nanoscale magnetic sensor 105.

[0156] As explained above, the exemplary monitoring system 100 can be implemented using magnetic sensors 105 in various configurations. For example, in some embodiments of the monitoring system 100, the magnetic sensors 105 (e.g., MTJs) are arranged in a square lattice with the same geometry as existing cross-point MRAM sensors. As a specific example, a sensor array 110 with a configuration similar to the single Toshiba 4G-bit density STT-MRAM chip first introduced at the International Electron Devices Meeting (IEDM) in 2016 can be used. In this case, each nanometer-scale magnetic sensor 105 or a region immediately adjacent to it can be functionalized to serve as a corresponding binding site 116. The minimum nearest neighbor distance 112 between the magnetic sensors 105 on the Toshiba platform is 90 nm. Assuming that the MNP 102 is a superparamagnetic nanoparticle (e.g., iron oxide, iron platinum ore, etc.), the length of the biopolymer 101 is 150 nt, and the sensor array 110 is a rectangular (e.g., square) array of magnetic tunnel junctions (MTJs) similar to those used in non-volatile data storage applications, then the minimum nearest neighbor distance 112 is a sufficient spacing.

[0157] It should be understood that a grid pattern (e.g., such as...) Figure 19B The arrangement of the magnetic sensors 105 (shown as a square lattice) is one of many possible arrangements. Those skilled in the art will appreciate that other arrangements of the magnetic sensors 105 are also possible and within the scope of this disclosure. For example, the magnetic sensors 105 may be arranged in a hexagonal pattern, in which each magnetic sensor 105 has up to six nearest neighbors, all at a nearest neighbor distance 112. As those skilled in the art will appreciate, sensor packaging limits (e.g., minimum nearest neighbor distance 112) for the monitoring system 100 with the hexagonal arrangement of the binding sites 116 and the magnetic sensors 105 can be derived based on knowledge of the size, shape, and properties of the magnetic sensors 105, the expected length of the biopolymer 101, and the size and type of the MNP 102 to be used.

[0158] Example monitoring methods

[0159] As described above (for example, in the context of...) Figure 17A , 17B (As discussed in the sections on 17C, 18A, 18B and 18C), the magnetic sensor 105 described herein may be used in methods for monitoring single-molecule processes. Figure 20 This is a flowchart of an exemplary method 300 for sensing motion of a connected MNP 102 according to some embodiments. At 302, optionally, the noise PSD of the magnetic sensor 105 is determined in the absence of any MNP 102 in the vicinity of the magnetic sensor 105. As explained above, this step (if performed) establishes a baseline sensor PSD that can be compared with other PSDs to determine the presence of an MNP 102.

[0160] At 304, MNP 102 is coupled to the first end of biopolymer 101 (e.g., nucleic acid, protein, etc.). As explained above, MNP 102 can be any suitable particle, including (for example) superparamagnetic particles and / or particles having a diameter of a few nanometers (e.g., less than about 5 nm). MNP 102 can be of different sizes (e.g., 20 nm). MNP 102 can comprise or be any suitable material detectable by magnetic sensor 105. For example, MNP 102 can be or includes iron oxide (FeO), Fe3O4, or FePt.

[0161] At 306, a second end (the other end) of the biopolymer 101 is coupled to a binding site 116 sensed by the magnetic sensor 105. As described above, the binding site 116 may be located within the fluid chamber 115 of the monitoring system 100. Also as described above, the magnetic sensor 105 may be any suitable sensor. For example, the magnetic sensor 105 may include MTJ or STO.

[0162] At point 308, sensor signal 207 is obtained from magnetic sensor 105 during both the first and second detection cycles. As explained above, sensor signal 207 can be, or indicates (for example), current, voltage, resistance, noise (e.g., frequency noise or phase noise), frequency (e.g., the oscillation frequency of STO), magnetic field, etc. The first and second detection cycles can be partially overlapping time periods, or they can be non-overlapping, in which case a solution (e.g., containing Mg) can be added between the first and second time periods. 2+ Ions (e.g., added to the fluid chamber 115 of the detection device) (e.g., as described above in the section on...) Figure 17B and 17C as well as Figure 18B and 18C (As discussed in the explanation).

