Primary Quantities Research Articles

Termination Kinetics of N‐Vinyl Formamide Radical Polymerization in Aqueous Solution

AbstractTermination kinetics of radical polymerization of N‐vinyl formamide (NVF) in aqueous solution has been measured via SP–PLP–NIR, i.e., single pulse (SP) initiation of pulsed laser polymerization (PLP) in conjunction with microsecond time‐resolved near‐infrared (NIR) detection of monomer concentration. Experiments are performed at initial NVF weight fractions from 0.20 up to bulk NVF, at monomer conversions up to 40%, and at temperatures from 40 to 70 °C as well as pressures from 500 to 2500 bar. Applying high pressure improves signal‐to‐noise quality. Data obtained upon pressure variation allow for extrapolation toward ambient pressure. The primary quantity from SP–PLP–NIR is kp/<kt>, i.e., the ratio of propagation rate coefficient, kp, to apparent chain‐length‐averaged termination rate coefficient, <kt>. With kp being available from literature, kp/<kt> yields <kt>. This quantity is relevant for modeling polymerization rate and polymer properties. Termination in the initial polymerization period turns out to be controlled by segmental diffusion and, at higher degrees of monomer conversion up to 40%, by translational diffusion.

Real-Time Simulation of Tube Hydroforming by Integrating Finite-Element Method and Machine Learning

The real-time, full-field simulation of the tube hydroforming process is crucial for deformation monitoring and the timely prediction of defects. However, this is rather difficult for finite-element simulation due to its time-consuming nature. To overcome this drawback, in this paper, a surrogate model framework was proposed by integrating the finite-element method (FEM) and machine learning (ML), in which the basic methodology involved interrupting the computational workflow of the FEM and reassembling it with ML. Specifically, the displacement field, as the primary unknown quantity to be solved using the FEM, was mapped onto the displacement boundary conditions of the tube component with ML. To this end, the titanium tube material as well as the hydroforming process was investigated, and a fairly accurate FEM model was developed based on the CPB06 yield criterion coupled with a simplified Kim–Tuan hardening model. Numerous FEM simulations were performed by varying the loading conditions to generate the training database for ML. Then, a random forest algorithm was applied and trained to develop the surrogate model, in which the grid search method was employed to obtain the optimal combination of the hyperparameters. Sequentially, the principal strain, the effective strain/stress, as well as the wall thickness was derived according to continuum mechanics theories. Although further improvements were required in certain aspects, the developed FEM-ML surrogate model delivered extraordinary accuracy and instantaneity in reproducing multi-physical fields, especially the displacement field and wall-thickness distribution, manifesting its feasibility in the real-time, full-field simulation and monitoring of deformation states.

Splitting probabilities as optimal controllers of rare reactive events.

The committor constitutes the primary quantity of interest within chemical kinetics as it is understood to encode the ideal reaction coordinate for a rare reactive event. We show the generative utility of the committor in that it can be used explicitly to produce a reactive trajectory ensemble that exhibits numerically exact statistics as that of the original transition path ensemble. This is done by relating a time-dependent analog of the committor that solves a generalized bridge problem to the splitting probability that solves a boundary value problem under a bistable assumption. By invoking stochastic optimal control and spectral theory, we derive a general form for the optimal controller of a bridge process that connects two metastable states expressed in terms of the splitting probability. This formalism offers an alternative perspective into the role of the committor and its gradients in that they encode force fields that guarantee reactivity, generating trajectories that are statistically identical to the way that a system would react autonomously.

Food web attributes to assess spatial–temporal dynamics in estuarine benthic ecosystem

