Papers

The massive volume of oil sands tailings has been one of the most challenging environmental issues. In this work, we experimentally explore a simple and effective approach to bitumen residue separation from a highly concentrated slurry flow of the artificial oil sands tailings. By utilizing microbubbles from in-situ catalytic decomposition of H2O2 at low concentrations, bitumen aggregation is enhanced on the top part of the hydrotransport pipeline. The microscopic image analysis revealed the in-situ formation of microbubbles and confirmed that magnetic particles present in the slurries contributed to the fast release of the gas products and bubble formation from hydrogen peroxide decomposition. A high-speed camera was applied to capture images of the tailings flow in the pipeline through a transparent view window. A large number of tiny bubbles were identified post to the injection of H2O2 to the slurry flow. More than 70 % bitumen could be recovered from a lab-scale pipeline loop within 30 mins after injection. The bitumen recovery efficiency from the collected froth was quantitatively compared under seven conditions with varied dosages, the concentration of H2O2, and the amount of magnetic solids in the slurries. Our results confirmed that the total dosage of H2O2 is the dominant factor in in-situ microbubble formation for enhanced bitumen aggregation in the flow. Importantly, microbubbles were generated rapidly in the real mature fine tailings. The results from our study provide insights into the preferential distribution of oil residue in the flow during hydrotransport without the requirement for an additional device. Removal of oily residues from concentrated slurries may bring economical and environmental advantages.

Our study investigates the segregation of bitumen residues within the transport pipeline before disposal in the presence of coal particles in carriers and microbubbles. Coal particles decreased the bitumen recovery by 17% without the injection of microbubbles. In addition, the improvement in bitumen recovery efficiency by 6 mL of H2O2 is negligible due to a small number of bubbles formed from H2O2 decomposition in the flow. However, tremendous enhancement in the recovery efficiency was achieved with the simultaneous addition of coal particles and H2O2. Further increase in recovery was noted as a larger volume of H2O2 was injected to form more microbubbles. Computational fluid dynamics (CFD) simulations were conducted to help understand the effects of coal particles and microbubbles. The simulation results illustrated that the introduction of coal particles caused bitumen contents to accumulate in the middle of the pipe. Furthermore, an increased volume fraction of microbubbles contributed to a higher distribution of bitumen at the top of the pipe. This study not only offers valuable insights for developing an innovative strategy to enhance the efficiency of bitumen separation in hydrotransport processes but also contributes to a deeper understanding of the intricate interactions among bubbles, bitumen, and coal particles in a slurry flow.

The formation and evolution of galaxies and other astrophysical objects have become of great interest, especially since the launch of the James Webb Space Telescope in 2021. The mass, size, and density of objects in the early universe appear to be drastically different from those predicted by the standard cosmology - the $\Lambda$CDM model. This work shows that the mass-size-density evolution is not surprising when we use the CCC+TL cosmology, which is based on the concepts of covarying coupling constants in an expanding universe and the tired light effect contributing to the observed redshift. This model is consistent with supernovae Pantheon+ data, the angular size of the cosmic dawn galaxies, BAO, CMB sound horizon, galaxy formation time scales, time dilation, galaxy rotation curves, etc., and does not have the coincidence problem. The effective radii $r_e$ of the objects are larger in the new model by $r_e \propto (1+z)^{0.93}$. Thus, the object size evolution in different studies, estimated as $r_e \propto (1+z)^s$ with $s=-1.0 \pm {0.3}$, is modified to $r_e \propto (1+z)^{s+0.93}$, the dynamical mass by $(1+z)^{0.93}$, and number density by $(1+z)^{-2.80}$. The luminosity modification increases slowly with $z$ to 1.8 at $z=20$. Thus, the stellar mass increase is modest, and the luminosity and stellar density decrease are mainly due to the larger object size in the new model. Since the aging of the universe is stretched in the new model, its temporal evolution is much slower (e.g., at $z=10$, the age is about a dex longer); stars, black holes, and galaxies do not have to form at unrealistic rates.