[0163] At 310, motion of MNP 102 is detected based on analysis of changes in sensor signal 207 between the first detection period and the second detection period. Changes in sensor signal 207 between the first and second detection periods can be detected (for example) by: obtaining a first autocorrelation of the signal corresponding to a portion of the first detection period; obtaining a second autocorrelation of the signal corresponding to a portion of the second detection period; and identifying at least one difference between the first and second autocorrelation (e.g., by using the method described above in the analysis of…). Figure 18A , 18B (Compared with the autocorrelation function described in 18C). As another example, changes in sensor signal 207 between the first and second detection periods can be detected in part by determining at least one Lorentz function, which generates the PSD of sensor signal 207 during the first and / or second detection periods when noise PSD is added to magnetic sensor 105. Motion of MNP 102 can be determined based on a comparison of the Lorentz function fitted to sensor signal 207 captured during the first detection period and the Lorentz function fitted to sensor signal 207 captured during the second detection period. Processing and / or analysis of sensor signal 207 can be performed in the time domain, frequency domain, or a combination of both. For example, as described above, the autocorrelation function of portions of sensor signal 207 acquired at different times can reveal movement of MNP 102 sensed by magnetic sensor 105. In some cases, time-domain processing may be preferred for this analysis. As another example, as described above, the PSD of sensor signal 207 and / or the PSD fitted to a Lorentz function can be processed, and / or different Lorentz functions can be compared. In some cases, this processing may be more convenient in the frequency domain. As yet another example, if sensor signal 207 conveys a frequency (e.g., the oscillation frequency of the STO of magnetic sensor 105), then frequency domain processing (e.g., after a Fourier transform of the time-domain data) may be preferred. As yet another example, an autocorrelation function can be calculated or determined and said autocorrelation function transformed to the frequency domain for further analysis.

[0164] It will be understood that the steps of method 300 are illustrated in an exemplary order, but at least some of the steps may be performed in a different order. As just one example, step 306 may be performed before step 304 (e.g., as described above in the section on...). Figure 17A , 17B (As described in 17C). It will also be understood that it can be executed in real-time (or near real-time) or at a later time. Figure 20 Specific steps in the illustrated steps are described below. For example, step 302 (if fully performed) may be performed earlier than any of the other steps, or even after all the other steps have been completed (e.g., after MNP 102 has been rinsed off). As another example, one or more signals collected during step 308 may be recorded, and step 310 may be performed on the recorded data. Specifically, the magnetic sensor 105 may be read / interrogated during testing or experimentation, and the collected sensor signals 207 may be recorded in their original form or in another format (e.g., sampled, amplified, normalized, etc.) (e.g., saved to memory). At a later time, one or more processors (e.g., at least one processor 130) may retrieve and process the recorded sensor signals 207 and determine whether and / or when and / or how the magnetic sensor 105, which was moved during testing or experimentation, monitored MNP 102.

[0165] Multiplexed magnetic digital homogenized non-enzyme (HoNon) ELISA

[0166] As explained above, conventional ELISA (analog) readout systems require a large volume of final dilution of the reaction product, necessitating millions of enzyme labels to generate a signal detectable using conventional plate readers. Conventional ELISA sensitivity is limited to the picomolar (e.g., pg / mL) range and above.

[0167] In contrast, single-molecule measurements are inherently digital. Each molecule generates a signal that can be detected and counted. The presence or absence of a measurement signal (1 and 0) is easier to determine than the absolute quantity of the detection signal. The sensitivity of digital ELISA is approximately micromolar (aM) to sub-femtomolar (fM).

[0168] One example of single-molecule digital ELISA technology is Quanterix's Simoa bead-based assay. (See https: / / www.quanterix.com / simoa-technology / , last accessed June 30, 2021.) In Simoa, paramagnetic particles are coupled to antibodies designed to bind to specific targets. These particles are added to the sample. A detection antibody capable of producing fluorescence is then added, with the goal of forming an immune complex consisting of beads, bound proteins, and the detection antibody. If the concentration is low enough, each bead will contain one bound protein or zero bound proteins. The sample is then loaded into an array of numerous micro-traps, each large enough to hold one bead. The data can be analyzed after amplification of the enzyme signal using a fluorescent substrate and fluorescence imaging.

[0169] Both traditional ELISA and digital ELISA are heterogeneous assays involving enzyme signal amplification and multiple time-consuming latency, reaction, and washing steps that typically last for several hours. Homogenization assays allow for measurements via a simple mixing and reading procedure without requiring sample processing through separation or washing steps (which significantly reduces analysis time). However, shorter detection times are often associated with reduced sensitivity and dynamic range.

[0170] It is possible to achieve highly sensitive detection comparable to digital ELISA by leveraging the simplicity of homogeneous assays. For example, homogeneous entropy-driven biomolecular assays (HEBA) enable one-pot catalytically amplified signal generation without the use of enzymes or precise temperature cycling. (See, for example, Donghyuk Kim et al., "Homogeneous Entropy-Driven Amplified Detection of Biomolecular Interactions", ACS Nano, July 2016, 10(8), 7467-75.)