Threatened benthic ecosystems need urgent tools for effective bioassessment and relevant management. EU Marine Strategy Framework Directive (MSFD) obligates member states to achieve GES (Good Environmental Status) for 11 descriptors of environmental state (MSFD; 2008/56/EC). From all of the descriptors, D4 that focuses on Food Webs is the most functional-oriented indicator, but also the most challenging to implement due to our limited knowledge on benthic interactions. Particularly, it is still unclear how spatially and temporally regulated abiotic variables determine the entire benthic food webs, and which benthic food web attributes best respond to these spatially and temporally derived environmental variations. To fill this gap, we measured the natural isotopic ratios (δ13C and δ15N) of macrobenthic organisms and their food sources and build twelve food web topologies across three distinct sites (Navigator, Gambia, Tróia) in summer and winter during two consecutive years. To assess these food web topologies, we applied isotopic metrics, further integrated with univariate and multivariate analysis to find food web-based indicators that best respond to these spatial and temporal variability.We found clear spatial patterns associated to an increase in primary production and quantity and quality of organic matter (OM). Sites with higher organic load and less quality OM (Navigator and Gambia) had simpler food webs, likely associated to high abundance of opportunistic meiobenthic species. Site located inside protected area (Tróia) with high quality OM had the most complex food web characterized by high diversity of specialist consumers that used more efficiently available resources. Similarity metrics were valuable complementary tool that helped to further disentangle the causes of spatial variability, in this case distinguishing between two food webs (Navigator and Gambia) that had similar structures but different resource utilization.The temporal patterns were not so evident than the spatial patterns, although significant differences were reported between sampling occasions for the same metrics (maximum trophic position and the percentage of carnivores and omnivores, p < 0.05). The most complex Tróiás food web demonstrated greater responsiveness in capturing temporal differences in resource use, suggesting that more complex food webs are better equipped to reflect temporal variability. The integration of isotopic metrics complemented with multivariate and univariate analyses proved to be an important tool for the analysis of different aspects of the benthic food web complexity in a spatial–temporal context providing a promising approach to assess the functional integrity of the estuarine ecosystems, especially in the context of the descriptor 4 within MSFD.

A mixed-methods exploration of autonomy-supportive parenting, confidence, and natural mentoring relationships among Black adolescents.

The current study examined whether autonomy-supportive parenting practices may be associated with Black adolescents' quantity of natural mentors (i.e., adults from youths' everyday lives who youth go to for support and guidance) via adolescents' confidence. This study employed survey data from 216 Black youth and qualitative interviews from a subsample of youth (n = 25), their primary caregivers (n = 25), and one nonparental adult relative with whom the youth reported feeling close (n = 25). Comparative analyses were then completed among a subset of 10 family triads corresponding to youth from the qualitative subsample who had the highest (n = 5) and the lowest (n = 5) scores on a survey measure of adolescents' confidence. Study findings suggest that Black adolescents' confidence may be an explanatory link in the association between autonomy-supportive parenting practices among primary caregivers and Black adolescents' quantity of natural mentoring relationships. Moreover, we found that a range of autonomy-supportive parenting practices may be associated with youth confidence, which may, in turn, inform how Black adolescents engage with adults in their social networks.

Interpreting chemisorption strength with AutoML-based feature deletion experiments

The chemisorption energy of reactants on a catalyst surface, [Formula: see text], is among the most informative characteristics of understanding and pinpointing the optimal catalyst. The intrinsic complexity of catalyst surfaces and chemisorption reactions presents significant difficulties in identifying the pivotal physical quantities determining [Formula: see text]. In response to this, the study proposes a methodology, the feature deletion experiment, based on Automatic Machine Learning (AutoML) for knowledge extraction from a high-throughput density functional theory (DFT) database. The study reveals that, for binary alloy surfaces, the local adsorption site geometric information is the primary physical quantity determining [Formula: see text], compared to the electronic and physiochemical properties of the catalyst alloys. By integrating the feature deletion experiment with instance-wise variable selection (INVASE), a neural network-based explainable AI (XAI) tool, we established the best-performing feature set containing 21 intrinsic, non-DFT computed properties, achieving an MAE of 0.23 eV across a periodic table-wide chemical space involving more than 1,600 types of alloys surfaces and 8,400 chemisorption reactions. This study demonstrates the stability, consistency, and potential of AutoML-based feature deletion experiment in developing concise, predictive, and theoretically meaningful models for complex chemical problems with minimal human intervention.

Emergence of the temperature–density relation in the low-density intergalactic medium

ABSTRACT We examine the evolution of the phase diagram of the low-density intergalactic medium during the Epoch of Reionization in simulation boxes with varying reionization histories from the Cosmic Reionization on Computers project. The probability density function (PDF) of gas temperature at fixed density exhibits two clear modes: a warm and a cold temperature mode, corresponding to the gas inside and outside of ionized bubbles. We find that the transition between the two modes is ‘universal’ in the sense that its timing is accurately parametrized by the value of the volume-weighted neutral fraction for any reionization history. This ‘universality’ is more complex than just a reflection of the fact that ionized gas is warm and neutral gas is cold: it holds for the transition at a fixed value of gas density, and gas at different densities transitions from the cold to the warm mode at different values of the neutral fraction, reflecting a non-trivial relationship between the ionization history and the evolving gas density PDF. Furthermore, the ‘emergence’ of the tight temperature–density relation in the warm mode is also approximately ‘universally’ controlled by the volume-weighted neutral fraction for any reionization history. In particular, the ‘emergence’ of the temperature–density relation (as quantified by the rapid decrease in its width) occurs when the neutral fraction is 10−4 ≲ XH i ≲ 10−3 for any reionization history. Our results indicate that the neutral fraction is a primary quantity controlling the various properties of the temperature–density relation, regardless of reionization history.

