arXiv Physics @arxiv_physics@qoto.org

Studying the dynamics of small-scale beams with attached particles is crucial for sensing applications in various fields, such as bioscience, material science, energy storage devices, and environmental monitoring. Here, a stress-driven nonlocal model is presented for the free transverse vibration of small-scale beams carrying multiple masses taking into account the eccentricity of the masses relative to the beam axis. The results show excellent agreement with the experimental and numerical data in the literature. New insights into the frequency shifts and mode shapes of the first four vibrational modes of stress-driven nonlocal beams with up to three attached particles are presented. The study investigates the inverse problem of detecting the location and mass of an attached particle based on natural frequency shifts. The knowledge acquired from the present study provides valuable guidance for the design and analysis of ultrasensitive mechanical mass sensors.

Molecular representation learning is pivotal for various molecular property prediction tasks related to drug discovery. Robust and accurate benchmarks are essential for refining and validating current methods. Existing molecular property benchmarks derived from wet experiments, however, face limitations such as data volume constraints, unbalanced label distribution, and noisy labels. To address these issues, we construct a large-scale and precise molecular representation dataset of approximately 140,000 small molecules, meticulously designed to capture an extensive array of chemical, physical, and biological properties, derived through a robust computational ligand-target binding analysis pipeline. We conduct extensive experiments on various deep learning models, demonstrating that our dataset offers significant physicochemical interpretability to guide model development and design. Notably, the dataset's properties are linked to binding affinity metrics, providing additional insights into model performance in drug-target interaction tasks. We believe this dataset will serve as a more accurate and reliable benchmark for molecular representation learning, thereby expediting progress in the field of artificial intelligence-driven drug discovery.

Vibration attenuation has played a crucial role in engineering structure, wherein metamaterials have found escalated usage. These structures can be cleverly built to be lightweight and have negative mass properties, which can attenuate waves at specific frequency bands. The first part studies wave propagation in meta-beam with coupled acoustic black holes and local resonators, whereas the second part discusses multi-stable nonlinear oscillators in a 1D metamaterial chain.

This paper presents a comprehensive analysis of both conventional and deep learning methods for eliminating electromagnetic interference (EMI) in MRI systems. We explore the underlying principles and implementation of traditional analytical and adaptive EMI elimination techniques, as well as cutting-edge deep learning approaches. Through a detailed comparison, the strengths and limitations of each method are highlighted. Recent advancements in active EMI elimination utilizing multiple external EMI receiver coils and analytical techniques are discussed alongside the superior performance of deep learning methods, which leverage neural networks trained on extensive MRI data. While deep learning methods demonstrate significant improvements in EMI suppression, enhancing diagnostic capabilities and accessibility of MRI technology, they also introduce potential security and safety concerns, especially in production and commercial applications. This study underscores the need to address these challenges to fully realize the benefits of deep learning in EMI elimination. The findings suggest a balanced approach, combining the reliability of conventional methods with the advanced capabilities of deep learning, to develop more robust and effective EMI suppression strategies in MRI systems.

The finite-difference time-domain (FDTD) method, which is important in photonic hardware design flow, is widely adopted to solve time-domain Maxwell equations. However, FDTD is known for its prohibitive runtime cost, taking minutes to hours to simulate a single device. Recently, AI has been applied to realize orders-of-magnitude speedup in partial differential equation (PDE) solving. However, AI-based FDTD solvers for photonic devices have not been clearly formulated. Directly applying off-the-shelf models to predict the optical field dynamics shows unsatisfying fidelity and efficiency since the model primitives are agnostic to the unique physical properties of Maxwell equations and lack algorithmic customization. In this work, we thoroughly investigate the synergy between neural operator designs and the physical property of Maxwell equations and introduce a physics-inspired AI-based FDTD prediction framework PIC2O-Sim which features a causality-aware dynamic convolutional neural operator as its backbone model that honors the space-time causality constraints via careful receptive field configuration and explicitly captures the permittivity-dependent light propagation behavior via an efficient dynamic convolution operator. Meanwhile, we explore the trade-offs among prediction scalability, fidelity, and efficiency via a multi-stage partitioned time-bundling technique in autoregressive prediction. Multiple key techniques have been introduced to mitigate iterative error accumulation while maintaining efficiency advantages during autoregressive field prediction. Extensive evaluations on three challenging photonic device simulation tasks have shown the superiority of our PIC2O-Sim method, showing 51.2% lower roll-out prediction error, 23.5 times fewer parameters than state-of-the-art neural operators, providing 300-600x higher simulation speed than an open-source FDTD numerical solver.