Photoreduction of cryptochrome protein in the retina is a well-known mechanism of navigation of birds through the geomagnetic field, yet the biosignal nature of the mechanism remains unclear. The absorption of blue light by the flavin adenine dinucleotide (FAD) chromophore can alter the distribution of electrons in cryptochrome and create radical pairs with separated charges. In this study, the spin dynamics of electrons in the radical pair including its spin-orbit coupling were investigated by quantum mechanical modeling. Spin-orbit coupling is negligible relative to other terms and has no significant role in the dynamics. However, it engages the spatial states of the radical pair and make possible to study spatial related observables. Several interactions were considered in the presence of an external magnetic field, and the resulting electric dipole moment in cryptochrome was computed as the quantity emerging from this coupling. The computations show the induced electric dipole moment clearly depend on the characteristics of the applied magnetic field even after considering dissipative effects. In fact, our findings indicate that the radical pair in cryptochrome protein is a magnetic biosensor, in the sense that in the presence of the geomagnetic field, variations in spin states can influence its electric dipole moment, which may be interpreted via the bird as an orientation signal. The results can be used in the advancement of bio-inspired technologies which replicate animal magnetic sensitivity.

We derive analytic formulas to reconstruct particle-averaged quantities from experimental results that suffer from the efficiency loss of particle measurements. These formulas are derived under the assumption that the probabilities of observing individual particles are independent. The formulas do not agree with the conventionally used intuitive formulas.

Domain wall memories have undergone several changes over the years for faster shift, read, and write operations; however, fundamental issues persist due to creating pinning sites topographically along the nanowire. The deformity in notches creates non-uniform pinning strength, leading to multiple faults during shift operation. This study proposes a novel approach to address these challenges and gain greater control over domain manipulation for advanced applications in Boolean and non-Boolean paradigms. Utilizing the magnetic topological insulator (MTI) can create pinning sites without potentially faulty topographical notches made through complex lithography. Moreover, applying an external current enables precise control over the pinning potentials at these sites. Micromagnetic simulations validate the effectiveness of MTI-based pinning sites, showcasing their potential for future applications. Our approach introduces an alternative method for creating pinning sites by cross-architecture of ferromagnetic nanowires and MTI nanobars, inducing exchange interaction at their intersection points. This method offers simplicity of fabrication and enables control over the pinning strength by external current.

Charged water drops are more widespread than commonly acknowledged. For example, raindrops typically carry charges of order Q ~ 1 pC, while routine pipetting in the laboratory produces drops with Q ~ 50 pC. Here, we show that such modest charging can spontaneously generate periodic Coulomb fissions for evaporating water drops on lubricated surfaces, with more than 60 successive cycles observed over 30 min. Interestingly, the underlying instability can be quantitatively predicted by two fissility thresholds: one marking the onset of drop elongation and another triggering fission. Each fission culminates with a fine liquid jet that disintegrates into 40-50 microdroplets, expelled within microseconds. The phenomenon spans an extraordinary range of length scales (from millimetres to microns) and time scales (hour to microseconds), with broad potential applications ranging from nanoscale fabrication to electrospray ionization.

Aerosol-cloud–radiation interactions remain among the most uncertain components of the Earth's climate system, in partdue to the high dimensionality of aerosol state representations and the difficulty of obtaining complete in situ measurements. Addressing these challenges requires methods that distill complex aerosol properties into compact yet physically meaningful forms. Generative autoencoder models provide such a pathway. We present a framework for learning deep variational autoencoder (VAE) models of speciated mass and number concentration distributions, which capture detailed aerosol size-composition characteristics. By compressing hundreds of original dimensions into ten latent variables, the approach enables efficient storage and processing while preserving the fidelity of key diagnostics, including cloud condensation nuclei (CCN) spectra, optical scattering and absorption coefficients, and ice nucleation properties. Results show that CCN spectra are easiest to reconstruct accurately, optical properties are moderately difficult, and ice nucleation properties are the most challenging. To improve performance, we introduce a preprocessing optimization strategy that avoids repeated retraining and yields latent representations resilient to high-magnitude Gaussian noise, boosting accuracy for CCN spectra, optical coefficients, and frozen fraction spectra. Finally, we propose a novel realism metric – based on the sliced Wasserstein distance between generated samples and a held-out test set – for optimizing the KL divergence weight in VAEs. Together, these contributions enable compact, robust, and physically meaningful representations of aerosol states for large-scale climate applications.