[0171] Digital homogeneous non-enzymatic (HoNon) immunosorbent assays (ELISAs) without signal amplification have been demonstrated. (See, for example, Kenji Akama et al., "Wash-and Amplification-Free Digital Immunoassay Based on Single-Particle Motion Analysis", ACS Nano, Nov 2019, 13(11), 13116-26; Kenji Akama and Hiroyuki Noji, "Multiplexed homogeneous digital immunoassay based on single-particle motion analysis", Lab on a Chip, Vol. 12, 2020; Kenji Akama and Hiroyuki Noji, "Multiparameter single-particle motion analysis for homogeneous digital immunoassay", Lab on a Chip, Vol. 12, 2020.)

[0172] Compared to optical, plasma, and electrochemical biosensors, magnetic biosensors (e.g., magnetic sensor 105 described herein) exhibit low background noise because most biological environments are non-magnetic. The sensor signal 207 is also less affected by the type of sample matrix, thereby allowing for accurate and reliable detection. Therefore, embodiments of the systems (e.g., system 100), apparatus, and methods described herein can be used to provide what may be called a "multiplexed magnetic digital HoNon ELISA."

[0173] Figure 21 The diagram illustrates several components involved in a multiplexed magnetic digital HoNon ELISA according to some embodiments. For example, it is assumed that there are three biomarkers A, B, and C to be tested, such as... Figure 21 As shown in the diagram. To test these three biomarkers, three antibiomarker beads, A, B, and C, are also illustrated. Each bead contains MNP 102 and chain-binding groups (illustrated as small circles) to allow it to bind to a flexible molecular chain. The same type of MNP 102 can be used for each bead, or different beads can contain different types of MNP 102. For example, the MNP 102 contained in antibiomarker beads A, B, and C can be of the same type (e.g., a single type of MNP 102 with the same chemical composition (e.g., FeO, Fe3O4, FePt, etc.) can be used for all antibiomarker beads A, B, and C). Alternatively, two or more types of MNP 102 can be used for different antibiomarker beads (e.g., FeO can be used for antibiomarker bead A, FePt for antibiomarker bead B, etc.). Figure 21 In the diagram, antibiomarker A beads comprise MNP 102A of type 1, antibiomarker B beads comprise MNP 102B of type 2, which may be the same as or different from type 1, and antibiomarker C beads comprise MNP 102C of type 3, which may be the same as or different from type 1 and / or type 2. Different types of antibiomarkers are represented by different shades in the diagram to allow them to be distinguished from each other; however, it should be understood that the shading in the diagram does not necessarily indicate that the chemical composition of MNP 102 in use is different.

[0174] As described above, the monitoring system 100 may include a sensor array 110. Figure 21 The diagram illustrates a portion 118 of such a sensor array 110 according to some embodiments. Portion 118 includes three magnetic sensors 105: magnetic sensor 105A, magnetic sensor 105B, and magnetic sensor 105C. Each magnetic sensor 105 has a corresponding binding site 116 on a surface 117 of the sensor array 110 (i.e., magnetic sensor 105A has binding site 116A, magnetic sensor 105B has binding site 116B, and magnetic sensor 105C has binding site 116C), the binding sites 116 being within a fluid chamber 115. A corresponding flexible molecular chain (e.g., biopolymer 101) is attached to the surface 117 at each binding site 116. For example, chain 101A is at binding site 116A, chain 101B is at binding site 116B, and chain 101C is at binding site 116C.

[0175] Figure 22A and 22B The illustration is part of an exemplary procedure for a multiplexed magnetic digital HoNon ELISA, according to some embodiments. Figure 22A The diagram illustrates the introduction of multiple anti-biomarker A beads containing MNP 102A into sensor array 110 (e.g., by adding a solution to fluid chamber 115 of monitoring system 100). As in Figure 22A As shown on the right, the anti-biomarker A bead containing MNP 102A binds to the chain 101A at the binding site 116A sensed by the magnetic sensor 105A. Figure 22B The diagram illustrates how incorporating MNP 102A into chain 101A affects sensor signal 207 (assumed to be MTJ for example) detected by magnetic sensor 105. As sensor signal 207 and Figure 22B As shown in the graph on the left, before the antibiomarker A bead containing MNP 102A is bound to chain 101A, the noise PSD of sensor signal 207 exhibits the 1 / f characteristic expected by the MTJ sensor when MNP 102 is not present. Figure 22B The right-hand side diagram illustrates that after MNP 102A has been incorporated into chain 101A, the noise PSD of sensor signal 207 exhibits the characteristic bulge 140 expected due to the Lorentz function of the overall noise. The presence of bulge 140 in the overall noise PSD indicates that MNP 102 has been incorporated into chain 101A at magnetic sensor 105A. Since only antibiomarker A beads have been added at this point, all magnetic sensors 105 in sensor array 110 can be queried to identify which of their overall PSDs have bulge 140 and thereby determine the location of antibiomarker A beads (e.g., to determine which of all chains 101 have incorporated type A antibiomarker beads).

