5632 research outputs found
Sort by
Recovery of continuous 3D refractive index maps from discrete intensity-only measurements using neural fields
No full textIntensity diffraction tomography (IDT) refers to a class of optical microscopy techniques for imaging the three-dimensional refractive index (RI) distribution of a sample from a set of two-dimensional intensity-only measurements. The reconstruction of artefact-free RI maps is a fundamental challenge in IDT due to the loss of phase information and the missing-cone problem. Neural fields has recently emerged as a new deep learning approach for learning continuous representations of physical fields. The technique uses a coordinate-based neural network to represent the field by mapping the spatial coordinates to the corresponding physical quantities, in our case the complex-valued refractive index values. We present Deep Continuous Artefact-free RI Field (DeCAF) as a neural-fields-based IDT method that can learn a high-quality continuous representation of a RI volume from its intensity-only and limited-angle measurements. The representation in DeCAF is learned directly from the measurements of the test sample by using the IDT forward model without any ground-truth RI maps. We qualitatively and quantitatively evaluate DeCAF on the simulated and experimental biological samples. Our results show that DeCAF can generate high-contrast and artefact-free RI maps and lead to an up to 2.1-fold reduction in the mean squared error over existing methods
The Anthropic (and Mis-) Principle revisited, Steven Weinberg in memoriam
No full textI review the role and meaning of the Anthropic Principle, particularly in its relevance to particle physics as that subject evolves. This is neither a technical paper nor a resource letter, but rather an easy introduction
Reply to Oren et al., "New Phylum Names Harmonize Prokaryotic Nomenclature"
No full textThis is a response to a letter by Oren et al. https://doi.org//10.1128/mBio.01479-22
Rupture Directivity of the 2021 M_W 6.0 Yangbi, Yunnan Earthquake
No full textThe 2021 M_W 6.0 Yangbi, Yunnan strike-slip earthquake occurred on an unmapped crustal fault near the Weixi-Qiaoho-Weishan Fault along the southeast margin of the Tibetan Plateau. Using near-source broadband seismic data from ChinArray, we investigate the spatial and temporal rupture evolution of the mainshock using apparent moment-rate functions (AMRFs) determined by the empirical Green's function (EGF) method. Assuming a 1D line source on the fault plane, the rupture propagated unilaterally southeastward (∼144°) over a rupture length of ∼8.0 km with an estimated rupture speed of 2.1 km/s to 2.4 km/s. A 2D coseismic slip distribution for an assumed maximum rupture propagation speed of 2.2 km/s indicates that the rupture propagated to the southeast ∼8.0 km along strike and ∼5.0 km downdip with a peak slip of ∼2.1 m before stopping near the largest foreshock, where three bifurcating subfaults intersect. Using the AMRFs, the radiated energy of the mainshock is estimated as ∼1.6 x 10^(13)J. The relatively low moment scaled radiated energy (E_R)/(M_0) of 1.5 × 10^(−5) and intense foreshock and aftershock activity might indicate reactivation of an immature fault. The earthquake sequence is mainly distributed along a northwest-southeast trend, and aftershocks and foreshocks are distributed near the periphery of the mainshock large-slip area, suggesting that the stress in the mainshock slip zone is significantly reduced to below the level for more than a few overlapping aftershock to occur
Modelling the downstream development of a turbulent boundary layer following a step change of roughness
No full textIn this study, we develop an analytical model to predict the turbulent boundary layer downstream of a step-change in the surface roughness where upstream flow conditions are given. We first revisit the classical model of Elliott (Trans. Am. Geophys. Union, vol. 39, 1958, pp. 1048–1054), who modelled the velocity distribution within and above the internal layer with a simple piecewise logarithmic profile, and evolved the velocity profile using the streamwise momentum equation. Elliott's model was originally developed for an atmospheric surface layer, and to make the model applicable to a spatially developing turbulent boundary layer with finite thickness, we propose a number of more physical refinements, including adding a wake function to the velocity profile, considering the growth of the entire boundary layer in the streamwise direction, and using a more realistic shear stress profile in the momentum equation. In particular, we implement the blending model (Li et al., J. Fluid Mech., vol. 923, 2021, p. A18) to account for the deviation of the mean flow within the internal layer from a canonical velocity profile based on the local wall condition. These refinements lead to improved agreement between the prediction and the measurement, especially in the vicinity of the rough-to-smooth change
Trophic specialization of a newly described spider ant symbiont, Myrmecicultor chihuahuensis (Araneae: Myrmecicultoridae)
No full textThe spider Myrmecicultor chihuahuensis Ramírez, Grismado & Ubick (Myrmecicultoridae) was described in 2019 and hypothesized to be a myrmecophile, living inside the nests of Novomessor (Myrmicinae) and perhaps also Pogonomyrmex (Myrmicinae) ants. To test the hypothesis that M. chihuahuensis are chemical mimics of their host ants, we carried out behavioral bioassays to observe interactions between the spiders and the host ants. We compared the cuticular hydrocarbon (CHC) profiles of the spiders and the ants. We discovered that this new species of spider is a myrmecophage, displaying hunting strategies similar to other myrmecophagous spiders, particularly those in the genus Zodarion Walckenaer, 1826 (Zodariidae). The CHC profile of M. chihuahuensis did not match the profile of the ants with which it associates, Novomessor albisetosus (Mayr), but ants and spider shared several compounds, potentially involved in species recognition
Lifetime-Limited and Tunable Quantum Light Emission in h-BN via Electric Field Modulation