Tensor network algorithms can efficiently simulate complex quantum many-body systems by utilizing knowledge of their structure and entanglement. These methodologies have been adapted recently for solving the Navier-Stokes equations, which describe a spectrum of fluid phenomena, from the aerodynamics of vehicles to weather patterns. Within this quantum-inspired paradigm, velocity is encoded as matrix product states (MPS), effectively harnessing the analogy between interscale correlations of fluid dynamics and entanglement in quantum many-body physics. This particular tensor structure is also called quantics tensor train (QTT). By utilizing NVIDIA's cuQuantum library to perform parallel tensor computations on GPUs, our adaptation speeds up simulations by up to 12.1 times. This allows us to study the algorithm in terms of its applicability, scalability, and performance. By simulating two qualitatively different but commonly encountered 2D flow problems at high Reynolds numbers up to $1\times10^7$ using a fourth-order time stepping scheme, we find that the algorithm has a potential advantage over direct numerical simulations in the turbulent regime as the requirements for grid resolution increase drastically. In addition, we derive the scaling $χ=\mathcal{O}(\text{poly}(1/ε))$ for the maximum bond dimension $χ$ of MPS representing turbulent flow fields, with an error $ε$, based on the spectral distribution of turbulent kinetic energy. Our findings motivate further exploration of related quantum algorithms and other tensor network methods.

: Non-resonant background (NRB) plays a significant role in coherent anti-Stokes Raman scattering (CARS) spectroscopic applications. All the recent works primarily focused on removing the NRB using different deep learning methods, and only one study explored the effect of NRB. Hence, in this work, we systematically investigated the impact of NRB variation on Raman signal retrieval. The NRB is simulated as a linear function with different strengths relative to the resonant Raman signal, and the variance also changed for each NRB strength. The resonant part of nonlinear susceptibility is extracted from real experimental Raman data; hence, the simulated CARS data better approximate the experimental CARS spectra. Then, the corresponding Raman signal is retrieved by four different methods: maximum entropy method (MEM), Kramers-Kronig (KK), convolutional neural network (CNN), and long short-term memory (LSTM) network. Pearson correlation measurements and principal component analysis combined with linear discriminant analysis (PCA-LDA) modelling revealed that MEM and KK methods have an edge over LSTM and CNN for higher NRB strengths. It is also demonstrated that normalizing the input data favors LSTM and CNN predictions. In contrast, background removal from the predictions significantly influenced Pearson correlation but not the classification accuracies for MEM and KK. This comprehensive study is done for the first time to the best of our knowledge and has the potential to impact the CARS spectroscopy and microscopy applications in different areas.

Photon detectors featuring single-photon sensitivity play a crucial role in various scientific domains, including high-energy physics, astronomy, and quantum optics. Fast response time, high quantum efficiency, and minimal dark counts are the characteristics that render them ideal candidates for detecting individual photons with exceptional signal-to-noise ratios, at frequencies in the the range of hundreds of MHz. Here, we report on our first design and operational results on a Hybrid Photon Detector (HPD) that combines the high quantum efficiency of a Gallium Nitride (GaN) photocathode and the low noise characteristics of a Si-based Low-Gain Avalanche Diode (LGAD). This hybrid detection scheme has the potential to reach single-photon detection sensitivity with high quantum efficiency, low noise levels and capable of operating at hundreds of MHz repetition rates.

Using a high-stakes thermodynamics exam as sample (252~students, four multipart problems), we investigate the viability of four workflows for AI-assisted grading of handwritten student solutions. We find that the greatest challenge lies in converting handwritten answers into a machine-readable format. The granularity of grading criteria also influences grading performance: employing a fine-grained rubric for entire problems often leads to bookkeeping errors and grading failures, while grading problems in parts is more reliable but tends to miss nuances. We also found that grading hand-drawn graphics, such as process diagrams, is less reliable than mathematical derivations due to the difficulty in differentiating essential details from extraneous information. Although the system is precise in identifying exams that meet passing criteria, exams with failing grades still require human grading. We conclude with recommendations to overcome some of the encountered challenges.

Could the microdroplets formed by viscoelastic stretching and break-up of mucosal liquids in the upper respiratory tract (URT), when inhaled further downwind, explain the brisk pace at which deep lung infections emerge following onset of initial infection at the URT? While it is well-established that particulates inhaled from outside can possibly penetrate to the lower airway only if they are < 5 microns, the fate of particulates (many > 5-microns in diameter) sheared away from the intra-URT mucosa during inhalation remains an open question. These particulates predominantly originate at the nasopharynx, oropharynx, and laryngeal chamber with the vocal folds. To resolve the posed question, this study considers a CT-based 3D anatomical airway reconstruction and isolates the tract from the laryngeal vocal fold region, mapping the entire tracheal cavity and concluding at generation 2 of the tracheobronchial tree. Through the delineated geometry, airflow simulation is conducted using the LES scheme to replicate relaxed inhalation at 15 L/min. Against the ambient air flux, numerical experiments have been performed to monitor the transport of liquid particulates with diameters 1-30 microns, bearing physical properties akin to aerosolized mucus with embedded virions. The full-scale numerical transmission trends to the lower airway were found consistent with the findings from a reduced-order mathematical model that conceptualized the impact of intra-airway vortex instabilities on local particle transport through point vortex idealization in an anatomy-guided 2D potential flow domain. The results collectively demonstrate markedly elevated trends of deep lung penetration by the URT-derived particulates, even if they are as large as 10- and 15 microns. The high viral load carried by such droplets to the bronchial spaces could mechanistically explain the accelerated seeding of infection in the lungs.