Phytoplankton physiology and functional traits under artificial upwelling with varying Si:N

IntroductionArtificial upwelling has been discussed as a nature-based solution to fertilize currently unproductive areas of the ocean to enhance food web productivity and atmospheric CO2 sequestration. The efficacy of this approach may be closely tied to the nutrient stoichiometry of the upwelled water, as Si-rich upwelling should benefit the growth of diatoms, who are key players for primary production, carbon export and food web efficiency.MethodsWith a mesocosm experiment in subtropical waters, we assessed the physiological and functional responses of an oligotrophic phytoplankton community to artificial upwelling under varying Si:N ratios (0.07-1.33).ResultsDeep water fertilization led to strongly enhanced primary productivity rates and net autotrophy across Si scenarios. At the community level, Si-rich upwelling50 temporarily increased primary production and consistently enhanced diatom growth, producing up to 10-fold higher abundances compared to Si-deficient upwelling. At the organism level, contrasting effects were observed. On the one hand, silicification and size of diatom cells remained unaffected by Si:N, which is surprising given the direct dependency of these traits on Si. On the other hand, diatom Chlorophyll a density and carbon density were strongly reduced and particulate matter C:N was elevated under Si-rich upwelling.DiscussionThis suggests a reduced nutritional value for higher trophic levels under high Si:N ratios. Despite these strong qualitative changes under high Si, diatom cells appeared healthy and showed high photosynthetic efficiency. Our findings reveal great physiological plasticity and adaptability in phytoplankton under artificial upwelling, with Si-dependent trade-offs between primary producer quantity and quality.

Refinement bounds for the expected number of renewal epochs over a finite interval

ABSTRACT Primary quantities of interest in renewal theory are the expected number of renewals in ( 0 , t ] , known as renewal function (denoted by U ( t ) ), and the expected number of renewals in ( t , t + h ] , given by the difference U ( t + h ) − U ( t ) .One of the most known bounds for the expected number of renewals over a finite interval is the one delivered by Barlow and Proschan [Statistical theory of reliability and life testing. MD: Silver Spring; 1981. Reprint Edition. To begin with]. The essential quantity of this bound is the difference U ( t + h ) − U ( t ) − U ( h ) . In this paper, initially, we offer bounds for the conditional tails of the backward and forward recurrence times, and we give a renewal-type equation for the difference U ( t + h ) − U ( t ) − U ( h ) . Employing those results, we refine the provided bound by Barlow and Proschan [Statistical theory of reliability and life testing. MD: Silver Spring; 1981. Reprint Edition. To begin with]. The results are illustrated with numerical examples.

Impact of dementia special care units for short-stay nursing home patients.

Improving quality of care provided to short-stay patients with dementia in nursing homes is a policy priority. However, it is unknown whether dementia-focused care strategies are associated with improved clinical outcomes or lower utilization and costs for short-stay dementia patients. We performed a national survey of nursing home administrators in 2020-2021, asking about the presence of three dementia-focused care services used for their short-stay patients: (1) a dementia care unit, (2) cognitive deficiency training for staff, and (3) dementia-specific occupational therapy. Using Medicare claims, we identified short-stay episodes for beneficiaries residing in surveyed skilled nursing facilities (SNFs) with and without dementia. We compared clinical, cost, and utilization outcomes for dementia patients in SNFs, which did and did not offer dementia-focused care services. As a counterfactual control, we compared these differences to those for non-dementia patients in the same facilities. Our primary quantity of interest was an interaction term between a patients' dementia status and the presence of a dementia-focused care tool. The study population included 102,860 Medicare episodes of care from 377 SNF survey respondents in 2018-2019. In adjusted comparisons of the interaction between dementia status and the presence of each dementia-focused care tool, dementia care units were associated with a 1.5-day increase in healthy days at home in the 90 days following discharge (p = 0.01) and a 3.1% decrease in the likelihood of a subsequent SNF admission (p = 0.001). Cognitive deficiency training was also associated with a 2.0% increase in antipsychotics (p = 0.03), whereas dementia-specific occupational therapy was associated with a 1.2% increase in falls (p = 0.01) per patient episode. Self-reported use of dementia care units for short-stay patients was associated with modestly better performance in some, but not all, outcome measures. This provides hypothesis-generating evidence that dementia care units could be a promising mechanism to improve care delivery in nursing homes.