In hydrodynamic descriptions of lipid bilayers, the membrane is often approximated as being impermeable to the surrounding, solute-containing fluid. However, biological and in vitro lipid membranes are influenced by their permeability and the resultant osmotic forces – whose effects remain poorly understood. Here, we study the dynamics of a fluctuating, planar lipid membrane that is ideally selective: fluid can pass through it, while the electrically-neutral solutes cannot. We find that the canonical membrane relaxation mode, in which internal membrane forces are balanced by fluid drag, no longer exists over all wavenumbers. Rather, this mode only exists when it is slower than solute diffusion – corresponding to a finite range of wavenumbers. The well-known equipartition result quantifying the size of membrane undulations due to thermal perturbations is consequently limited in its validity to the aforementioned range. Moreover, this range shrinks as the membrane surface tension is increased, and above a critical tension the membrane mode vanishes. Our findings are relevant when interpreting experimental measurements of membrane fluctuations, especially in vesicles at moderate to high tensions.

This study numerically investigates the thermal sampling properties of QAOA, the Quantum Alternating Operator Ansatz which was generalized from the original Quantum Approximate Optimization Algorithm. Specifically, the ability of QAOA to sample from the Gibbs distribution, equivalently the Boltzmann distribution, defined by a classical Ising model, specifically a fully connected disordered spin glass (Sherrington-Kirkpatrick) model. We focus on two different QAOA mixers; the standard transverse field X mixer, and the Grover mixer. At a QAOA depth of one we examine, for a single full QAOA parameter search space period, the energy landscape, the Shannon entropy landscape of the QAOA probability distribution, and the tradeoff between Boltzmann distribution sampling temperature and error rate (how close to the true Boltzmann distribution is the QAOA distribution). We find that at very high temperatures one-round Grover mixer QAOA can sample from the Boltzmann distribution more accurately than the standard X mixer QAOA at one round. Both X mixer and Grover mixer depth one QAOA can serve as approximate Boltzmann distribution samplers, and how good this approximation is depends heavily on the QAOA angle choice.

Amorphous boron nitride (\textrm{$\alpha$}-BN) is a promising ultrathin barrier for nanoelectronics, yet the atomistic mechanisms governing its chemical stability remain poorly understood. Here, we investigate the structure-property relationship that dictates the oxidation of \textrm{$\alpha$}-BN using a combination of machine-learning molecular dynamics simulations and angle-resolved X-ray photoelectron spectroscopy. The simulations reveal that the film structure, controlled by synthesis conditions, is the critical factor determining oxidation resistance. Dense, chemically ordered networks with a high fraction of B-N bonds effectively resist oxidation by confining it to the surface, whereas porous, defect-rich structures with abundant homonuclear B-B and N-N bonds permit oxygen penetration and undergo extensive bulk degradation. These computational findings are consistent with experimental trends observed in \textrm{$\alpha$}-BN films grown by chemical vapour deposition. XPS analysis shows that a film grown at a higher temperature develops a more ordered structure with a B/N ratio nearer to stoichiometric and exhibits superior resistance to surface oxidation compared to its more defective, lower-temperature counterpart. Together, these results demonstrate that the oxidation resistance of \textrm{$\alpha$}-BN is a tunable property directly linked to its atomic-scale morphology, providing a clear framework for engineering chemically robust dielectric barriers for future nanoelectronic applications.

