Thermoelectric materials have attracted considerable interest for energy applications such as waste-heat recovery and energy harvesting to power Internet-of-things sensors. In recent decades, an increasing number of different strategies to increase performance have been invented and tested, including the synthesis of thin films and other high-performance multilayered structures. Although it has already been shown that the pure combination of the properties of each layer without interactions will yield worse performance compared to the best layer, a critical estimation of the size of the deviation to trace back individual properties is still missing. In this paper we derive a set of formulas to describe the total Seebeck coefficient, electrical and thermal conductivity, power factor, and $zT$ value of a two-layer system from a simple model and elucidate the origin and size of the contribution of each layer to the total thermoelectric performance. We further show that the influence of the substrate can lead to large deviation between the measured and the film’s properties, advising caution when analyzing such systems. Moreover, this model allows one to ensure that the contribution of the substrate is below a desired threshold by introducing material-related quantities ${\epsilon }_{\sigma }$ and ${\epsilon }_{\lambda }$.

Quantum machine learning has the potential to provide powerful algorithms for artificial intelligence. The pursuit of quantum advantage in quantum machine learning is an active area of research. For current noisy intermediate-scale quantum computers, various quantum-classical hybrid algorithms have been proposed. One such previously proposed hybrid algorithm is a gate-based variational embedding classifier, which is composed of a classical neural network and a parameterized gate-based quantum circuit. We propose a quantum variational embedding classifier based on an analog quantum computer, where control signals vary continuously in time: our particular focus is an implementation using quantum annealers. In our algorithm, the classical data are transformed into the parameters of the time-varying Hamiltonian of the analog quantum computer by a linear transformation. The nonlinearity needed for a nonlinear classification problem is purely provided by the analog quantum computer through the nonlinear dependence of the final quantum state on the control parameters of the Hamiltonian. We perform numerical simulations that demonstrate the effectiveness of our algorithm for performing binary and multiclass classification on linearly inseparable datasets such as concentric circles and MNIST digits. Our classifier can reach accuracy comparable with that of the best classical classifiers. We find that the performance of our classifier can be increased by increasing the number of qubits, until the performance saturates and fluctuates. Moreover, the number of optimization parameters of our classifier scales linearly with the number of qubits. The increase of the number of training parameters when the size of our model increases is therefore not as fast as that of a neural network. Our algorithm presents the possibility of using current quantum annealers for solving practical machine-learning problems, and it could also be useful to explore quantum advantage in quantum machine learning.

In view of the long-standing controversy over the surface ferromagnetism of $\mathrm{Fe}\mathrm{O}$, we propose to prepare its nanoparticles based on the lithium-battery reaction mechanism and carry out operando magnetometry measurements. This operando magnetoelectrochemical technology allows accurate magnetic detection of electrochemical synthesis products, effectively avoiding various intractable surface reactions during sample transmission processes. Due to the large surface area of nanosized $\mathrm{Fe}\mathrm{O}$, the magnetometry measurements show strong ferromagnetism and a high Curie temperature of 754 K, which can be attributed to surface frustration and the surface-bulk exchange interaction. These critical results confirm the surface ferromagnetism of $\mathrm{Fe}\mathrm{O}$ and provide valuable insights toward the design of spintronic devices.

Superconducting interferometers are quantum devices able to transduce a magnetic flux into an electrical output with excellent sensitivity, integrability, and power consumption. Yet, their voltage response is intrinsically nonlinear, a limitation which is conventionally circumvented through the introduction of compensation inductances or by the construction of complex device arrays. Here we propose an intrinsically linear flux-to-voltage mesoscopic transducer, exploiting the superconducting quantum interference proximity transistor (SQUIPT) as a fundamental building block, called bi-SQUIPT. It provides a voltage-noise spectral density as low as approximately ${10}^{-16}{\mathrm{V}/\mathrm{Hz}}^{1/2}$ and, more interestingly, under a proper operation parameter selection, exhibits a spur-free dynamic range as large as approximately $60$ dB, a value on par with that obtained with state-of-the-art linear flux-to-voltage superconducting transducers based on superconducting quantum interference devices (SQUIDs). Furthermore, thanks to its peculiar measurement configuration, the bi-SQUIPT is tolerant to imperfections and nonidealities in general. For the above reasons, we believe that the bi-SQUIPT could provide a relevant step beyond in the field of low-dissipation and low-noise current amplification with a special emphasis on applications in cryogenic quantum electronics.