In this paper, we experimentally validate our previous numerical works in recurrent optical spectrum slicing (ROSS) accelerators for dispersion compensation in high-speed IM/DD links. For this, we utilize recurrent filters implemented both through a waveshaper and by exploiting novel integrated programmable photonic platforms. Different recurrent configurations are tested. The ROSS accelerators exploit frequency processing through recurrent optical filter nodes in order to mitigate the power fading effect, which hinders the transmission distance and baudrate scalability of IM/DD systems. By equalizing even 80 km of 64 Gb/s PAM-4 transmission in C-band, we prove that our system can offer an appealing solution in highly dispersive channels.

Energy-transfer processes can be viewed as being due to the emission of a virtual photon. It is demonstrated that the emission of virtual photons and thus of energy transfer is stimulated by the sheer presence of photons. We concentrate here on interatomic/intermolecular Coulombic decay (ICD) where an excited system relaxes by transferring its excess energy to a neighbor ionizing it. ICD is inactive if this excess energy is insufficiently large. However, in the presence of photons, the long-range interaction between the system and its neighbor can utilize the photon field making ICD active. The properties of this stimulated-ICD mechanism are discussed. The concept can be transferred to other scenarios. We discuss collective-ICD where two excited molecules concertedly transfer their excess energy. Also here, the presence of photons can make the process active if the sum of excess energies were insufficient to do so. Examples with typical molecules and atoms are presented to demonstrate that these stimulated processes can play a role.

The shape of a microchannel cross-section is usually affected by a significant uncertainty due to the small hydraulic diameter. Such an uncertainty is indeed present at any scale, but is amplified in smaller scales, becoming significantly important when the hydraulic diameter is smaller than some tenth micrometers. In this scenario, this paper is focused on analyzing the sensitivity of the heat and fluid flow characteristics with respect to the channel shape, considering a random modifications in the channel cross section. Forced convection in a fully developed regime with a wall slip is considered, and the analysis includes the evaluation of the Fanning friction factor and of the Nusselt number for the H1 thermal boundary condition, considering different slip-flow configurations, as dictated by the slip-length parameter. The heat and fluid flow problem is solved for one thousand randomly generated channel geometries, based on a circular microchannel which is allowed to have its boundary points move within 10\% of its nominal diameter. The calculated data for $f \Re$ and $\Nu$ are then analyzed, and compared with the nominal values (obtained for smooth channels). The results show that, on an average basis, the roughness effect has a tendency to reduce the Nusselt number while increasing the friction factor, however, with a small number of exceptions.

The process of bubble formation from an orifice submerged in liquid with constant gas flow is studied by numerical simulations using an OpenFOAM volume-of-fluid solver named interIsoFoam. The computed results show that the detached bubble size tends to increase with the gas flow rate, orifice size, surface tension, liquid contact angle, etc. in qualitative agreement with most previous authors. For a given orifice size and liquid properties, there exists a critical gas flow rate above which detached bubbles will combine via coalescence known as bubble pairing. The detached bubble size seems insensitive to the contact angle when the liquid adequately wets the orifice wall, but can increase substantially if the contact angle is increased beyond a critical value resulting in contact line motion on the horizontal orifice surface. The value of such a critical contact angle increases with the orifice size as well as gas flow rate. Such revelations would logically suggest that the usage of reduced orifice size for generating smaller bubbles could be more challenging and practically difficult with constant gas flow for sub-millimeter orifices.

Aromatic molecules represent fundamental building blocks in prebiotic chemistry and are contemplated as vital precursors to DNA and RNA nitrogen bases. However, despite the identification of some 300 molecules in extraterrestrial environments, the pathways to pyridine (C5H5N), pyridinyl (C5H4N), and (iso)quinoline (C9H7N) the simplest representative of mono and bicyclic aromatic molecule carrying nitrogen are elusive. Here, we afford compelling evidence on the gas phase formation of methylene amidogen (H2CN) and cyanomethyl (H2CCN) radicals via molecular beam studies and electronic structure calculations. The modeling of the chemistries of Taurus Molecular Cloud (TMC 1) and Titans atmosphere contemplates a complex chain of reactions synthesizing pyridine, pyridinyl, and (iso)quinoline from H2CN and H2CCN at levels of up to 75%. This study affords unique entry points to precursors of DNA and RNA nitrogen bases in hydrocarbon rich extraterrestrial environments thus changing the way we think about the origin of prebiotic molecules in our Galaxy.