The process of dephasing during wave evolution has traditionally been viewed as an obstacle to localization, leading to diffusion even in strongly disordered Hermitian lattices. In contrast, here we demonstrate how the interplay of dephasing with non-Hermitian defects can be harnessed to engineer wave localization. Specifically, we identify a novel dynamical localization phenomenon characterized by wavefunction accumulation at the lattice's boundary due solely to dephasing, despite globally reciprocal couplings. Furthermore, we study the incoherent skin effect arising from coupling asymmetry, and investigate the interplay between these antagonistic localization mechanisms. By reframing dephasing from a hindrance into a tool, this study overturns established paradigms of wave localization and paves the way for novel approaches to controlling localization phenomena in non-Hermitian physics.

Life relies on a sophisticated metabolic molecular machinery that turns over high-energy molecules to evolve complex macromolecules and assemblies. At the molecular origin of life, such machinery was absent, implying the need for simple yet robust physical mechanisms to harvest energy from the environment and perform chemical work or produce chemical power. However, the mechanisms involved in harvesting energy from a macroscopic cyclic environment to drive chemical processes on the molecular scale remain elusive. In this work, we propose a theory that describes the kinetics of chemical reactions in a system subject to a cyclic reservoir with varying properties. We compare cycles of solvent (wet-dry cycles), with cycles of a component participating in a chemical reaction (reactant cycle). We find that for both wet-dry and reactant cycles, resonance frequencies exist at which the chemical power is maximal. We identify which cycle type is more beneficial in harvesting chemical power for different molecular interactions. Our findings of harvest efficiencies around ten percent suggest that the cyclic environment could have played a key role in catalyzing the metabolic molecular machinery at the molecular origin of life.

We revisit the classical Womersley solution for pulsatile viscous flow in a circular tube and reconstruct its full time-domain geometry from first principles. By combining harmonic decomposition with exact Bessel solutions, we derive a unified spectral spacetime analytical solution in which the instantaneous relationship between pressure gradient and velocity can be visualized as a loop in phase space. The enclosed loop area is shown to equal the mean hydraulic power per cycle, establishing an exact geometric-energetic identity that holds for arbitrary Womersley number and harmonic composition. We further show that higher harmonic content and increasing Womersley number induce topological transitions in these loops, producing cusps, self-intersections, and curvature hot spots that correspond to inertial-viscous phase dispersion. This framework provides a rigorous baseline for interpreting arterial pressure-flow loops, connecting frequency-domain impedance analysis to measurable time-domain geometry.

Measurements in the highly Lorentz-boosted regime provoke increased interest in probing the Higgs boson properties and in searching for particles beyond the standard model at the LHC. In the CMS Collaboration, various boosted-object tagging algorithms, designed to identify hadronic jets originating from a massive particle decaying to $\mathrm{b\overline{b}}$ or $\mathrm{c\overline{c}}$, have been developed and deployed across a range of physics analyses. This paper highlights their performance on simulated events, and summarizes novel calibration techniques using proton-proton collision data collected at $\sqrt{s}$ = 13 TeV during the 2016$-$2018 LHC data-taking period. Three dedicated methods are used for the calibration in multijet events, leveraging either machine learning techniques, the presence of muons within energetic boosted jets, or the reconstruction of hadronically decaying high-energy Z bosons. The calibration results, obtained through a combination of these approaches, are presented and discussed.

How long a stochastic process survives before leaving a domain depends not only on its intrinsic dynamics but also on how it is observed. Classical first-passage theory assumes continuous monitoring with absorbing boundaries (“kill-on-touch”). In practice, however, measurements are often taken at discrete times. Between two checks, a trajectory may leave and re-enter the domain without being detected. Under this stroboscopic rule (“kill-on-check”), exit statistics change qualitatively. We analyze one-dimensional Brownian motion confined to an interval of length $L$ and observed at frame intervals~$\Delta t$, with diffusive step scale $\sigma\sqrt{\Delta t}$. The dynamics collapse onto a single confinement ratio $\rho=L/(\sigma\sqrt{\Delta t})$. For boundary starts we obtain linear scaling of the mean number of frames until exit, while for bulk starts the survival is governed by the spectral gap of a one-step stroboscopic operator, leading to a quadratic law with linear corrections. These results identify the stroboscopic first-passage problem where the observation protocol itself reshapes the statistics of escape.