[0176] Figure 23 Diagram Explanation Figure 22A and 22B Additional possible steps in the exemplary procedure described above. Figure 22A and 22B The discussion describes the terms marked "(a)" and "(b)". Figure 23 The various parts. Which statement applies to? Figure 23 And it was not repeated. After recording the position of antibiomarker A beads in sensor array 110, multiple other antibiomarker beads may optionally be added. For example, next, Figure 23 The diagram illustrates the addition of multiple anti-biomarker B beads, one of which contains MNP 102B. For example... Figure 23 As shown in section "(c)", the antibiomarker B bead containing MNP 102B is bound to chain 101C at magnetic sensor 105C. As explained above, the presence of MNP 102B can be detected in sensor signal 207 of magnetic sensor 105C: the overall noise PSD will have a bulge 140 due to the Lorentz component induced by MNP 102B. Therefore, the location of antibiomarker B bead within sensor array 110 can be determined by interrogating magnetic sensor 105 of sensor array 110, which previously did not sense antibiomarker A bead. After the identity of magnetic sensor 105 sensing antibiomarker B bead has been determined, the identity / location of magnetic sensor 105 detecting antibiomarker A bead and the identity / location of magnetic sensor 105 detecting antibiomarker B bead within sensor array 110 are known.

[0177] Next, optionally, several more anti-biomarker beads may be added. For example, next, Figure 23 The diagram illustrates the addition of multiple anti-biomarker C beads, one of which contains MNP 102C. For example... Figure 23 As shown in section "(d)", the antibiomarker C bead containing MNP 102C is bound to chain 101B at magnetic sensor 105B. As explained above, the presence of MNP 102C can be detected in sensor signal 207 of magnetic sensor 105B: the overall noise PSD will have a bulge 140 due to the Lorentz component induced by MNP 102C. Therefore, the location of antibiomarker C bead can be determined by interrogating the magnetic sensor 105 of sensor array 110, which has not previously sensed antibiomarker A bead or antibiomarker B bead. After the identity of the magnetic sensor 105 that detects antibiomarker C beads has been determined, the identity / location of the magnetic sensor 105 that detects antibiomarker A beads, the identity / location of the magnetic sensor 105 that detects antibiomarker B beads, the identity / location of the magnetic sensor 105 that detects antibiomarker C beads, and the location / identity of the magnetic sensor 105 that does not detect any MNP 102 within the sensor array 110 are all known.

[0178] Optionally, additional types of antibiomarker beads may be added (e.g., more or fewer than three types of biomarkers may be tested), and the location of these additional antibiomarker beads may be determined as described above.

[0179] Next, as Figure 24A The illustration shows that a biomarker corresponding to the previously added anti-biomarker bead can be added (e.g., added to the fluid chamber 115 of the monitoring system 100). Figure 24A The diagram illustrates the addition of a complex biological solution containing all biomarkers A, B, and C. Because the positions of anti-biomarker A beads, anti-biomarker B beads, and anti-biomarker C beads are known, and because each biomarker type will bind only to the same type of anti-biomarker bead, all biomarkers to be tested can be added simultaneously without interference. Figure 24A The illustration in the example shows that type A biomarkers bind to antibiomarker A beads containing MNP 102A attached to chain 101A. Similarly, type B biomarkers bind to antibiomarker B beads containing MNP 102B attached to chain 101C, and type C biomarkers bind to antibiomarker C beads containing MNP 102C attached to chain 101B. Figure 24B An example is shown illustrating what the entire sensor array 110 might look like after the addition of a composite biological solution containing all three biomarkers A, B, and C. (It should be understood that, as explained above, embodiments of sensor array 110 may have many more magnetic sensors 105 than shown in the figures herein (e.g., thousands, millions, etc.).)

[0180] Figure 25 The diagram illustrates how the binding of biomarkers can be detected based on the detected noise PSD of the sensor signal 207 of a specific magnetic sensor 105. Figure 25 The left-hand diagram illustrates the instance noise PSD of the magnetic sensor 105A after MNP 102A has been integrated into the chain 101A (e.g., corresponding to the noise level in the MNP 102A). Figure 22A (The status of sensor array 110 is shown on the right). Figure 25 The left side shows the components of the PSD that generate the total noise in sensor signal 207 when added to the sensor noise PSD: the sensor noise PSD (caused by magnetic sensor 105A) and the Lorentz function (caused by MNP 102A). In the illustrated example, the corner frequency of the Lorentz function is approximately 10kHz, and as described above, it is a function of the diameter of MNP 102A.

