Solid and liquid particles in the atmosphere, referred to as airborne particulate matter (PM), have been rising significantly over the past two decades. Exposure to PM carries significant health risks such as lungs damage, heart disease, cancer, and death. PM2.5 is a subgroup of PM particles that are smaller than 2.5 µm and is a major concern as it is more harmful to health and more difficult to detect. One problematic component of PM2.5 is magnetite nanoparticles (<200 nm), which are readily absorbed into the bloodstream through the respiratory system. Eventually, magnetite nanoparticles deposit inside the brain causing neurodegenerative diseases such as Alzheimer’s or cancerous tumors by inducing oxidative stress. Additionally, Magnetite nanoparticles are often surrounded by heavy metal nanoparticles such as Cadmium and lead which are a great concern to the environment and health. Traditional PM detection methods such as laser scattering are bulky, expensive, and incapable of detecting particles smaller than 200 nm such as magnetite nanoparticles. Therefore, developing a low-cost highly sensitive sensor for monitoring magnetite nanoparticles is vital. Tunneling Magneto-Resistance (TMR) sensors are an attractive option due to their low-cost and high sensitivity toward magnetic nanoparticle detection. Moreover, developing a cheap, portable, and precise remote monitoring technique will allow for the creation of high spatial resolution highly sensitive monitoring networks for magnetic PM2.5. This work focuses on developing, modeling, and simulation of low-cost highly sensitive TMR sensor based on Magnetic Tunnel Junction (MTJ) that can detect and count magnetite nanoparticles.
Airborne particulate matter (PM) refers to dispersed solid and liquid particles in the atmosphere. It consists of a mixture of particles that are emitted directly into the air and particles formed by chemical reactions of gaseous pollutants.1 The concentration of these particles is dependent on dispersion and removal rates, transport mechanisms, and emission rates.1
A. PM pollution concerns
There are three classifications of PM by its mean aerodynamic diameter which are PM10 (<10 um), PM2.5 (<2.5 um), and PM0.1 (<100 nm).1,2 The latter two are also known as fine and ultrafine PM, respectively. PM10, when inhaled, is too large to cross the blood-air barrier and enter circulation while PM2.5 and PM0.1 can reach much deeper inside lungs tissues and potentially cross the blood-air barrier into the blood circulation.2,3 One problematic constituent of PM2.5 is magnetic nanoparticles which are usually smaller than 200 nm and have been found inside the human brain where they cause oxidative damage.4,5 This damage has been linked to neurodegenerative and neurodevelopmental diseases such as Alzheimer’s disease, autism, and attention deficit disorder.5–11 Moreover, the Presence of heavy metals, another problematic constituent of PM, can be predicted by the presence of magnetite nanoparticles since they are both by-products of exogenous reactions.12,13 However, Heavy metals are much more dangerous to health than magnetic nanoparticles which further justifies the importance of magnetic detection of PM.
B. PM monitoring solutions
As it stands, PM air pollution is one of the largest contributors to global mortality and morbidity rates responsible for the death of between 4.2 million and 8.9 million.14,15 Unfortunately, Monitoring of PM2.5 around the world is inadequate due to poor spatial resolution of monitoring networks and its insufficiency. This gives rise to the need for low-cost, highly sensitive PM sensors to improve spatial resolution cost-effectively as current low-cost sensors have doubts regarding data reliability.15 The most common PM monitoring techniques involve weight measurements which are bulky, expensive, and require heavy lab processing on filtered mass.16 Another common method is through laser scattering of pollution particles which is expensive and less capable of detecting smaller particles due to the light diffraction limit.17 As such, magnetic nanoparticle monitoring of PM2.5 species can provide significant benefits to large-scale PM monitoring networks due to its low-cost and small form factor.18,19 This technique can detect 50–200 nm magnetic particles compared to existing commercial monitoring solutions that are limited to 1–3 µm particles. This work focuses on developing, modeling, and simulating our MTJ magnetic pollution sensor for counting and localization of magnetic nanoparticles. This study is supported by a finite element method (FEM) model-based simulation developed in COMSOL.
A. Proposed sensor concept and fabrication
The proposed MTJ sensor is shown in Fig. 1. The magnetic field produced by the magnetite nanoparticles can be detected when they pass over the sensing region of the MTJ by altering its free layer magnetization. When this occurs, the electrical resistivity of the sensor decreases as it becomes easier for electrons to tunnel through the oxide layer. Fabrication of the proposed TMR sensor is achieved by magnetron sputtering using the Singulus Rotaris system. The MTJ stack used in this study is given as follows with corresponding thickness in nm in bracket: Si/SiO2/Ru(3)/Ta(8)/Ru(8)/Ta(8)/Ru(3)/MnIr(8)/ Co70Fe30(2.3)/Ru(0.85)/ Co60Fe20B20(2.4) “pinned layer” / MgO(1.53) “oxide barrier” / Co60Fe20B20(1.45) “free layer”/Ru(3)/ Ta(8) as shown in Fig. 1. Patterning of the stack is achieved by optical lithography and ion milling for elliptical microdevices.20–24 Gold contact of approximately 150 nm is deposited on top of the formed junction area. The entire device is annealed after that for 1 hour at 1 µTorr and 360 °C to pin the bottom layer under an applied field of 1 T. Finally, a 50 nm silicon dioxide passivation layer is sputtered for corrosion protection. The modeled magnetic system consists of a TMR sensor array and a current-carrying conductor that produces a magnetic field to attract magnetic PM for detection.
