Liquid-phase quartz crystal microbalance (QCM) biosensors are commonly quantified using scalar readouts such as frequency shift, dissipation, or bandwidth. Although useful, these descriptors discard resonance line-shape morphology that carries additional information for quantitative inference. Here, we present a machine-learning-assisted impedance line-shape workflow for temporally robust quantitative 10 MHz QCM biosensing. A passive microfluidic mixer generated controlled glycerol-water concentration gradients under constant total flow while complex impedance spectra were acquired in real time, and each sweep was parameterized using constrained Gaussian/Lorentzian models to obtain 52 physically interpretable descriptors spanning resistance, reactance, impedance magnitude, admittance components, and phase. The pipeline combines consensus outlier handling, training-fold mRMR feature ranking, and regression models evaluated under both shuffled five-fold cross-validation and a stricter temporally blocked protocol in which contiguous portions of the experiment are held out. The workflow achieved R2=0.948 under shuffled validation and R2=0.823 under seven-block temporal validation, the latter reducing prediction error by 48.4% relative to a row-matched Kanazawa frequency-shift baseline evaluated on the same sweeps. Feature-set ablation indicated that phase and conductance line-shape descriptors carry particularly strong temporally generalizable information. Applied to an experimental DNA hybridization assay in 1× PBS, the workflow reduced mean absolute percentage concentration error from 21.02% to 10.73%, a 48.9% improvement over conventional Δf calibration. Retaining resonance morphology therefore improves quantitative QCM biosensing beyond scalar frequency-shift calibration and provides an interpretable basis for AI-assisted electronic biosensors.

Terahertz (THz) metasurfaces with efficient and electrically reconfigurable electromagnetic responses are highly desired for emerging wireless communication systems. However, realizing compact solid-state active THz devices with multiband modulation capability remains challenging. Here, we demonstrate a dual-band electrically reconfigurable THz metasurface enabled by AlGaN/GaN high-electron-mobility transistors (HEMTs). The dual-band response originates from two cavity modes supported by the reflective metal-insulator-metal (MIM) structure, while the gate-controlled two-dimensional electron gas (2DEG) channel tunes the effective surface admittance of the HEMT-integrated metasurface. The fabricated device achieves reflection amplitude modulation depths of 59% and 94% at 0.245 and 0.429 THz, respectively, together with maximum phase shifts of 35[∘] and 180[∘] at 0.238 and 0.425 THz. This work extends AlGaN/GaN HEMT-based THz modulation toward dual-band operation, offering enhanced frequency-domain flexibility for wireless communications and spectroscopic sensing.

Ringing of piezoelectric transducers complicates time-domain ultrasound measurements by increasing pulse length, narrowing bandwidth, and producing a 'dead zone' in the pulse envelope that obscures near-field measurements. This leads to decreased axial resolution, and a reduction of the region over which the ultrasound measurements are sensitive. Ringing is caused by resonant behavior of the transmitter, but can also occur from resonances within or between test objects, the receiver, and/or other components. This paper focuses on transducer ringing caused by radial modes of disc-shaped transmitters, but the techniques described are generalizable to other transmitter geometries. We eliminate ringing by making careful adjustments to the excitation pulse, and we call this 'pulse shaping'. We bring concepts from control systems and system identification theory, especially areas concerning feedforward control and input shaping. We implement a procedure that identifies a mechanical systems model from the electrical admittance spectrum of the transmitter, and then computes pulse shaping filters that are capable of canceling multiple ringing modes simultaneously. We include an in-depth review of previous work, an overview of applicable techniques, and experimental results section for various filter designs.

Bulk acoustic wave devices commonly use thickness modes of piezoelectric plates to generate standing waves in microfluidic cavities, typically at 2 MHz and above, with corresponding wavelengths well below the millimeter scale. However, handling larger mesoscopic objects-such as organoids and small model organisms, including nematodes-requires millimeter-scale fluidic structures. Single-node alignment in such cavities demands half-wavelength fields and thus sub-megahertz actuation. Conventional sub-megahertz operation requires thicker piezoelectric plates, resulting in bulky designs or requiring plate thicknesses that are not readily available commercially. Here, we demonstrate a practical yet elegant alternative based on a low-order lateral extensional mode of the transducer plate. Compared with thickness-mode devices of identical footprint, the approach lowers the operating frequency approximately fourfold and increases the channel cross section sixteenfold. Unlike typical Lamb-wave devices based on multiwavelength propagation at several megahertz and above, the present approach uses a low-order standing S0 Lamb mode. The numerically optimized devices operate at approximately 540 kHz and achieve rapid particle focusing in millimeter-wide channels, while vibrometry confirms the predicted vibration patterns and admittance measurements show that the lateral resonance is well preserved upon chip loading, with only a small, predictable shift in resonance frequency. The present study focuses on modal characterization and particle-focusing validation. Practical limitations include heating at high actuation voltages and the finite lifetime of the liquid acoustic coupling layer.