Extended analysis of benchmarks for gas phase appearance in low-permeable rocks

This manuscript presents a comprehensive study on the numerical simulation of gas transport in clay rock using the finite-element method, with a specific focus on the transition of the transport regime from single-phase to two-phase conditions. Our code demonstrates the capability to cover this transition seamlessly, without relying on common approaches such as the use of persistent primary variables or the switching of primary variables. In our simulations, the primary variables are gas pressure, capillary pressure, temperature, and displacement of the solid phase. To validate our approach, two benchmark tests were conducted. The first benchmark replicates a well-known scenario in the field of radioactive waste disposal, where gas injection induces a transition from single-phase to two-phase flow. The second benchmark simulates a core drilling experiment, where the mechanical unloading of a fully saturated domain results in the appearance of a gas phase. In addition to analyzing primary quantities, a comprehensive set of secondary variables was introduced to gain deeper insights into the model’s operation and enhance understanding of the underlying processes. By plotting these secondary variables alongside the primary quantities, a comprehensive understanding of the system’s behavior during the transition of flow regimes was obtained. The primary objective of this work is to improve our understanding and confidence in the model used for simulating large repository systems, particularly in the context of nuclear waste disposal and CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_2$$\\end{document} storage. By successfully capturing the transition from single-phase to two-phase gas transport, our study provides valuable insights into the behavior of gas in clay rock. This enhanced understanding lays the groundwork for utilizing the model effectively in large-scale repository simulations, contributing to the advancement of the field of gas transport in clay rock.

Zero-frequency added mass of submerged bodies with near-surface effects

The added mass of ships, submarines, and marine platforms is a primary quantity of interest to understand vessel dynamics and motion. For low-frequency maneuvers at low forward speed, the water surface acts as a wall and the added-mass depends on the proximity to the free-surface. In this paper a method for efficiently determining the full six-degree-of-freedom zero-speed added-mass tensor is presented for bodies that are on or near a wall boundary. The method is based on solving the unsteady Euler equations from rest for one time step to find the generalized added-mass force. The method is based on the work of el Moctar (2022) but in this paper it is simplified to use a static grid, and thereby extended for general six degree-of-freedom motion. The method is validated for a series of canonical problems with analytical solutions that include a circular cylinder that is both deeply submerged and near a wall, and a spheroid that extends from a wall. The method is further validated by computing the added mass for a very-large-crude carrier that is in finite-depth water. Finally the method is used to compute the full six degree-of-freedom added-mass tensor for the Joubert BB2 Submarine for deep submergence and near the water surface.

New scaling law for turbulent boundary layers with high surface mass transfer

A fundamental understanding of the phenomena occurring in the turbulent boundary layer in the presence of surface blowing is limited, and considerable disagreements persist even in describing primary quantities, such as the boundary layer profile. The theories based on the linear boundary layer equations show that the thickness of the sublayer increases in the presence of surface blowing; therefore, the viscous sublayer and law of the wall modify. In this study, direct numerical simulations (DNS) of turbulent boundary layers with uniform surface mass transfers are carried out in order to scale the velocity profile. Emphasis is placed on moderate to high mass transfer rates, which are relevant to the most common hybrid rockets configuration. DNS data are used to establish a functional law of the wall and a law of wake by means of the relation between the wall shear stress and surface mass transfer. Analysis of the mean kinetic energy budget shows that the magnitude of turbulent kinetic energy increases by surface mass transfer, and the production rate extends significantly in the inner layer as the injection rate increases. DNS data of various surface blowing are used to complete the closure of turbulence kinetic energy equation and develop an eddy viscosity model. The predicted turbulent kinetic energy and eddy viscosity agree with DNS data for moderate to high blowing rates.

Sensor Fusion for Power Line Sensitive Monitoring and Load State Estimation.

This paper deals with a specific approach to fault detection in transformer systems using the extended Kalman filter (EKF). Specific faults are investigated in power lines where a transformer is connected and only the primary electrical quantities, input voltage, and current are measured. Faults can occur in either the primary or secondary winding of the transformer. Two EKFs are proposed for fault detection. The first EKF estimates the voltage, current, and electrical load resistance of the secondary winding using measurements of the primary winding. The model of the transformer used is known as mutual inductance. For a short circuit in the secondary winding, the observer generates a signal indicating a fault. The second EKF is designed for harmonic detection and estimates the amplitude and frequency of the primary winding voltage. This contribution focuses on mathematical methods useful for galvanic decoupled soft sensing and fault detection. Moreover, the contribution emphasizes how EKF observers play a key role in the context of sensor fusion, which is characterized by merging multiple lines of information in an accurate conceptualization of data and their reconciliation with the measurements. Simulations demonstrate the efficiency of the fault detection using EKF observers.

