Accelerated Nano-Optical Imaging through Sparse Sampling.

The integration time and signal-to-noise ratio are inextricably linked when performing scanning probe microscopy based on raster scanning. This often yields a large lower bound on the measurement time, for example, in nano-optical imaging experiments performed using a scanning near-field optical microscope (SNOM). Here, we utilize sparse scanning augmented with Gaussian process regression to bypass the time constraint. We apply this approach to image charge-transfer polaritons in graphene residing on ruthenium trichloride (α-RuCl3) and obtain key features such as polariton damping and dispersion. Critically, nano-optical SNOM imaging data obtained via sparse sampling are in good agreement with those extracted from traditional raster scans but require 11 times fewer sampled points. As a result, Gaussian process-aided sparse spiral scans offer a major decrease in scanning time.

Hyperbolic exciton polaritons in a van der Waals magnet.

Exciton polaritons are quasiparticles of photons coupled strongly to bound electron-hole pairs, manifesting as an anti-crossing light dispersion near an exciton resonance. Highly anisotropic semiconductors with opposite-signed permittivities along different crystal axes are predicted to host exotic modes inside the anti-crossing called hyperbolic exciton polaritons (HEPs), which confine light subdiffractionally with enhanced density of states. Here, we show observational evidence of steady-state HEPs in the van der Waals magnet chromium sulfide bromide (CrSBr) using a cryogenic near-infrared near-field microscope. At low temperatures, in the magnetically-ordered state, anisotropic exciton resonances sharpen, driving the permittivity negative along one crystal axis and enabling HEP propagation. We characterize HEP momentum and losses in CrSBr, also demonstrating coupling to excitonic sidebands and enhancement by magnetic order: which boosts exciton spectral weight via wavefunction delocalization. Our findings open new pathways to nanoscale manipulation of excitons and light, including routes to magnetic, nonlocal, and quantum polaritonics.

Visualizing moiré ferroelectricity via plasmons and nano-photocurrent in graphene/twisted-WSe2 structures.

Ferroelectricity, a spontaneous and reversible electric polarization, is found in certain classes of van der Waals (vdW) materials. The discovery of ferroelectricity in twisted vdW layers provides new opportunities to engineer spatially dependent electric and optical properties associated with the configuration of moiré superlattice domains and the network of domain walls. Here, we employ near-field infrared nano-imaging and nano-photocurrent measurements to study ferroelectricity in minimally twisted WSe2. The ferroelectric domains are visualized through the imaging of the plasmonic response in a graphene monolayer adjacent to the moiré WSe2 bilayers. Specifically, we find that the ferroelectric polarization in moiré domains is imprinted on the plasmonic response of the graphene. Complementary nano-photocurrent measurements demonstrate that the optoelectronic properties of graphene are also modulated by the proximal ferroelectric domains. Our approach represents an alternative strategy for studying moiré ferroelectricity at native length scales and opens promising prospects for (opto)electronic devices.

Programming twist angle and strain profiles in 2D materials.

Moiré superlattices in twisted two-dimensional materials have generated tremendous excitement as a platform for achieving quantum properties on demand. However, the moiré pattern is highly sensitive to the interlayer atomic registry, and current assembly techniques suffer from imprecise control of the average twist angle, spatial inhomogeneity in the local twist angle, and distortions caused by random strain. We manipulated the moiré patterns in hetero- and homobilayers through in-plane bending of monolayer ribbons, using the tip of an atomic force microscope. This technique achieves continuous variation of twist angles with improved twist-angle homogeneity and reduced random strain, resulting in moiré patterns with tunable wavelength and ultralow disorder. Our results may enable detailed studies of ultralow-disorder moiré systems and the realization of precise strain-engineered devices.

Machine Learning for Optical Scanning Probe Nanoscopy.

The ability to perform nanometer-scale optical imaging and spectroscopy is key to deciphering the low-energy effects in quantum materials, as well as vibrational fingerprints in planetary and extraterrestrial particles, catalytic substances, and aqueous biological samples. These tasks can be accomplished by the scattering-type scanning near-field optical microscopy (s-SNOM) technique that has recently spread to many research fields and enabled notable discoveries. Herein, it is shown that the s-SNOM, together with scanning probe research in general, can benefit in many ways from artificial-intelligence (AI) and machine-learning (ML) algorithms. Augmented with AI- and ML-enhanced data acquisition and analysis, scanning probe optical nanoscopy is poised to become more efficient, accurate, and intelligent.

A Soft Material Flow Sensor for Micro Air Vehicles.

To control and navigate micro air vehicles (MAVs) efficiently, there is a need for small, lightweight, durable, sensitive, fast, and low-power airspeed sensors. When designing sensors to meet these requirements, soft materials are promising alternatives to more traditional materials due to the large deformations they can withstand. In this article, a new concept of a soft material flow sensor is presented based on elastic filament velocimetry, which fulfills all necessary criteria. This technique measures flow velocity by relating it to the strain of a soft ribbon suspended between two static supports and subjected to a flow of interest. The ribbon is manufactured from polydimethylsiloxane and can be made piezoresistive by the addition of silver nanowires. With the described manufacturing method, the sensor can be made using common laboratory tools, outside of a clean room, significantly reducing its complexity. Furthermore, it can be operated using a simple and lightweight circuit, making it a convenient alternative for MAVs. Using a piezoresistive material allows for the flow velocity to be calibrated to the resistance change of the strained ribbon. Although certain challenges remain unsolved, such as polymer creep, the sensor has demonstrated its ability to measure flow velocities down to 4 m/s in air through experiments. A time-dependent analytical model is also provided. The model shows that the current sensor has a bandwidth of 480 Hz. Most importantly, the sensitivity and the bandwidth of the sensor can be varied strictly by modifying the geometry and the material properties of the ribbon.

Investigation of Incidents and Trends of Antimicrobial Resistance in Foodborne Pathogens in Eight Countries from Historical Sample Data.

Antimicrobial resistance (AMR) causes millions of illnesses every year, threatening the success of lifesaving antibiotic therapy and, thus, public health. To examine the rise and spread of antimicrobial resistance around the world, our study performs a multivariate statistical analysis of antimicrobial resistance gene data from eight different countries: the US, the UK, China, Brazil, Mexico, Canada, Australia, and South Africa. Multi-dimensional data points were projected onto a two-dimensional plane using principal component analysis and organized into a dendrogram utilizing hierarchical clustering to identify significant AMR genes and pathogens. Outlier genes/pathogens were typically involved in high occurrences of antimicrobial resistance, and they were able to indicate the trend of antimicrobial resistance in the future. Statistical analysis of the data identified: (1) tet(A), aph(3'')-Ib, aph(6)-Id, blaEC, blaTEM-1, qacEdelta1, sul1, sul2, and aadA1 as the nine most common AMR genes among the studied countries; (2) Salmonella enterica and E. coli and Shigella as the most common AMR foodborne pathogens; and (3) chicken as the most prevalent meat carrier of antimicrobial resistance. Our study shows that the overall number of reported antimicrobial resistance cases in foodborne pathogens is generally rising. One potential contributing factor for this is the increasing antimicrobial usage in the growing livestock industry.