This research developed a novel crack localization technique that employed cross-electromechanical admittance (EMA), and its validity was verified through both analytical simulations and experimental measurements. Initially, an analytical model of the cross-EMA was established based on the transfer matrix method (TMM), and subsequently verified by experimental data and finite element analysis. Using the established model, the cross-EMA signatures were then utilized for crack localization in conjunction with a damage index defined as the correlation coefficient (CC). Furthermore, experimental investigations were carried out on five steel specimens, each of which was instrumented with multiple surface-bonded Lead Zirconate Titanate (PZT) wafers in a distributed manner. A crack with a width of 1.5 mm and a depth of 8 mm was introduced into each specimen to simulate structural damage. Both analytical investigations and experimental research have demonstrated that the cross-EMA signatures exhibit crack localization capabilities and can precisely reflect the local mechanical characteristics of the monitored structure.

ObjectiveThis study tested whether therapeutic massage techniques demonstrated by a single experienced practitioner can be encoded as structured kinematic-dynamic signatures and reproduced robotically under adaptive admittance control.MethodsSingle-expert manual demonstrations of four mechanically distinct techniques - beating, pressing, pushing, and vibrating - were synchronously recorded using optical motion capture and tri-axial force sensing. Each technique was acquired as a 30-s continuous trial; after predefined screening, one stable analysis segment was retained per technique (Beat 8 s, Press 20 s, Push 20 s, and Vibrate 5 s). The retained demonstrations were characterized by active Cartesian degrees of freedom, movement excursion and timing, loading direction, force amplitude, and dominant frequency. These descriptors were mapped to technique-specific nominal robot trajectories, while adaptive admittance generated contact-direction corrections during execution. Reproduction fidelity was summarized descriptively by DTW-aligned waveform correlation, mean-force bias, relative mean-force bias, peak-force error, and dominant-frequency difference.ResultsAfter DTW alignment, robotic executions preserved force waveform morphology with high descriptive similarity across techniques (Pearson r = 0.858-0.925; Spearman rho = 0.817-0.907). Descriptive error metrics identified two technique-dependent mismatch patterns. Press and Push retained the principal cyclic or loading morphology but showed marked mean-force bias (+8.25 N, +33.55% for Press; -23.38 N, -43.66% for Push). Beat and Vibrate showed smaller absolute mean-force offsets (+1.79 N and -1.54 N, respectively), although Beat still exhibited a relative mean-force bias of +21.39%. Beat and Vibrate also showed larger dominant-frequency differences (-1.25 and -1.80 Hz, respectively). Peak-force error remained within ±2.11% for all four techniques.ConclusionUnder controlled biomimetic-interface conditions, single-expert massage demonstrations could be encoded as interpretable kinematic-dynamic signatures and reproduced robotically with high temporal waveform similarity, although force-amplitude and rhythm reproduction remained technique dependent. These findings support the feasibility of the expert-to-robot representation and execution framework. Broader reproducibility and clinical relevance require validation with multiple practitioners, repeated trials, dedicated controller-performance experiments, and human participants.

Electric double-layer capacitors (EDLCs) rely on the dynamical response of confined electrolytes to store and release charge, yet the interplay between ion transport, electrostatic interactions, and electrode metallicity remains poorly understood at the nanoscale. In this work, we develop a comprehensive Brownian dynamics framework to compute the frequency-dependent admittance of nanocapacitors, explicitly accounting for salt concentration and the finite screening length of electrodes (modeled via Thomas-Fermi theory). We derive the fluctuation-dissipation relation connecting the dynamics of equilibrium charge fluctuations to the linear response of the system quantified by the frequency-dependent admittance. In particular, we obtain two estimators for the admittance-based on ionic positions and forces-and combine them via a control variate method to reduce statistical uncertainty across all frequencies. Our simulations show that the admittance exhibits a low-frequency regime dominated by capacitive effects and a high-frequency one governed by the ideal Nernst-Einstein conductivity. The crossover between these regimes is characterized by a timescale that depends on both the electrode metallicity and salt concentration, highlighting the role of ion-wall collisions and electrostatic interactions. Comparisons with analytical models show that while mean-field theories capture qualitative trends, they systematically overestimate low-frequency admittance and underestimate high-frequency behavior, underscoring the necessity of explicit ion-ion and ion-wall interactions. This work connects microscopic dynamics to macroscopic electrochemical observables, offering a tool to interpret impedance spectra in nanoscale systems. Beyond charge storage in EDLCs, our framework provides insights for sensing applications in nanofluidic devices, where charge/current fluctuations enable the detection of electrochemically active species.