No full textColor-center-based single-photon emitters in hexagonal boron nitride (h-BN) have shown promising photophysical properties as sources for quantum light emission. Despite significant advances toward such a goal, achieving lifetime-limited quantum light emission in h-BN has proven to be challenging, primarily due to various broadening mechanisms, including spectral diffusion. Here, we propose and experimentally demonstrate suppression of spectral diffusion by applying an electrostatic field. We observe both Stark shift tuning of the resonant emission wavelength and emission line width reduction (down to 89 MHz) nearly to the homogeneously broadened lifetime limit. Finally, we find a cubic dependence of the line width with respect to temperature at the homogeneous broadening regime. Our results suggest that field tuning in electrostatically gated heterostructures is promising as an approach to control the emission characteristics of h-BN color centers, removing spectral diffusion and providing the energy tunability necessary for integrate of quantum light emission in nanophotonic architectures
Stress-based forecasting of induced seismicity with instantaneous earthquake failure functions: Applications to the Groningen gas reservoir
No full textIn this study we use the Groningen gas field to test a new method to assess stress changes due to gas extraction and forecast induced seismicity. We take advantage of the detailed knowledge of the reservoir geometry and production history, and of the availability of surface subsidence measurements and high quality seismicity data. The subsurface is represented as a homogeneous isotropic linear poroelastic half-space subject to stress changes in three-dimensional space due to reservoir compaction and pore pressure variations. The reservoir is represented with cuboidal strain volumes. Stress changes within and outside the reservoir are calculated using a convolution with semi-analytical Green functions. The uniaxial compressibility of the reservoir is spatially variable and constrained with surface subsidence data. We calculate stress changes since the onset of gas production. Coulomb stress changes are maximum near the top and bottom of the reservoir where the reservoir is offset by faults. To assess earthquake probability, we use the standard Mohr-Coulomb failure criterion assuming instantaneous nucleation and a non-critical initial stress. The distribution of initial strength excess, the difference between the initial Coulomb stress and the critical Coulomb stress at failure, is treated as a stochastic variable and estimated from the observations and the modelled stress changes. The exponential rise of seismicity nearly 30 years after the onset of production, provides constraints on the distribution of initial strength. The lag and exponential onset of seismicity are well reproduced assuming either a generalized Pareto distribution, which can represent the tail of any distribution, or a Gaussian distribution, to describe both the tail and body of the distribution. The Gaussian distribution allows to test if the induced seismicity at Groningen has transitioned to the steady-state where seismicity rate is proportional to the stressing rate. We find no evidence that the system has reached such a steady-state regime. The modeling framework is computationally efficient making it possible to test the sensitivity to modeling assumptions regarding the estimation of stress changes. The forecast is found robust to uncertainties about the ability of the model to represent accurately the physical processes. It does not require in particular a priori knowledge of the location and orientation of the faults that can be activated. The method presented here is in principle applicable to induced seismicity in any setting provided deformation and seismicity data are available to calibrate the model
Machine Learning Predicts the X-ray Photoelectron Spectroscopy of the Solid Electrolyte Interface of Lithium Metal Battery
No full textX-ray photoelectron spectroscopy (XPS) is a powerful surface analysis technique widely applied in characterizing the solid electrolyte interphase (SEI) of lithium metal batteries. However, experiment XPS measurements alone fail to provide atomic structures from a deeply buried SEI, leaving vital details missing. By combining hybrid ab initio and reactive molecular dynamics (HAIR) and machine learning (ML) models, we present an artificial intelligence ab initio (AI-ai) framework to predict the XPS of a SEI. A localized high-concentration electrolyte with a Li metal anode is simulated with a HAIR scheme for ∼3 ns. Taking the local many-body tensor representation as a descriptor, four ML models are utilized to predict the core level shifts. Overall, extreme gradient boosting exhibits the highest accuracy and lowest variance (with errors ≤ 0.05 eV). Such an AI-ai model enables the XPS predictions of ten thousand frames with marginal cost
Machine Learning-Aided Design of Gold Core–Shell Nanocatalysts toward Enhanced and Selective Photooxygenation
No full textWe demonstrate the use of the machine learning (ML) tools to rapidly and accurately predict the electric field as a guide for designing core–shell Au–silica nanoparticles to enhance ¹O₂ sensitization and selectivity of organic synthesis. Based on the feature importance analysis, obtained from a deep neural network algorithm, we found a general and linear dependent descriptor (θ ∝ aD⁰.²⁵t⁻¹, where a, D, and t are the shape constant, size of metal nanoparticles, and distance from the metal surface) for the electric field around the core–shell plasmonic nanoparticle. Directed by the new descriptor, we synthesized gold-silica nanoparticles and validated their plasmonic intensity using scanning transmission electron microscopy-electron energy loss spectroscopy (STEM-EELS) mapping. The nanoparticles with θ = 0.40 demonstrate an ∼3-fold increase in the reaction rate of photooxygenation of anthracene and 4% increase in the selectivity of photooxygenation of dihydroartemisinic acid (DHAA), a long-standing goal in organic synthesis. In addition, the combination of ML and experimental investigations shows the synergetic effect of plasmonic enhancement and fluorescence quenching, leading to enhancement for 1O2 generation. Our results from time-dependent density functional theory (TD-DFT) calculations suggest that the presence of an electric field can favor intersystem crossing (ISC) of methylene blue to enhance ¹O₂ generation. The strategy reported here provides a data-driven catalyst preparation method that can significantly reduce experimental cost while paving the way for designing photocatalysts for organic drug synthesis