[0181]

[0182] Where (as described above), η is the dynamic viscosity of the surrounding liquid (for water at room temperature, it is approximately...). ), d is the diameter of MNP 102A, and K is the spring constant of molecular chain 101A.

[0183] Figure 25 The right-hand side diagram illustrates an example noise PSD of magnetic sensor 105A after the addition of the complex biological solution and after biomarker of type A has bound to anti-biomarker A beads containing MNP 102A (which binds to chain 101A at magnetic sensor 105A). The component sensor noise PSD and Lorentz function are also shown, representing the overall noise PSD in sensor signal 207 after the addition of the sensor noise PSD. The sensor noise PSD is compared with... Figure 25 The left-hand side is the same, but the Lorentz function has been altered due to the incorporation of type A biomarkers. Assuming the diameter of the type A biomarker is approximately the same as that of MNP 102A, the corner frequency of the Lorentz function will shift to a lower frequency given by the following equation.

[0184]

[0185] Therefore, the presence of biomarker A at magnetic sensor 105A approximately doubles the apparent diameter of MNP 102A, resulting in a non-negligible shift in the corner frequency of the Lorentz function. The presence of biomarker A at magnetic sensor 105A can be detected by detecting this corner frequency shift. The presence of biomarkers (of any type) at other magnetic sensors 105 can be detected similarly.

[0186] Figure 26 This is a flowchart of a process 600 for detecting biomarker binding according to some embodiments. For example, process 600 can be used to detect biological events (e.g., in...) Figure 2A (The biological events discussed in the context of the above) and others. At 602, the noise PSD of the magnetic sensor 105 of the sensor array 110 is determined in the absence of any MNP 102 (e.g., in the absence of any MNP 102 in the sensing region 206). At 604, the biopolymer 101 (chain) is coupled to the corresponding binding site 116 sensed by the corresponding magnetic sensor 105. At 606, a plurality of anti-biomarker beads are prepared. As described above in the context of the biological events discussed in the above. Figure 21 As described in the discussion, the antibiomarker beads contain MNP 102. At 608, a first set of antibiomarker beads (e.g., of the first type to be tested) is added to the fluid chamber 115 of the monitoring system 100. At 610, the identity (or location) of the magnetic sensor 105 for detecting the antibiomarker beads is determined. As illustrated above (e.g., in the context of...), Figure 22A and 22B In the discussion, the presence of the antibiomarker bead at a particular magnetic sensor 105 can be detected by determining whether the overall noise PSD of the sensor signal 207 after the addition of the antibiomarker bead (and therefore MNP 102) has a bulge 140 due to the addition of the Lorentz function characterizing the noise caused by MNP 102.

[0187] At position 612, determine if there are any more anti-biomarker beads to test (e.g., refer to...). Figure 23 (Whether antibiomarker B beads or antibiomarker C beads are present). If so, then process 600 repeats steps 608 and 610. Once no more antibiomarker beads are to be added, the monitoring system 100 has a map of which magnetic sensors 105 of the sensor array 110 sense the chain 101 that has incorporated antibiomarker beads and, in the case of multiple types of antibiomarker beads, which magnetic sensors 105 sense which types of antibiomarker beads.

[0188] At point 614, a solution containing a biomarker corresponding to the anti-biomarker beads in fluid chamber 115 is added to fluid chamber 115. As explained above, one advantage of some embodiments is that multiple biomarkers can be tested simultaneously. Therefore, if fluid chamber 115 contains more than one type of anti-biomarker beads, the added solution can contain multiple types of biomarkers, all of which can be added to fluid chamber 115 simultaneously. (Of course, it should be understood that if multiple biomarkers to be tested are present, they can be added individually.)

[0189] At 616, sensor signals 207 are obtained from at least those magnetic sensors 105 sensing the corresponding MNP 102. At 618, binding of the biomarker is detected based on a comparison between the sensor signals 207 collected in step 610 and the sensor signals collected in step 616. For example, as described above in the section on... Figure 25 As explained in the discussion, the corner frequency of the Lorentz function of the overall noise PSD of the sensor signal 207 fitted from step 610 can be compared with the corner frequency of the Lorentz function of the overall noise PSD of the sensor signal 207 fitted from step 616 to see if the corner frequency has changed. Specifically, and as explained above, the incorporation of biomarkers can be detected based on the decrease in corner frequency due to the increase in the effective diameter of MNP 102 (e.g., the increase in the effective mass of biopolymer 101 and the decrease in the motion frequency of MNP 102).