It is commonly accepted that phase singularities in refractive index sensors can provide highly sensitive detection. To address this issue, we studied the phase sensitivity and the limit of detection of Tamm photonic crystals used as temperature sensors, taken here as a model system by exploring critical coupling and its associated phase singularity. To finely tune the optical Tamm mode to critical coupling, the top metal layer is periodically nanostructured and controlled, enabling the investigation of optical Tamm resonances around phase singularities. We use a highly stable common-path interferometry setup based on digital holography, which allows us to measure extremely high phase sensitivity even at very low light intensities, while also providing information about reflectivity in the complex domain. We experimentally validate an analytical model based on Temporal Coupled Mode Theory, which fully explains the phase sensitivity response and the limit of detection of such resonant photonic sensors. We demonstrate that, although approaching a phase singularity drastically enhances phase sensitivity, no improvement of the limit of detection is expected. Furthermore, phase singularity has no effect on the limit of detection, and therefore on the efficiency of such sensors for practical applications. This work provides a comprehensive description of phase sensors operating at critical coupling, and challenges the commonly accepted assumption regarding the role of phase singularities in sensing.

Wave vortices constitute a large family of wave entities, closely related to phase singularities and orbital angular momentum (OAM). So far, two main classes of localized wave vortices have been explored: (i) transversely-localized monochromatic vortex beams that carry well-defined longitudinal OAM and propagate/diffract along the longitudinal $z$-axis in space, and (ii) 2D-localized spatiotemporal vortex pulses that carry the more elusive transverse (or tilted) OAM and propagate/diffract along both the $z$-axis and time. Here we introduce another class of wave vortices which are localized in a 2D $(x,y)$ plane, do not propagate in space (apart from uniform radial deformations), and instead propagate/diffract solely along time. These vortices possess well-defined transverse OAM and can naturally appear in 2D wave systems, such as surface polaritons or water waves. We provide a general integral expression for time-diffracting 2D wave vortices, their underlying ray model, and examples of approximate and exact wave solutions. We also analyze the temporal Gouy phase closely related to the rotational evolution in such vortices. Finally, we show that time-diffracting 2D vortices can provide strong spatiotemporal concentration of energy and OAM at sub-wavelength and oscillation-period scales.

This study proposes an Artificial Intelligence (AI) driven methodology for predicting a combination of brazed ceramic-metal composite materials. Multiple machine learning (ML) algorithms are compared with the deep learning (DL) model. The developed models are tested using k-fold validation. Nine different input-output feature configurations are evaluated to assess the model performance. The input-output feature comprises material properties, namely, the coefficient of thermal expansion (CTE) and molecular mass of brazed ceramic-metal composite materials obtained from literature and the strength parameter (average Von Mises Stress (VMS)) estimated from Finite Element Method (FEM) simulation for joint assembly structure. A multi-output model, Autoencoder (AE), has also been developed and tested to predict various features. The ML model, namely the polynomial regression (PR), outperforms the other ML/DL models with a Mean square Error (MSE) of 0.01 for the test data. The autoencoder model with a 32-16-32 structure outperforms LR, PR, RF, and ANN with an MSE of 0.04% for the prediction of unseen data. The developed multi-output model accurately predicts all the features (single and multiple), while PR fails to accurately predict multi-output features of low importance. The developed AE model predicts the different material properties with an average error of ~0.16-3.78% with literature-reported values.