The architecture of artificial atoms coupled to superconducting cavities allows the study of light-matter interactions and intriguing phenomena such as cavity gain implying photon generation. Here, we integrate a high-impedance cavity with a double quantum dot (DQD) in GaAs/(Al,Ga)As heterostructures, achieving a considerable DQD-cavity coupling strength and a relatively small cavity decay rate. By applying a strong drive to the DQD, we realize a population inversion and observe the cavity amplitude gain in multiple regions in the measured Landau-Zener-Stückelberg-Majorana interference pattern. We further systematically investigate the dependence of cavity gain on the driving frequency and tunnel coupling strength of the DQD. The results show that the cavity gain is tunable, with a maximum value of approximately $1.16$ in the measured range. Our experimental results are in good agreement with theoretical simulations and may provide an opportunity to implement on-chip microwave sources or microwave amplifiers in a controllable way.

We report on the development of a system called velocimetry by actively stabilized coherent optical transfer (VASCOT) that uses active optical phase tracking to measure the in-line velocity of retroreflecting targets with narrow-band photodetection. VASCOT is experimentally demonstrated over a 584-m atmospheric link to a corner-cube retroreflector (CCR) on an airborne drone. The in-line target-velocity measurement achieves residual uncertainties below $2{\text{nm s}}^{-1}$ within 5 s of averaging. Cycle-slip-free phase tracking over the full 3-min experiment allows the relative target range to be tracked with a one-standard-deviation uncertainty of 39.5 nm. VASCOT also provides an absolute target-range measurement with a statistical error of $\pm 13.7\mathrm{mm}$, which agrees with an independent Global-Positioning-System- (GPS) derived range measurement to within the GPS uncertainty.

The energy loss of hole-charge transfer in nonfullerene-acceptor (NFA) organic solar cells has been significantly reduced by minimizing their interfacial energy offset to near-zero and negative values. However, the physical mechanism of highly efficient hole-charge transfer in these conditions has not been fully addressed. In this work, we theoretically clarify that a moving hole charge along the polymer-donor and NFA interface, generated from the spontaneous dissociation of NFA molecular excited states, tends to transfer from NFA molecules to polymer-donor to keep moving, even their interfacial energy offset is chosen as a negative value. This dynamic viewpoint revises the conventional static viewpoint about the dependence of hole-charge transfer upon the interfacial energy offset. In addition, considering the polaron effect in organic molecules with a hole charge generated, we redefine the interfacial energy offset and further consider the effects of the NFA molecular electron-lattice interaction and lattice elastic constant on the hole-charge transfer.

An optical lattice is a platform that is used in studies in multiple branches of physics, e.g., ultracold atoms and microfluidic sorting. Unlike traditional complex optical systems, surface plasmon polaritons enable the generation of optical lattices on a chip. Here, we propose an on-chip scheme to transform five topological optical lattices using tunable polarization and a phase vortex. Using the dipolar approximation and numerical simulations, we demonstrate that the spin density force is the main force for sorting chiral particles in the out-of-plane direction when their radius is more than 100 nm. A microflow system is proposed to trap particles with electric optical lattices and sort chiral particles with spin optical lattices. Our findings offer possible routes toward realization of on-chip optical lattices and enable their potential application in both biology and chemistry, e.g., in simultaneous trapping or excitation of multiple objects and identification and sorting of chiral objects.

Quantum nondemolition (QND) measurement enhances the detection efficiency and measurement fidelity, and is highly desired for its applications in precision measurements and quantum information processing. We propose and demonstrate a QND measurement scheme for the spin states of laser-trapped atoms. On ${}^{171}\mathrm{Yb}$ atoms held in an optical dipole trap, a transition that is simultaneously cycling, spin-selective, and spin-preserving is created by introducing a circularly polarized beam of a control laser to optically dress the spin states in the excited level, while leaving the spin states in the ground level unperturbed. We measure the phase of spin precession of $5\times {10}^4$ atoms in a bias magnetic field of 20 mG. This QND approach reduces the optical absorption detection noise by $\sim 19\mathrm{dB}$, to an inferred level of 2.3 dB below the atomic quantum projection noise. In addition to providing a general approach for efficient spin-state readout, this all-optical technique allows quick switching and real-time programming for quantum sensing and quantum information processing.