[0190] It should be understood that the steps of process 600 are shown in an exemplary order, but some steps may be performed in a different order. As just one example, the order of steps 602, 604, and 606 may be different (e.g., step 604 may be performed before step 602 or after step 606; step 606 may be performed before step 602 and / or before step 604; etc.).

[0191] In the foregoing description and in the accompanying drawings, specific terminology has been set forth in order to provide a thorough understanding of the disclosed embodiments. In some instances, terminology or diagrams may imply specific details not required for practicing the invention.

[0192] To avoid unnecessarily obscuring the invention, well-known components are shown in block diagram form and / or not discussed in detail or, in some cases, not discussed at all.

[0193] Unless otherwise specifically defined herein, all terms shall be given the broadest possible interpretation, including the meaning implied by the specification and figures, and the meaning as understood by one of ordinary skill in the art and / or as defined in dictionaries, papers, etc. As expressly stated herein, some terms may not conform to their common or usual meaning.

[0194] As used herein, the singular forms "(a)", "(an)" and "the" do not exclude plural indicators unless otherwise specified. The wording "(a)" or "(an)" should be interpreted as inclusive unless otherwise specified. Thus, the phrase "A or B" should be interpreted as meaning all of the following: "both A and B", "A but not B", and "B but not A". "(Any use of the wording "(a)" or "(an)" in this document does not imply exclusivity."

[0195] As used herein, the phrases of the form "at least one of A, B and C", "at least one of A, B or C", "one or more of A, B or C" and "one or more of A, B and C" are interchangeable, and each encompasses all the following meanings: "A only", "B only", "C only", "A and B but not C", "A and C but not B", "B and C but not A" and "all A, B and C".

[0196] With regard to the use of the terms "comprising," "having," "with," and their variations herein, such terms are intended to be inclusive in a manner similar to the term "including," that is, meaning "including but not limited to." The terms "exemplary" and "example" are used to express instances, not preferences or requirements. The term "coupling" is used herein to express direct connection / attachment as well as connection / attachment through one or more intervening elements or structures. The terms "above," "below," "between," and "on" are used herein to refer to the relative position of one feature with respect to other features. For example, a feature positioned "above" or "below" another feature may be in direct contact with the other feature or may have intervening material. Furthermore, a feature positioned "between" two features may be in direct contact with said two features or may have one or more intervening features or materials. In contrast, a first feature "on" a second feature is in contact with that second feature.

[0197] The term "substantially" is used to describe structures, configurations, dimensions, etc., that are largely or almost as stated, but which may actually not always or necessarily be exactly as stated due to manufacturing tolerances, etc. For example, describing two lengths as "substantially equal" means that the two lengths are identical for all practical purposes, but they may not (and need not) be exactly equal at sufficiently small scales. As another example, a structure that is "substantially perpendicular" will be considered perpendicular for all practical purposes, even if it is not exactly 90 degrees relative to the horizontal.

[0198] The diagram may not be to scale, and the size, shape, and dimensions of the features may differ substantially from the way the features are depicted in the diagram.

[0199] Although specific embodiments have been disclosed, it will be understood that various modifications and changes can be made to the invention without departing from the broader spirit and scope thereof. For example, any feature or aspect of an embodiment may be used, at least in practical situations, in combination with or in place of any other feature or aspect in the embodiment. Therefore, the specification and drawings should be regarded as illustrative rather than restrictive.< / r>

Claims

1. A method (300) for monitoring single-molecule biological processes using a magnetic sensor (105) having a sensing region, the method comprising: The biopolymer is coupled (304) to the binding site sensed by the magnetic sensor; Magnetic particles are coupled (306) to the biopolymer; A signal (308) is obtained from the magnetic sensor, the signal representing the characteristics of the magnetic sensor, the characteristics of the magnetic sensor indicating the magnetic environment of the sensing area during a first detection cycle and a second detection cycle; Determine a first Lorentz function characterizing the signal obtained from the magnetic sensor during the first detection period; Determine a second Lorentz function characterizing the signal obtained from the magnetic sensor during the second detection period; and (310) Detecting the motion of the magnetic particle based on the change of the signal between the first detection period and the second detection period, wherein detecting the motion of the magnetic particle based on the change of the signal between the first detection period and the second detection period includes identifying the difference between the first corner angular frequency of the first Lorentz function and the second corner angular frequency of the second Lorentz function.