Accurate prediction of the thermal and electrical conductivities of materials under extremely high temperatures is essential in high-energy-density physics. These properties govern processes such as stellar core dynamics, planetary magnetic field generation, and laser-driven plasma evolution. However, first-principles methods like Kohn-Sham (KS) density functional theory (DFT) face challenges in predicting these properties due to prohibitively high computational costs. We propose a scheme that integrates the Kubo formalism with a mixed stochastic-deterministic DFT (mDFT) method, which substantially enhances efficiency in computing thermal and electrical conductivities of dense plasmas under extremely high temperatures. As a showcase, this approach enables ab initio calculations of the thermal and electrical conductivities of Aluminum (Al) up to 1000 eV. Compared to traditional transport models, our first-principles results reveal significant deviations in the thermal and electrical conductivities of Al within the warm dense matter regime, underscoring the importance of accounting for quantum effects when investigating these transport properties of warm dense matter.

The measurement of Earth's free oscillations plays an important role in studying the Earth's large-scale structure. Space technology development presents a potential method to observe these normal modes by measuring inter-satellite distances. However, the disturbance from the Earth's low-degree gravity field makes it challenging for low Earth orbit gravity measurement satellites such as Gravity Recovery and Climate Experiment (GRACE) and TianQin-2 to extract signals from Earth's free oscillations directly. Here, we propose that by taking advantage of the high Earth orbit, the TianQin satellites can effectively avoid this disturbance, enabling direct measurement of Earth's free oscillations. We derive an analytical waveform to describe the response of Earth's free oscillations in TianQin. Based on this waveform, we use Bayesian analysis to extract the normal modes from numerical simulation data and perform parameter estimation. Our findings reveal that for a magnitude 7.9, Wenchuan-like earthquake, the resulting free oscillations will generate a signal that signal-to-noise ratio (SNR) is 73 in TianQin, and approximately 9 different modes can be distinguished. This result shows TianQin can open a new window to examine the Earth's free oscillations and study the Earth's interior and earthquakes independently from ground-based gravity measurement.

We analyze Parker Solar Probe and Solar Orbiter observations to investigate the propagation and dissipation of Alfvénic fluctuations from the outer corona to 1~AU. Conservation of wave-action flux provides the theoretical baseline for how fluctuation amplitudes scale with the Alfvén Mach number $M_a$, once solar-wind acceleration is accounted for. Departures from this scaling quantify the net balance between energy injection and dissipation. Fluctuation amplitudes follow wave-action conservation for $M_a < M_a^{b}$ but steepen beyond this break point, which typically lies near the Alfvén surface ($M_a \approx 1$) yet varies systematically with normalized cross helicity $\sigma_c$ and fluctuation scale. In slow, quasi-balanced streams, the transition occurs at $M_a \lesssim 1$; in fast, imbalanced wind, WKB-like scaling persists to $M_a \gtrsim 1$. Outer-scale fluctuations maintain wave-action conservation to larger $M_a$ than inertial-range modes. The turbulent heating rate $Q$ is largest below $M_a^{b}$, indicating a preferential heating zone shaped by the degree of imbalance. Despite this, the Alfvénic energy flux $F_a$ remains elevated, and the corresponding damping length $\Lambda_d = F_a/Q$ remains sufficiently large to permit long-range propagation before appreciable damping occurs. Normalized damping lengths $\Lambda_d/H_A$, where $H_A$ is the inverse Alfvén-speed scale height, are near unity for $M_a \lesssim M_a^{b}$ but decline with increasing $M_a$ and decreasing $U$, implying that incompressible reflection-driven turbulence alone cannot account for the observed dissipation. Additional damping mechanisms – such as compressible effects – are likely required to account for the observed heating rates across much of the parameter space.

The use of a Si pixel-based particle tracking scheme in muSR will, among others, allow measurements using a ten-fold increased stopped muons rate and samples ten times smaller than currently possible. Here we present simulation results to assess the effects of magnetic fields on two spectrometer configurations using a two-layered tracking scheme for the incoming and outgoing particles. At a low magnetic field of up to ~50 mT, the tracking and reconstruction accuracy is only minimally influenced. Beyond a magnetic field of ~80 mT the tracking capabilities diminish significantly. Operating a two-layered scheme using small magnetic fields hence does not require adaptations. Only at large magnetic fields, a tracking scheme that makes use of an accurate field map or the use of at least three layers must be employed to achieve reliable particle tracking.

