Search for: All records

Tunable non-reciprocal components with ferrites that can be integrated using a foundry suitable process are key to achieving low-power adaptive microwave circuits. The current state-of-the-art still relies on electrical tuning or resistive absorbers to facilitate unidirectional propagation. Here, we demonstrate a novel process for spin-coating thick films of ferrites without the complexities of vacuum processes or high-temperature annealing. Composites of yttrium iron garnet (YIG) nanoparticles in a matrix spin-on-glass are spin-coated on silicon substrates, and magnetic properties comparable to bulk YIG are obtained in films exceeding 30 microns. We also propose a design for tunable phase shifter based on periodically serrated coplanar waveguide with a YIG cladding. A nonreciprocal phase difference of 20 – 60 degrees is obtained over a tunable band of 550 MHz between 3.85 - 4.4 GHz from a tuning magnetic field of 8 – 40 kA/m. 

Amorphous magnetic alloys with large perpendicular magnetic anisotropy (PMA) have emerged as a suitable material choice for spintronic memory and high-frequency non-reciprocal devices on-chip. Unlike ferromagnets, ferrimagnets offer faster switching dynamics, lower net saturation magnetization, minimal stray field and a lower net angular momentum. Ferrimagnetic thin films of Gd x Co 1− x sputter deposited as heterostructures of Ta/Pt/Gd x Co 1− x (t)/Pt on Si/SiO 2 have bulk-like PMA for thicknesses of 5–12 nm and room-temperature magnetic compensation for x = 28–32%. Preferential oxygenation of GdCo has been found to increase the effective anisotropy energy density by an order of magnitude and produce near-ideal remanence ratios. X-ray photoelectron spectroscopy accurately quantifies the metal-oxidation ratio, which shows that an oxygen-rich and Co-deficient stoichiometry (Gd 21 Co 28 O 51 ) likely weakens the ferromagnetic exchange interaction between Co–Co and contributes additional antiferromagnetic exchange through superexchange-like interactions between Gd and Co via O, resulting in a stronger out-of-plane magnetization. Even greater PMA and giant-anisotropy field of 11 kOe are achieved in super-lattices of the Gd 21 Co 28 O 51 heterostructure. The combination of ferrimagnetic ordering in amorphous Gd x Co 1− x and its affordance of pathways for engineering large PMA will enable the design of integrated high-frequency devices beyond 30 GHz and ultrafast energy efficient memory devices with switching speeds down to tens of picoseconds. 

COVID-19 is a respiratory disease caused by a recently discovered, novel coronavirus, SARS-COV-2. The disease has led to over 81 million confirmed cases of COVID-19, with close to two million deaths. In the current social climate, the risk of COVID-19 infection is driven by individual and public perception of risk and sentiments. A number of factors influences public perception, including an individual’s belief system, prior knowledge about a disease and information about a disease. In this article, we develop a model for COVID-19 using a system of ordinary differential equations following the natural history of the infection. The model uniquely incorporates social behavioral aspects such as quarantine and quarantine violation. The model is further driven by people’s sentiments (positive and negative) which accounts for the influence of disinformation. People’s sentiments were obtained by parsing through and analyzing COVID-19 related tweets from Twitter, a social media platform across six countries. Our results show that our model incorporating public sentiments is able to capture the trend in the trajectory of the epidemic curve of the reported cases. Furthermore, our results show that positive public sentiments reduce disease burden in the community. Our results also show that quarantine violation and early discharge of the infected population amplifies the disease burden on the community. Hence, it is important to account for public sentiment and individual social behavior in epidemic models developed to study diseases like COVID-19. 

Rare-earth iron garnets with large magnetic gyrotropy, made with reduced thermal budgets, are ideal magneto-optical materials for integrated isolators. However, reduced thermal budgets impact Faraday rotation by limiting crystallization, and characterization of crystallinity is limited by resolution or scannable area. Here, electron backscatter diffraction (EBSD) is used to measure crystallinity in cerium substituted yttrium- and terbium-iron garnets (CeYIG and CeTbIG) grown on planar Si, crystallized using one-step rapid thermal processes, leading to large Faraday rotations > −3500 °/cm at 1550 nm. Varying degrees of crystallinity are observed in planar Si and patterned Si waveguides, and specific dependences of crystallite size are attributed to the nucleation/growth processes of the garnets and the lateral dimensions of the waveguide. On the other hand, a low thermal budget alternative–exfoliated CeTbIG nanosheets–are fully crystalline and maintain high Faraday rotations of −3200 °/cm on par with monolithically integrated thin film garnets.

 

The precise control of magnetic properties at the microscale has transformative potential in healthcare and human‐robot interaction. This research focuses on understanding the magnetic interactions in nanostructure assemblies responsible for microactuation. By combining experimental measurements and micromagnetic simulations, the interactions in both nanocube and nanochain assemblies are elucidated. Hysteresis measurements and first‐order reversal curves (FORC) reveal that the spatial arrangement of these assemblies governs their collective magnetism. A critical concentration threshold is observed where a transition from ferromagnetic‐like to antiferromagnetic‐like coupling occurs. Leveraging the high uniaxial anisotropy of 1D nanochains, the remanent magnetization of assembled chain structures is maximized for efficient magneto‐mechanical energy transduction. By utilizing an optimized magnetic nanostructure concentration, a flexible film is fabricated, and its significantly enhanced mechanical deformation response to a small magnetic field, surpassing conventional particle‐based samples by a factor of five, is demonstrated. Demonstrating excellent transduction efficiency, visible deformations such as bending and S‐shaped twisting modes are achieved with an applied field of less than 400 Oe. Furthermore, the reprogrammability of the actuator, achieving a U‐shaped bending mode by altering its magnetization profile, is showcased. This research provides valuable insights for designing reconfigurable and effective microactuators and devices at significantly smaller scales than previously possible.

 

Many rapidly developing countries around the world are at a crossroads when it comes to transportation, air quality, and sustainability. Indeed, the challenges presented by vehicular growth in India have motivated the search for sustainable transportation solutions. One solution constitutes ridehailing services, which are expected to reduce car ownership and provide affordable means of transportation. Another key solution is the rise of electric vehicles (EVs), which are expected to reduce greenhouse gas emission and address the growing demand for sustainable urban mobility. Using a unique survey data set collected in 2018 from a sample of 43,000 respondents spread across 20 cities in India, this paper attempts to shed light on the factors that affect adoption of on-demand transportation services and EVs in India. In particular, not only does this paper consider the socio-economic and demographic variables that affect these behavioral choices, but the modeling framework adopted in this study places a special emphasis on representing the important role played by attitudes, values, and perceptions in determining adoption of on-demand transportation services and EVs. It is observed that attitudes and values significantly affect the use of on-demand transportation services and EV ownership, suggesting that information campaigns and free trials/demonstrations would help advance the adoption of sustainable transportation modes. The model results help in the identification of policy options and infrastructure investments that can advance a sustainable transportation future in India.