2. The method according to claim 1, wherein the magnetic particles are magnetic nanoparticles.

3. The method according to claim 1, wherein the magnetic particles are superparamagnetic.

4. The method according to claim 1, wherein the size of the magnetic particles is less than 5 nm.

5. The method according to claim 1, wherein the magnetic particles comprise iron oxide (FeO), Fe3O4, or FePt.

6. The method of claim 1, wherein the biopolymer is a nucleic acid or a protein.

7. The method of claim 1, wherein the signal conveys the detected magnetic field.

8. The method of claim 1, wherein detecting the motion of the magnetic particle based on the change of the signal between the first detection period and the second detection period comprises: Obtain a first autocorrelation of a first portion of the signal, the first portion of the signal being detected during the first detection period; Obtain the second autocorrelation of the second portion of the signal, the second portion of the signal being detected during the second detection period; and Identify at least one difference between the first autocorrelation and the second autocorrelation.

9. The method of claim 8, further comprising sampling the signal.

10. The method according to claim 1, wherein the first detection period and the second detection period are non-overlapping.

11. The method of claim 1, wherein the signal transmits noise.

12. The method of claim 11, wherein the noise is frequency noise or phase noise.

13. The method of claim 1, wherein the signal conveys the oscillation frequency of the magnetic sensor.

14. The method of claim 1, further comprising sampling the signal.

15. The method of claim 1, wherein the magnetic sensor comprises a magnetic tunnel junction (MTJ).

16. The method of claim 1, wherein the magnetic sensor comprises a spin torque oscillator (STO).

17. The method of claim 1, wherein the magnetic sensor comprises a spin valve.

18. The method of claim 1, wherein the volume of the sensing area of ​​the magnetic sensor is between 10... 5 nm 3 With 5×10 5 nm 3 between.

19. The method of claim 1, wherein the binding site is located in a fluid chamber of the detection system, and the method further comprises adding a solution to the fluid chamber.

20. The method of claim 19, wherein the addition of the solution to the fluid chamber occurs between the first detection cycle and the second detection cycle.

21. The method of claim 19, wherein the solution contains Mg 2+ ion.

22. The method of claim 19, wherein the solution contains at least one biomarker.

23. The method of claim 1, further comprising applying a magnetic field to the magnetic particles.

24. The method of claim 1, further comprising obtaining the signal from the magnetic sensor during a third detection cycle, wherein the third detection cycle occurs when the magnetic particle is located outside the sensing region.

25. The method of claim 24, further comprising using the signal detected during the third detection period to determine the noise power spectral density (PSD) of the magnetic sensor.

26. The method of claim 25, further comprising determining a Lorentz function characterized by a corner frequency, wherein the sum of the Lorentz function and the noise PSD of the magnetic sensor is equal to the PSD of the signal from the magnetic sensor during the first detection period or during the second detection period.

27. The method according to claim 25, Wherein the sum of the first Lorentz function and the noise PSD of the magnetic sensor is equal to the first PSD of the signal representing the characteristic of the magnetic sensor during the first detection period; Wherein the sum of the second Lorentz function and the noise PSD of the magnetic sensor is equal to the second PSD of the signal representing the characteristic of the magnetic sensor during the second detection period; and The conclusion that a biological process has occurred is drawn based on the fact that the first corner frequency is different from the second corner frequency.

28. The method of claim 27, wherein the biological process includes coupling a biomarker to the biopolymer, and the second detection cycle is after the addition of a composite biological solution comprising a plurality of biomarkers, and wherein the first corner frequency is greater than the second corner frequency.

29. The method according to claim 1, Wherein the first Lorentz function represents the first noise PSD generated due to the motion of the magnetic particles during the first detection period; and The second Lorentz function represents the second noise PSD generated due to the motion of the magnetic particles during the second detection period.

30. The method of claim 29, wherein the second detection cycle is performed after adding a composite biological solution comprising a plurality of biomarkers, and wherein the first corner frequency is greater than the second corner frequency.

31. A system (100) for monitoring the motion of magnetic particles (102) coupled to a biopolymer (101), the system comprising: A fluid chamber (115) includes binding sites (116) for holding no more than a single biopolymer at a time, wherein the binding sites are configured to attach one end of the biopolymer to the surface (117) of the fluid chamber and allow the magnetic particles to move. At least one processor (130); and A magnetic sensor (105) is configured to sense a sensing region (206) within the fluid chamber, wherein the sensing region is configured to allow the magnetic sensor to sense the binding site but not other binding sites, and wherein the magnetic sensor is configured to generate a signal (207) characterizing the magnetic environment within the sensing region and to provide the signal to the at least one processor. The at least one processor is configured to: A first portion of the signal is obtained, the first portion of the signal representing the magnetic environment within the sensing area during a first detection period. Determine the first power spectral density (PSD) of the first portion of the signal. A second portion of the signal is obtained, the second portion of the signal representing the magnetic environment within the sensing area during a second detection period, the second detection period being after the first detection period. Determine the second power spectral density (PSD) of the second portion of the signal, and Analyzing the first portion and the second portion of the signal to detect the motion of the magnetic particle, wherein analyzing the first portion and the second portion of the signal to detect the motion of the magnetic particle includes: Fit the first Lorentz function to the first PSD. Fit the second Lorentz function to the second PSD, and The first corner frequency of the first Lorentz function is compared with the second corner frequency of the second Lorentz function.