Moore–Gibson–Thompson model with the influence of moisture diffusivity of semiconductor materials during photothermal excitation

In the present work, the semiconductor material is used to study the moisture diffusivity when a modified Moore–Gibson–Thompson (MGT) model is taken into account. The influence of moisture concentration is included in the governing equations throughout the photothermal transfer process. Based on the dissimilar relaxation durations of the coupled optoelectronic and thermoelastic waves, the MGT model is used to investigate the issue at hand. The method of the Laplace transform is used to obtain analytical solutions for the physical quantities, constitutive relationships, elastic waves, carrier density, heat equation conduction, and moisture diffusivity for the thermo-elastic medium. To extract the primary physical quantities in the space–time domain, the boundary conditions, temperature, plasma, displacement, and mechanical stress are inverted numerically using the Laplace transform. The effect of the new parameter like the reference moisture parameter with various values is discussed graphically on the primary physical quantities. The comparison between silicon and germanium is taken into account to achieve numerical computations.

Deep learning-based left ventricular segmentation demonstrates improved performance on respiratory motion-resolved whole-heart reconstructions.

Deep learning (DL)-based segmentation has gained popularity for routine cardiac magnetic resonance (CMR) image analysis and in particular, delineation of left ventricular (LV) borders for LV volume determination. Free-breathing, self-navigated, whole-heart CMR exams provide high-resolution, isotropic coverage of the heart for assessment of cardiac anatomy including LV volume. The combination of whole-heart free-breathing CMR and DL-based LV segmentation has the potential to streamline the acquisition and analysis of clinical CMR exams. The purpose of this study was to compare the performance of a DL-based automatic LV segmentation network trained primarily on computed tomography (CT) images in two whole-heart CMR reconstruction methods: (1) an in-line respiratory motion-corrected (Mcorr) reconstruction and (2) an off-line, compressed sensing-based, multi-volume respiratory motion-resolved (Mres) reconstruction. Given that Mres images were shown to have greater image quality in previous studies than Mcorr images, we hypothesized that the LV volumes segmented from Mres images are closer to the manual expert-traced left ventricular endocardial border than the Mcorr images. This retrospective study used 15 patients who underwent clinically indicated 1.5 T CMR exams with a prototype ECG-gated 3D radial phyllotaxis balanced steady state free precession (bSSFP) sequence. For each reconstruction method, the absolute volume difference (AVD) of the automatically and manually segmented LV volumes was used as the primary quantity to investigate whether 3D DL-based LV segmentation generalized better on Mcorr or Mres 3D whole-heart images. Additionally, we assessed the 3D Dice similarity coefficient between the manual and automatic LV masks of each reconstructed 3D whole-heart image and the sharpness of the LV myocardium-blood pool interface. A two-tail paired Student's t-test (alpha = 0.05) was used to test the significance in this study. The AVD in the respiratory Mres reconstruction was lower than the AVD in the respiratory Mcorr reconstruction: 7.73 ± 6.54 ml vs. 20.0 ± 22.4 ml, respectively (n = 15, p-value = 0.03). The 3D Dice coefficient between the DL-segmented masks and the manually segmented masks was higher for Mres images than for Mcorr images: 0.90 ± 0.02 vs. 0.87 ± 0.03 respectively, with a p-value = 0.02. Sharpness on Mres images was higher than on Mcorr images: 0.15 ± 0.05 vs. 0.12 ± 0.04, respectively, with a p-value of 0.014 (n = 15). We conclude that the DL-based 3D automatic LV segmentation network trained on CT images and fine-tuned on MR images generalized better on Mres images than on Mcorr images for quantifying LV volumes.