Simultaneously tracking and imaging moving objects has been an open problem, due to the requirement on a large amount of information and the low photon flux during the very moment that the object can be taken as stationary. Information of the moving object can be decomposed into motion information of the centroid and image of the object. The amount of motion information can be far less than that for imaging. Therefore, we propose to solve the problem by information segregating. That is, to obtain the motion trajectory and gradually achieve the image during the tracking process. As an example, we unify tracking and imaging within a single system, based on ghost imaging with a four-quadrant detector. The differential signals from the quadrant detector are used for tracking, and the sum signals are for imaging. Experimental results show that our method provides high tracking accuracy and high-quality imaging. For objects of angular velocity up to 165 mrad/s, the tracking accuracy reaches 1/7 of the imaging resolution. Possible influences from different practical factors are also discussed.

The two-dimensional intrinsic ferromagnet $\mathrm{Cr}\mathrm{S}\mathrm{Cl}$ monolayer has considerable potential for application in the development of spintronic devices because of properties such as robust ferromagnetic ordering, large spin polarization, high Curie temperature, and high carrier mobilities. Here, we investigate the electromagnetic properties of the $\mathrm{Cr}\mathrm{S}\mathrm{Cl}$ monolayer and the spin-transport properties of some conceptual magnetic devices we construct, such as p-n-junction diodes, field-effect transistors, and phototransistors, by means of first-principles calculations. The results indicate that the p-n-junction diodes of the $\mathrm{Cr}\mathrm{S}\mathrm{Cl}$ monolayer exhibit full spin-polarized transport behavior, and both the p-n and p-i-n junctions show excellent spin-filtering behavior. The phototransistor of the $\mathrm{Cr}\mathrm{S}\mathrm{Cl}$ monolayer exhibits spin-resolved photoresponse characteristics for different wavelengths of light. Furthermore, it possesses the ability to generate a fully spin-up polarized current in the visible range. Our results provide key insights into the fundamental physical properties and the underlying transport and photoresponse mechanisms of the $\mathrm{Cr}\mathrm{S}\mathrm{Cl}$ monolayer.

We propose a quantum algorithm for simulating dissipative waves in inhomogeneous linear media as a boundary-value problem. Using the so-called quantum singular value transformation (QSVT), we construct a quantum circuit that models the propagation of electromagnetic waves in a one-dimensional system with outgoing boundary conditions. The corresponding measurement procedure is also discussed. Limitations of the QSVT algorithm are identified in connection with the large condition numbers that the dispersion matrices exhibit at weak dissipation.

The design and implementation of accelerators capable of providing high-quality bunches require precise and efficient online modeling tools. Current comprehensive beam dynamics studies are prohibitively costly and challenging to use for interactive system design. A precise high-speed method for beam dynamics analysis in accelerator components is presented and compared to the results of the conventional particle-in-cell codes. Using powerful mathematical techniques, the suggested method evaluates the temporal evolution of a bunch shape in six-dimensional (6D) phase space along the accelerators. The moment equations that govern the evolution of the bunch envelope in 6D phase space are introduced. The three-dimensional space-charge, external, and emittance forces are calculated to be fully analytically insensitive to different beam envelopes. Substituting the obtained forces into the beam-envelope equations establishes a set of six modified equations describing the beam dynamics using simple algebraic expressions. The whole solution considers the energy spread inherent to an electron beam. The model accuracy is demonstrated by studying beam transport through various components of an accelerator. Applying this analytical approach not only forms a style of physical thinking by indicating the factors that affect the behavior of the charged particle bunches but also has an ultrafast computational speed that is at least 3 orders of magnitude faster than that of particle tracking codes for designing today’s linear accelerators. Finally, the model’s feasibility is benchmarked for successfully designing a photoinjector for the advanced proton driven plasma wakefield acceleration experiment.

We develop a code to extract the signal-to-noise ratio (SNR) arising from the magnetic film in a recording medium. The approach allows us to separate the remanence and transition contributions from the global spatial noise. The results are in excellent agreement with the analysis performed on the same data sets by means of Seagate proprietary software based on ensemble wave-form analysis. We then apply this analytical approach to the results of heat-assisted magnetic recording (HAMR) dynamics simulations by means of the open-source multi-time-scale micromagnetic code mars and compare these with experimental spin-stand measurements of analogous systems. The proposed model could be used as the standard tool to understand the underlying physics of the noise components affecting HAMR operations and how to decrease the noise arising from the medium to improve the writing performance of HAMR.