32. The system of claim 31, wherein the signal transmits noise.

33. The system of claim 32, wherein the noise is frequency noise or phase noise.

34. The system of claim 31, wherein the signal conveys the oscillation frequency of the magnetic sensor.

35. The system of claim 31, wherein the magnetic sensor comprises a magnetic tunnel junction (MTJ).

36. The system of claim 31, wherein the magnetic sensor comprises a spin torque oscillator (STO).

37. The system of claim 31, wherein the magnetic sensor includes a spin valve.

38. The system of claim 31, wherein the volume of the sensing region is between 10 5 nm 3 With 5×10 5 nm 3 between.

39. The system of claim 31, wherein the at least one processor is further configured to: Determine the first autocorrelation function of the first portion of the signal; and Determine the second autocorrelation function of the second portion of the signal; Furthermore, analyzing the first portion and the second portion of the signal to detect the motion of the magnetic particle further includes comparing the first autocorrelation function with the second autocorrelation function.

40. The system of claim 31, further comprising a detection circuit system coupled to the magnetic sensor and coupled to the at least one processor.

41. The system of claim 40, wherein the detection circuit system comprises at least one line.

42. The system of claim 40, wherein the detection circuitry includes at least one of an amplifier or an analog-to-digital converter.

43. The system of claim 31, wherein the binding site comprises a structure configured to anchor the biopolymer to the binding site.

44. The system of claim 43, wherein the structure comprises a cavity or a ridge.

45. The system of claim 31, wherein the magnetic particle is a first magnetic particle, the biopolymer is a first biopolymer, the magnetic sensor is a first magnetic sensor, the sensing region is a first sensing region, and the signal is a first signal, and wherein the fluid chamber further includes a second binding site for holding no more than a single biopolymer at a time, and wherein the second binding site is configured to attach one end of the second biopolymer to the surface of the fluid chamber and allow movement of the second magnetic particle coupled to the second biopolymer, and the system further includes: A second magnetic sensor has a second sensing region within the fluid chamber, wherein the second sensing region includes the second binding site but not other binding sites, and wherein the second magnetic sensor is configured to generate a second signal characterizing the magnetic environment within the second sensing region and to provide the second signal to the at least one processor. And wherein the at least one processor is further configured to: A first portion of the second signal is obtained, wherein the first portion of the second signal represents the magnetic environment within the second sensing area during the third detection cycle. A second portion of the second signal is obtained, the second portion of the second signal representing the magnetic environment within the second sensing area during the fourth detection cycle, and The first portion of the second signal and the second portion of the second signal are analyzed to detect the motion of the second magnetic particle.

46. ​​The system according to claim 45, wherein the first detection cycle is the same as the third detection cycle, and the second detection cycle is the same as the fourth detection cycle.

47. The system of claim 31, wherein the magnetic sensor is one of a plurality of magnetic sensors disposed in a sensor array (110).

48. The system of claim 47, further comprising at least one line coupling the sensor array to the at least one processor.

49. The system of claim 48, wherein the binding site is located in a trench in a first line of the at least one line.

50. The system of claim 47, wherein the plurality of magnetic sensors are arranged in a rectangular grid pattern.

51. The system of claim 31, wherein the at least one processor comprises at least two processors, wherein a first processor of the at least two processors is configured to obtain the first portion and the second portion of the signal, and a second processor of the at least two processors is configured to analyze the first portion and the second portion of the signal to detect the motion of the magnetic particles.

52. The system of claim 51, wherein the first processor is disposed in a device including the magnetic sensor, and the second processor is external to the device.

53. The system of claim 31, wherein the at least one processor is further configured to determine the Lorentz function.

54. The system of claim 31, wherein the at least one processor is further configured to determine the noise power spectral density of the magnetic sensor.

55. The system of claim 31, wherein the at least one processor is further configured to determine that a particular biomarker has been coupled to the biopolymer based on a comparison of a first corner frequency of the first Lorentz function with a second corner frequency of the second Lorentz function.

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