The effects of pressure waves-flame collisions and tube aspect ratio on flame evolution and the formation of tulip and distorted tulip flames were investigated using numerical simulations of the fully compressible Navier-Stokes equations coupled with a detailed chemical model for a stoichiometric hydrogen-air mixture. It is shown that: (1) the rarefaction wave generated by the decelerating flame in the unburned gas is the primary physical process leading to the flame front inversion and the tulip flame formation, (2) the flame front instabilities (Darrieus-Landau or Rayleigh-Taylor) do not participate in the formation of the tulip flame, since the time of the flame front inversion due to the rarefaction wave is considerably shorter than the characteristic times of the development of instabilities with wavelengths of the order of the tube width. The first rarefaction wave in the unburned gas mixture is generated after the flame skirt touches the tube walls and the flame is slowed down due to the reduction in flame surface area. The collision of the flame with the pressure waves reflected from the closed end of the tube leads to a faster and more pronounced formation of a tulip-shaped flame. In later stages, flame collisions with pressure waves can lead to the formation of distorted tulip flames due to short-wavelength Rayleigh-Taylor instability of the flame front. Because flame acceleration and deceleration occur much faster in 3D flames than in 2D flames, tulip flame formation also occurs much faster in 3D flames than in 2D flames.

Experimental and computational chemistry are two disciplines to conduct research in Astrochemistry, providing essential reference data for both astronomical observations and modeling. These approaches not only mutually support each other, but also serve as complementary tools to overcome their respective limitations. We characterized the binding energies (BEs) of ethanol (CH$_3$CH$_2$OH) and ethylamine (CH$_3$CH$_2$NH$_2$), two interstellar complex organic molecules (iCOMs), onto crystalline and amorphous water ices through density functional theory (DFT) calculations and temperature programmed desorption (TPD) experiments. Experimentally, CH$_3$CH$_2$OH and CH$_3$CH$_2$NH$_2$ behave similarly, in which desorption temperatures are higher on the water ices than on a bare gold surface. Computed cohesive energies of pure ethanol and ethylamine bulk structures allow describing the BEs of the pure species deposited on the gold surface, as extracted from the TPD curve analyses. The BEs of submonolayer coverages of CH$_3$CH$_2$OH and CH$_3$CH$_2$NH$_2$ on the water ices cannot be directly extracted from TPD due to their co-desorption with water, but they are computed through DFT calculations, and found to be greater than the cohesive energy of water. The behaviour of CH$_3$CH$_2$OH and CH$_3$CH$_2$NH$_2$ is different when depositing adsorbate multilayers on the amorphous ice, in that, according to their computed cohesive energies, ethylamine layers present weaker interactions compared to ethanol and water. Finally, from the computed BEs of ethanol, ethylamine and water, we can infer that the snow-lines of these three species in protoplanetary disks will be situated at different distances from the central star. It appears that a fraction of ethanol and ethylamine is already frozen on the grains in the water snow-lines, causing their incorporation in water-rich planetesimals.

With increasing miniaturization of diagnostic devices for more effective detection of blood-borne pathogens for example, Poiseuille molecular flow in micro channels has become increasingly relevant. Since continuum mechanics no longer applies for Poiseuille flow when the Knudson number is near or larger than unity, kinetic theory is required to capture the microscopic molecular scattering responsible for channel molecular flow and the velocity profile across a channel. Here, we apply a response matrix solution to the 1D Poiseuille flow with a BGK approximation featuring simplicity with precision by following a consistent numerical formulation leading to high precision, 8-place benchmarks.

In a cylindrical tube filled with incompressible fluid, the variable parameter method is applied to solve the vorticity motion equation, and the obtained results are substituted into the Poisson equation satisfied by the energy density. The static distribution of energy density in the tube and the energy density of the flowing fluid after a certain spatial sampling interval are obtained. Further numerical calculations are conducted on the energy density of the flowing fluid, the results show that the intermittent vibration observed by Reynolds 140 years ago can only be obtained with the appropriate spatial sampling interval. The energy density in the tube is proportional to to , where N is an integer It tends to infinity with increasing N.

The Takagi-Taupin equations are solved in their simplest form (zero deformation) to obtain the Bragg-diffracted and transmitted complex amplitudes. The case of plane-parallel crystal plates is discussed using a matrix model. The equations are implemented in an open-source python library crystalpy adapted for numerical applications such as crystal reflectivity calculations and ray tracing.