We investigate targeted and localized material modifications produced by electropulsing of $\mathrm{Al}$-capped $\mathrm{Nb}$ microbridges with a multiterminal configuration. The affected regions of the $\mathrm{Nb}$/$\mathrm{Al}$ bilayer terminals are revealed by an in-lens secondary-electron detector in a scanning electron microscope and by Kelvin-probe force microscopy, both suggesting a decrease in the work function in the modified areas. In contrast, the affected areas are neither apparent through an Everhart-Thornley secondary-electron detector nor through atomic force microscopy, which indicates little morphological change in the microstructure. In addition, we demonstrate that the extent of the electroannealed regions is strongly influenced by the terminal geometry. These results are captured by complementary finite-element modeling, which permits us to estimate that a threshold temperature of $435\pm 35$ K is needed to induce material modifications. These findings provide further insights into the subtle modifications produced by gentle electroannealing of $\mathrm{Nb}$/$\mathrm{Al}$ microstructures and represent a step forward towards mastering this emerging nanofabrication technique.

A reconfigurable phononic crystal (PnC) is proposed where elastic properties can be modulated by rotation of asymmetric solid scatterers immersed in water. The scatterers are metallic rods with a cross section of ${120}^{\circ }$ circular sector. Orientation of each rod is independently controlled by an external electric motor that allows continuous variation of the local scattering parameters and dispersion of sound in the entire crystal. Due to asymmetry of the scatterers, the crystal band structure possesses highly anisotropic band gaps. Synchronous rotation of all the scatterers by a definite angle changes the regime of reflection to the regime of transmission and vice versa. The same mechanically tunable structure functions as a gradient index medium by incremental, angular reorientation of rods along both row and column, and, subsequently, can serve as a tunable acoustic lens, an acoustic beam splitter, and finally an acoustic beam steerer.

Scalable technologies for the production of biocompatible complex microfibers of controllable size and composition at competitively high throughput are urgently needed in order to meet the growing demand for such microstructures in pharmaceutical and biomedical applications. Here, we introduce an in-air microfluidic strategy with throughput greater than $1400$ ml $\mathrm{h}{}^{-}1$ (corresponding to more than $17000$ m of fiber per hour). The microfibers of uniform diameter have regular inclusions, which can potentially be used for encapsulating cells into a protecting and nutrient environment, or for finely tuning the release of various actives at individualized doses. With the help of a recently developed prototype, we test seven different liquid combinations and obtain seven types of fibers, whose average “dry” diameter ranges between $94\text{μ}\mathrm{m}$ and $170\text{μ}\mathrm{m}$. The principle of our approach is to solidify the complex liquid structures generated by the controlled collisions of a drop stream with a continuous liquid jet, in air, via ionic cross-linking. After the stream of water-based droplets, which constitute the inclusions, collides in-air with the alginate-based jet (jet 1), the generated drops-in-jet compound is brought into contact with a second jet (jet 2) containing divalent cations (${\mathrm{Sr}}^{2+}$ or ${\mathrm{Ca}}^{2+}$) to initiate the solidification. Finally, the fibers are collected “on the fly” via a horizontal spinning plate, allow to dry (i.e., to fully equilibrate under controlled conditions), and characterized by their elongation at break and Young’s modulus.

We report a theoretical study of the phase diffusion in a gain-switched single-mode semiconductor laser. We use stochastic rate equations for the electric field to analyze the phase statistics of the gain-switched laser. Their use avoids the instabilities obtained with rate equations for the photon number and optical phase when the photon number is small. However, we show that a new problem appears when the field equations are integrated: the variance of the optical phase becomes divergent. This divergence cannot be observed with the numerical integration of the commonly used equations for the photon number and optical phase because of the previous instabilities. The divergence of the phase variance means that this quantity does not reach a fixed value as the integration time step is decreased. We find that the phase variance increases as the integration time step decreases, with no sign of saturation behavior even for tiny steps. We explain the divergence by making the analogy of our problem with two-dimensional Brownian motion. The fact that the divergence appears is not surprising because in 1940 Paul Lèvy demonstrated that the variance of the polar angle in two-dimensional Brownian motion is a divergent quantity. Our results show that stochastic rate equations for the photon number and phase are not appropriate for describing the phase statistics when the photon number is small. Simulation of the stochastic rate equations for the electric field are consistent with Lèvy’s results but gives unphysical results since an infinite value is obtained for a quantity that can be measured.