We tackle the challenge of robust, in-the-wild imaging using ultra-thin nanophotonic metalens cameras. Meta-lenses, composed of planar arrays of nanoscale scatterers, promise dramatic reductions in size and weight compared to conventional refractive optics. However, severe chromatic aberration, pronounced light scattering, narrow spectral bandwidth, and low light efficiency continue to limit their practical adoption. In this work, we present an end-to-end solution for in-the-wild imaging that pairs a metalens several times thinner than conventional optics with a bespoke multi-image restoration framework optimized for practical metalens cameras. Our method centers on a lightweight convolutional network paired with a memory-efficient burst fusion algorithm that adaptively corrects noise, saturation clipping, and lens-induced distortions across rapid sequences of extremely degraded metalens captures. Extensive experiments on diverse, real-world handheld captures demonstrate that our approach consistently outperforms existing burst-mode and single-image restoration techniques.These results point toward a practical route for deploying metalens-based cameras in everyday imaging applications.

We investigate bond percolation on mediation-driven attachment (MDA) networks under the generalized Achlioptas process, where $M>1$ candidate bonds are sampled and the one that minimizes the resulting cluster size is selected the best-of-$M$ rule. This framework offers a systematic approach to investigate how network topology and choice mechanisms jointly shape percolation behavior. We analyze the effects of the degree exponent $\omega$ and the choice parameter $M$ on the critical point $t_c$ and the critical exponents ($\beta,\alpha,\gamma$), which define universality classes and obey the Rushbrooke inequality $\alpha + 2\beta + \gamma \geq 2$. Using entropy, the order parameter, and their derivatives (representing specific heat and susceptibility respectively), we show that both $t_c$ and the universality class depend only weakly on $\omega$ but strongly on $M$, while the Rushbrooke inequality remains valid throughout. For $M=2$, the order parameter varies continuously without a clear order-disorder transition. By contrast, $M=3$ and $M=4$ display explosive percolation that still corresponds to a continuous phase transition, with $M=4$ producing a significantly sharper and clearer order-disorder transition. This sharpening is traced to an enhanced powder-keg effect at larger $M$, underscoring the entropic origin of explosive percolation.

The stability of a differentially rotating fluid subject to its own gravity is a problem with applications across wide areas of astrophysics–from protoplanetary discs (PPDs) to entire galaxies. The shearing box formalism offers a conceptually simple framework for studying differential rotation in the local approximation. Aimed at self-gravitating, and importantly, vertically stratified PPDs, we develop two novel methods for solving Poisson's equation in the framework of the shearing box with vertical vacuum boundary conditions (BCs). Both approaches naturally make use of multi-dimensional fast Fourier transforms for computational efficiency. While the first one exploits the linearity properties of the Poisson equation, the second, which is slightly more accurate, consists of finding the adequate discrete Green's function (in Fourier space) adapted to the problem at hand. To this end, we have revisited the method proposed by Vico et al. (2016) and have derived an analytical Green's function satisfying the shear-periodic BCs in the plane as well as vacuum BCs, vertically. Our spectral method demonstrates excellent accuracy, even with a modest number of grid points, and exhibits third-order convergence. It has been implemented in the NIRVANA-III code, where it exhibits good scalability up to 4096 CPU cores, consuming less than 6% of the total runtime. This was achieved through the use of P3DFFT, a fast Fourier Transform library that employs pencil decomposition, overcoming the scalability limitations inherent in libraries using slab decomposition. We have introduced two novel spectral Poisson solvers that guarantees high accuracy, performance, and intrinsically support vertical vacuum boundary conditions in the shearing-box framework. Our solvers enable high-resolution local studies involving self-gravity, such as MHD simulations of gravito-turbulence or gravitational fragmentation.