B. Magnetic field simulation
Figure 2(a) shows the magnetic flux density of the nanoparticle interacting with external magnetic fields produced by the current-carrying conductor to attract the particle. Figure 2(b) shows three frames of a simulation that have been combined into one image to illustrate the behavior of the magnetized nanoparticles passing over the sensitive area of the proposed TMR sensor. A magnetic nanoparticle will not be magnetized before or after passing over the current line. However, when the particle is subjected to an external magnetic field generated by the current line, the magnetization of the nanoparticle will grow and reach its strongest point at the center of the TMR sensor. Due to this passage of the magnetized particles over the sensor, the sensor will detect this external magnetic signal as the free layer rotates and the resistance changes.
This magnetic field is then detected when nanoparticles pass over the TMR sensor which generates an electrical pulse. The pulse is a result of a change in the magnetization of the free layer. When the magnetization of both layers is aligned, electrons tunnel the easiest which is observed by a drop in the electrical resistivity of the TMR sensor. If the magnetization of both layers is anti-parallel, the electrical resistivity is at its maximum since the electron tunneling is hindered. When Fe3O4 magnetite is injected inside the small, closed chamber, where its magnetic field can be aligned or opposed to the pinning field, which results in a resistivity shift across the MTJ curve as seen in Fig. 3(b). Applying current in the conductor line to create a magnetic field that attracts magnetic nanoparticles will also affect the magnetization of the MTJ-free layer. If we plot the hysteresis loop of the sensor in order in three cases. First, we plot the hysteresis loop of the sensor only in the absence of the current in the conductive line and no nanoparticles. Then, we apply current in the line and we actually create an external field, felt by the sensor and explained the shift of the plot. It means that at zero field in the inset graph you can see the change of the resistance because it is true we are no applying magnetic field to plot the Hysteresis (zero field) but we are applying the field created by the current in the line. That’s why the resistance changed at zero field in the plot. In the third case, we are applying the current in the line (so creating a magnetic field) and we are injecting magnetic nanoparticles. So now in this case, the sensor is sensitive to, the created magnetic field which attracted the nanoparticles close to the sensor, and as a result the sensor is sensitive now not only to the magnetic field but also to the particles magnetization. So the result is a new change on the resistance of the sensor which explain the shift again. All these shifts are clearly seen in the set of Fig. 3(b). We present the change of sensor resistance at zero fields.
The quantification of magnetite nanoparticles characterized in Fig. 4 can be achieved by analyzing the total output voltages from the TMR sensor by LABVIEW™. The real-time voltage change ΔV (mV) is defined between the reference voltage of the sensor, with the stray field, and the new measured voltage of the sensor once the Magnetic particles pass through the TMR sensor.
We used 10 mg of Fe3O4 nanopowder, 50–100 nm particle size (SEM), 97% trace metals basis weight formula of 231.53 g/mol which corresponds to some hundreds thousands of magnetic nanoparticles. In our system, we can detect the presence of the nanoparticules regardless the number and the size of the nanoparticles. We suggest this idea to prove the concept, and find a way to qualify the atmosphere air if we detect a magnetic pollution or not. As a continuity of this work, one of the possible studies is to try to see the difference of the peaks depending on the particles sizes.
This work presents a low-cost highly sensitive atmospheric pollution sensor that measures the magnetic signature of PM2.5 pollution. Unlike current measurement systems, this work can measure particle sizes much smaller in size. Additionally, due to their low cost, a large PM monitoring network can be created for increased spatial resolution while also maintaining high temporal resolution. Although this sensor only measures magnetic nanoparticles, mainly magnetite and ferrous oxides, it can be paired with machine learning algorithms to predict heavy metal pollution which is another dangerous constituent of airborne PM pollution.
Conflict of Interest
The authors have no conflicts to disclose.
Selma Amara: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Investigation (equal); Methodology (equal); Software (equal); Validation (equal); Visualization (equal). Abdulrahman Aljedaibi: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Investigation (equal); Methodology (equal); Software (equal); Validation (equal); Visualization (equal); Writing – original draft (equal); Writing – review & editing (equal). Ali Alrashoudi: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Investigation (equal); Methodology (equal); Software (equal); Validation (equal); Visualization (equal); Writing – original draft (equal); Writing – review & editing (equal). Sofiane Ben Mbarek: Investigation (equal); Validation (equal); Visualization (equal). Danial Khan: Writing – original draft (equal); Writing – review & editing (equal). Yehia Massoud: Funding acquisition (equal); Project administration (equal); Resources (equal); Supervision (equal).
The data that support the findings of this study are available from the corresponding author upon reasonable request.