Accurate knowledge of acoustic surface admittance or impedance is essential for reliable wave-based simulations, yet its in situ estimation remains challenging due to noise, model inaccuracies, and restrictive assumptions of conventional methods. This work presents a physics-informed neural operator approach for estimating frequency-dependent surface admittance directly from near-field measurements of sound pressure and particle velocity. A deep operator network is employed to learn the mapping from measurement data, spatial coordinates, and frequency to acoustic field quantities, while simultaneously inferring a globally consistent surface admittance spectrum without requiring repeated evaluations of an explicit numerical forward model during inversion. The governing acoustic relations, including the Helmholtz equation, the linearized momentum equation, and Robin boundary conditions, are embedded into the training process as physics-based regularization, enabling physically consistent and noise-robust predictions. The method is validated using synthetically generated data from a simulation model for two planar porous absorbers under semi free-field conditions across a broad frequency range. Results demonstrate accurate reconstruction of both real and imaginary admittance components and reliable prediction of acoustic field quantities. Parameter studies confirm improved robustness to noise and sparse sampling compared to purely data-driven approaches, highlighting the potential of physics-informed neural operators for in situ acoustic material characterization.

Adaptive wireless power transfer (WPT) systems for wearable and biomedical applications require continuous knowledge of the magnetic coupling coefficient to maintain stable power delivery under motion-induced variations. Conventional approaches rely on auxiliary sensors or dedicated RF measurement stages, which increase system complexity and limit integration in compact platforms. This paper introduces a sensorless coupling estimation method based on the input reflection phase of a Chireix outphasing combiner operating in in-phase mode (φ=0°). The reflection phase ∠Γin exhibits a numerically monotonic dependence on the coupling coefficient over the wearable-relevant range k∈[0.05,0.70], arising from a structural decoupling between coupling-dependent conductance and fixed susceptive compensation in the combiner input admittance. The method enables coupling estimation using only intrinsic RF observables at the transmitter port, without interrupting power transfer or requiring additional sensing hardware, via a two-step protocol combining sub-millisecond sensing with coupling-aware outphasing control. Validation combines analytical modeling, numerical evaluation, Monte Carlo analysis under component tolerances, and circuit-level S-parameter simulation at 13.56 MHz, demonstrating robust monotonic behavior under component variations, phase noise, and resonant detuning. The framework reinterprets the Chireix combiner as a dual-function structure enabling simultaneous power combining and coupling observability, providing a low-overhead solution for adaptive wearable WPT systems.

This paper proposes a hybrid matrix method (HMM) to predict the response of multilayered structures including poroelastic media. Hybrid matrices are a generalization of impedance and admittance matrices. It is shown that they are more convenient than the latter to deal with coupling between layers composed of media of different types. They are then a powerful tool to solve multilayered structure problems with any kind of interface. HMM is in addition designed to be stable thereby overcoming the major drawback of the transfer matrix method. The proposed method is also shown to be simpler and more efficient than a recursive method that was previously proposed. Various post-processing tools (finite-size effects, computation of dissipated powers) are also presented so as to illustrate its interest.

A long-standing challenge in electromagnetic dissipative systems is that dissipation and phase evolution are fundamentally entangled through the underlying admittance dynamics, leading to oscillatory and path-dependent response trajectories under tuning. In this Letter, we identify an admittance-scaling mechanism that restructures the evolution pathway of the effective input admittance, enabling broadband dissipation control through magnitude scaling while maintaining an approximately invariant phase manifold. The resulting response evolution avoids the oscillatory behavior associated with conventional phase-mediated tuning and instead follows an ordered broadband trajectory. This admittance-scaling framework establishes a general route toward state-continuous manipulation of electromagnetic dissipation and tunable wave-control systems.

ObjectivePeople in prison have high rates of self-harm and suicide but little is known about the patterns of occurrence over time in custodial settings. Rates of psychiatric diagnoses in custodial settings are also high, and strong associations have been found between psychiatric diagnoses and risk of self-harm and suicidality.MethodsIn this short report, we present a study examining 3 years of data (2020-2023) from a mental health unit within a prison in New South Wales, Australia. We describe the characteristics of the cohort and report rates of self-harm and suicidal behaviours during the admittance.ResultsOverall, 20% of the cohort reported thoughts of self-harm at least once during their stay, and 9% of the cohort had at least one recorded self-harm incident. Multivariable binary logistic regression analyses revealed greater odds of self-harm for individuals with a recorded history of self-harm or suicidal behaviour and for those who reported thoughts of self-harm to staff during their stay.ConclusionsFindings from our study highlight the high rates of self-harm behaviour and ideation among people with mental illness in a custodial setting, supporting the need for targeted prevention and aftercare strategies.