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Abstract Magnetoelastic micro-electromechanical systems (MEMS) are integral elements of sensors, actuators, and other devices utilizing magnetostriction for their functionality. Their sensitivity typically scales with the saturation magnetostriction and inversely with magnetic anisotropy. However, large saturation magnetostriction and small magnetic anisotropy make the magnetoelastic layer highly susceptible to minuscule anisotropic stress. It is inevitably introduced during the release of the mechanical structure during fabrication and severely impairs the device’s reproducibility, performance, and yield. To avoid the transfer of residual stress to the magnetic layer, we use a shadow mask deposition technology. It is combined with a free-free magnetoelectric microresonator design to minimize the influence of magnetic inhomogeneity on device performance. Magnetoelectric resonators are experimentally and theoretically analyzed regarding local stress anisotropy, magnetic anisotropy, and the ΔE-effect sensitivity in several resonance modes. The results demonstrate an exceptionally small device-to-device variation of the resonance frequency

An application specific integrated circuit (ASIC) and a custom-made microelectromechanical system (MEMS) sensor are presented, designed to function together as a sensor system for measuring low amplitude low frequency magnetic fields. The MEMS system comprises several free-standing double-wing magnetoelectric resonators with a size of $900~\mu $ m x $150~\mu $ m to measure alternating magnetic fields in the sub-kilohertz regime. It utilizes piezolelectric (AlN) and magnetostrictive (FeCoSiB) layers to exploit the delta-E effect for magnetic field sensing. On the ASIC a three-channel current-reuse amplifier with lateral bipolar transistors in the input stage is implemented occupying a chip area of 0.0864 mm2. Measurements demonstrate a voltage gain of 40 dB with a 3-dB bandwidth of 75 kHz and an input referred noise floor of 8 nV/ $\surd $ Hz while consuming $199~\mu $ W per channel. The sensor system is capable of detecting magnetic fields with a limit of detection (LOD) of 16 nT/ $\surd $ Hz for single sensor elements. By operating three sensor elements in parallel, one on each amplifier channel, the LOD is further reduced to 10 nT/ $\surd $ Hz. Owing to the high reproducibility of the sensor elements, this improvement in the LOD is close to the ideal value of $\surd 3$ . The results imply that the system can be scaled to large numbers of sensor elements without principle obstacles.

A split-voltage amplifier architecture is proposed which improves the power efficiency compared to a conventional implementation. The approach is verified with a prototype fabricated in 0.35 µm CMOS technology using lateral bipolar input transistors. It achieves a measured DC gain of 105 V/V, a differential AC gain of 40.3 dB with a bandwidth of 55 kHz, a CMRR of approximately 75 dB, and a PSRR of 55 dB. The input-referred noise is 7 nV/√Hz and 923 nVrms integrated from 100 Hz to 10 kHz, resulting in a Noise Efficiency Factor (NEF) of 2.84 and a Power Efficiency Factor (PEF) of 18.3. The split-voltage configuration improves power efficiency by nearly 25% compared to a full voltage supply and maintains a small area design. Action potentials of the medial and lateral giant fiber of an earthworm are recorded as an example application.

Rapid and accurate detection of the pathogens, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for COVID-19, is critical for mitigating the COVID-19 pandemic. Current state-of-the-art pathogen tests for COVID-19 diagnosis are done in a liquid medium and take 10–30 min for rapid antigen tests and hours to days for polymerase chain reaction (PCR) tests. Herein we report novel accurate pathogen sensors, a new test method, and machine-learning algorithms for a breathalyzer platform for fast detection of SARS-CoV-2 virion particles in the aerosol in 30 s. The pathogen sensors are based on a functionalized molecularly-imprinted polymer, with the template molecules being the receptor binding domain spike proteins for different variants of SARS-CoV-2. Sensors are tested in the air and exposed for 10 s to the aerosols of various types of pathogens, including wild-type, D614G, alpha, delta, and omicron variant SARS-CoV-2, BSA (Bovine serum albumin), Middle East respiratory syndrome–related coronavirus (MERS-CoV), influenza, and wastewater samples from local sewage. Our low-cost, fast-responsive pathogen sensors yield accuracy above 99% with a limit-of-detection (LOD) better than 1 copy/μL for detecting the SARS-CoV-2 virus from the aerosol. The machine-learning algorithm supporting these sensors can accurately detect the pathogens, thereby enabling a new and unique breathalyzer platform for rapid COVID-19 tests with unprecedented speeds.

Summary: Cryptococcus neoformans (C. neoformans) is estimated to cause about 220,000 new cases every year in patients with AIDS, despite advances in antifungal treatments. C. neoformans possesses a remarkable ability to disseminate through an immunocompromised host, making treatment difficult. Here, we examine the mechanism of survival of C. neoformans under varying host conditions and find a role for ceramide synthase in C. neoformans virulence. This study also provides a detailed lipidomics resource for the fungal lipid research community in addition to discovering a potential target for antifungal therapy.

Recently, Delta-E effect magnetic field sensors based on exchange-biased magnetic multilayers have shown the potential of detecting low-frequency and small-amplitude magnetic fields. Their design is compatible with microelectromechanical system technology, potentially small, and therefore, suitable for arrays with a large number N of sensor elements. In this study, we explore the prospects and limitations for improving the detection limit by averaging the output of N sensor elements operated in parallel with a single oscillator and a single amplifier to avoid additional electronics and keep the setup compact. Measurements are performed on a two-element array of exchange-biased sensor elements to validate a signal and noise model. With the model, we estimate requirements and tolerances for sensor elements using larger N. It is found that the intrinsic noise of the sensor elements can be considered uncorrelated, and the signal amplitude is improved if the resonance frequencies differ by less than approximately half the bandwidth of the resonators. Under these conditions, the averaging results in a maximum improvement in the detection limit by a factor of N. A maximum N≈200 exists, which depends on the read-out electronics and the sensor intrinsic noise. Overall, the results indicate that significant improvement in the limit of detection is possible, and a model is presented for optimizing the design of delta-E effect sensor arrays in the future.

Disease detection in historical samples currently relies on DNA extraction and amplification, or immunoassays. These techniques only establish pathogen presence rather than active disease. We report the first use of shotgun proteomics to detect the protein expression profile of buccal swabs and cloth samples from two 500-year-old Andean mummies. The profile of one of the mummies is consistent with immune system response to severe pulmonary bacterial infection at the time of death. Presence of a probably pathogenic Mycobacterium sp. in one buccal swab was confirmed by DNA amplification, sequencing, and phylogenetic analyses. Our study provides positive evidence of active pathogenic infection in an ancient sample for the first time. The protocol introduced here is less susceptible to contamination than DNA-based or immunoassay-based studies. In scarce forensic samples, shotgun proteomics narrows the range of pathogens to detect using DNA assays, reducing cost. This analytical technique can be broadly applied for detecting infection in ancient samples to answer questions on the historical ecology of specific pathogens, as well as in medico-legal cases when active pathogenic infection is suspected.