Probabilistic Modeling and Uncertainty Quantification of Detailed Combustion Simulation for a Swirl Stabilized Spray Burner

To enable risk informed decisions in the simulation-based design and development of novel combustors, uncertainties in the simulation results must be considered. However, due to the high computational costs for their quantification, these uncertainties are commonly not taken into account. Therefore, this work aims at applying an efficient methodology for uncertainty quantification based on Polynomial Chaos Expansion to a semi-technical spray burner reflecting characteristics typically found in modern aeroengine combustors. This requires accurate treatment of the multicomponent liquid fuel, a combustion model relying on finite rate chemistry and a scale resolving hybrid turbulence model to account for highly unsteady flow features and combustion. To overcome the need for costly experimental data for the spray boundary conditions, an algebraic primary breakup model is utilized. The resulting reduction in a priori information is compensated through probabilistic modeling and uncertainty quantification. Due to their importance in the design process, temperature distribution in the gas phase as well as the flame position are considered as the primary quantities of interest. For these quantities of interest, moderate uncertainties are found in the probabilistic simulation results. Further, the predictive capability of the simulation model under uncertainties is quantitively assessed by defining accurary metrics for the gas phase temperature prediction. The study further reveals that the imposed input uncertainties affect quantities of interest in both the dispersed and the gas phase through phase coupling effects.

Inverse problems for a model of biofilm growth

Abstract A bacterial biofilm is an aggregate of micro-organisms growing fixed onto a solid surface, rather than floating freely in a liquid. Biofilms play a major role in various practical situations such as surgical infections and water treatment. We consider a non-linear partial differential equation (PDE) model of biofilm growth subject to initial and Dirichlet boundary conditions, and the inverse coefficient problem of recovering the unknown parameters in the model from extra measurements of quantities related to the biofilm and substrate. By addressing and analysing this inverse problem, we provide reliable and robust reconstructions of the primary physical quantities of interest represented by the diffusion coefficients of substrate and biofilm, the biomass spreading parameters, the maximum specific consumption and growth rates, the biofilm decay rate and the half saturation constant. We give particular attention to the constant coefficients involved in the leading-part non-linearity, and present a uniqueness proof and some numerical results. In the course of the numerical investigation, we have identified extra data information that enables improving the reconstruction of the eight-parameter set of physical quantities associated to the model of biofilm growth.

The Nominal Ranges of Rocky Planet Masses, Radii, Surface Gravities, and Bulk Densities

The two primary observable quantities of an exoplanet—its mass and radius—alone are not sufficient to probe a rocky exoplanet’s interior composition and mineralogy. To overcome this, host-star abundances of the primary planet-building elements (Mg, Si, Fe) are typically used as a proxy for the planet’s bulk composition. The majority of small exoplanet hosts, however, do not have available abundance data. Here we present the open-source ExoPlex mass–radius–composition solver. Unlike previous open-source mass–radius solvers, ExoPlex calculates the core chemistry and equilibrium mantle mineralogy for a bulk composition, including effects of mantle FeO content, core light elements, and surface water/ice. We utilize ExoPlex to calculate the planetary radii, surface gravities, and bulk densities for 106 model planets up to 2 R ⊕ across these geochemistries, adopting the distribution of FGK stellar abundances to estimate of the range of bulk exoplanet compositions. We outline the 99.7% distribution of radii, surface gravities, and bulk densities that define planets as “nominally rocky.” Planets outside this range require compositions outside those expected from stellar abundance data, likely making them either Fe-enriched super-Mercuries, or volatile-enriched mini-Neptunes. We apply our classification scheme to a sample of 85 well-resolved exoplanets without available host-star abundances. We estimate only nine planets are within the “nominally rocky planet zone” at >70% confidence, while ∼20% and ∼30% of this sample can be reasonably classified as super-Mercuries or volatile-rich, respectively. Our results provide observers with a self-consistent way to classify broadly a planet as likely rocky, Mercury-like, or volatile-enriched, using mass and radius measurements alone.

Nonlocal metasurface for circularly polarized light detection

Modern-day sensing and imaging applications increasingly rely on accurate measurements of the primary physical quantities associated with light waves: intensity, wavelength, directionality, and polarization. These are conventionally performed with a series of bulky optical elements, but recently, it has been recognized that optical resonances in nanostructures can be engineered to achieve selective photodetection of light waves with a specific set of predetermined properties. Here, we theoretically illustrate how a thin silicon layer can be patterned into a dislocated nanowire-array that affords detection of circularly polarized light with an efficiency that reaches the theoretical limit for circular dichroism of a planar detector in a symmetric external environment. The presence of a periodic arrangement of dislocations is essential in achieving such unparalleled performance as they enable selective excitation of nonlocal, guided-mode resonances for one handedness of light. We also experimentally demonstrate compact, high-performance chiral photodetectors created from these dislocated nanowire-arrays. This work highlights the critical role defects can play in enabling new nanophotonic functions and devices.