Single-molecule detection enables direct observation of individual biomolecular events, providing mechanistic insights into biological processes and offering a powerful tool for disease diagnostics. However, the fundamental scale mismatch between optical wavelengths and molecules restricts the application of label-free techniques, leading to poor signal-to-noise (SNR) performance. Here, we propose a high-contrast, label-free approach based on interferometric imaging, utilizing the strong evanescent field supported on a microfiber surface to provide near-field illumination. We observed unique interference patterns generated by in-plane scattering from natural defects, which enabled high-contrast detection of localized phase changes induced by single molecules. The results indicate an approximately 38 dB enhancement in SNR over the conventional fluorescence methods, without employing any plasmonic or microcavity-based amplification techniques. This approach was further applied to track molecular dynamics, capturing both conformational transition and binding behaviors of individual protein molecules. Meanwhile, the stimulus-response of single molecules to acoustic waves was investigated, demonstrating the ultimate miniaturization of an acoustic sensor at the single-molecule scale. By enabling direct observation of molecular dynamics and mechanical responses at the single-molecule level, this approach provides a versatile platform for probing fundamental biological processes and developing ultra-sensitive biosensors. Moreover, this approach lays the foundation for coupling optical and acoustic waves at the molecular scale, opening new avenues for next-generation single-molecule diagnostics and precision biophysics studies.

In the construction of High-Luminosity Large Hadron Collider (HL-LHC) and Future Circular Collider (FCC) experiments, 3D pixel sensors have become indispensable components due to their superior radiation hardness, fast response, and low power consumption. However, there are still significant challenges in the process of 3D sensors manufacturing. In this work, single devices and arrays of 3D sensors based on 30 $\mu$m epitaxial silicon wafer have been designed, simulated, fabricated, and tested. This process was developed on the 8-inch CMOS process platform of the Institute of Microelectronics of the Chinese Academy of Sciences (IMECAS). The key processes include Deep Reactive Ion Etching (DRIE) with the Bosch process, in-situ doping, and an innovative back-etching. After testing the 3D pixel sensors, we have summarized the leakage current and capacitance of devices with different sizes with respect to bias voltages. We also found that the fabricated devices were almost all successfully produced, which laid a strong foundation for subsequent large-scale mass production.

This paper introduces ChemGen, a software package that uses code generation to integrate multispecies thermodynamics and chemical kinetics into C+-based computational physics codes. ChemGen aims to make chemical kinetics more accessible in existing simulation frameworks and help bridge the gap between combustion modeling and computational physics. The package employs the concept of decorators which enable flexible C++ code generation to target established software ecosystems. ChemGen generates code to evaluate thermodynamic properties, chemical source terms, and their analytical derivatives for Jacobian calculations. Also included are a variety of implicit time integration schemes, linear solvers, and preconditioners. The various components of Chemgen are verified by demonstrating agreement with Cantera and/or theoretical convergence rates. Finally, we integrate ChemGen into OpenFOAM and achieve a speedup over its native chemistry solver by approximately four times. ChemGen is an ongoing project released under the NRL Open License, a source-available license provided by the U.S. Naval Research Laboratory.

Classical computation of electronic properties in large-scale materials remains challenging. Quantum computation has the potential to offer advantages in memory footprint and computational scaling. However, general and practical quantum algorithms for simulating large-scale materials are still lacking. We propose and implement random-state quantum algorithms to calculate electronic-structure properties of real materials. Using a random state circuit with only a few qubits, we employ real-time evolution with first-order Trotter decomposition and Hadamard test to obtain electronic density of states, and we develop a modified quantum phase estimation algorithm to calculate real-space local density of states via direct quantum measurements. Furthermore, we validate these algorithms by numerically computing the density of states and spatial distributions of electronic states in graphene, twisted bilayer graphene quasicrystals, and fractal lattices, covering system sizes from hundreds to thousands of atoms. Our results manifest that the random-state quantum algorithms provide a general and qubit-efficient route to simulating electronic properties of large-scale periodic and aperiodic materials on quantum computers.