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Alkali-activated materials (AAMs) are candidates for high-strength lunar construction materials via in- situ resource utilization (ISRU) of aluminosilicate lunar regolith. To inform processing strategies for lunar AAMs, the shear-dependent rheological properties of a model AAM comprised of a sodium silicate activated metakaolin are measured from synthesis through gelation along with the compressive strength at longer reaction times. A combination of steady-shear, small amplitude oscillatory shear (SAOS), and Optimally Windowed Chirp (OWCh) techniques characterize the viscosity and shear moduli from initial slurry through the critical gel time. The critical gel point defines the processing time window regardless of the applied shear duration. Shearing prior to the critical gel point does not affect the critical gel time or viscoelastic properties of the material after the set point, or the 7-day compressive strength. However, the dynamic moduli prior to the critical gel time vary significantly based on the shear duration, and the critical gel exponent (n) increases with the duration of applied shear. These results demonstrate how to process without compromising final material properties. Metakaolin geopolymers exposed to low earth orbit (LEO) conditions on the Multi-purpose International Space Station Experiment Flight Facility (MISSE-FF) test station on the International Space Station (ISS) for six months of durability testing retain their compressive strength, furthering the technology readiness of aluminosilicate-derived construction material for future lunar construction. This study provides practical guidance for AAM processing protocols and insight into the effect of shear on the binder structural network valuable for both terrestrial and lunar ISRU 

When human observers are asked to describe the shape of a surface, they often identify an arrangement of surface features like bumps, dimples, ridges, or valleys. The central hypothesis of the present research is that the perceptual representation of three-dimensional shape has a graph-like structure that is defined by patterns of surface curvature, and that this is the structure that artists depict when they produce line drawings of objects. Two experiments were performed, in which observers marked the boundaries of bumps on a shaded surface, or the locations of ridges and valleys. Although they were not specifically instructed about where those features were located, the observers’ responses corresponded quite closely with the curvature extrema on each depicted object, and their judgments exhibited a high degree of constancy over changes in the pattern of illumination. The relationship is much weaker between the perceived locations of ridges and valleys and the local extrema of luminance in an image. Although variations of luminance are strongly influenced by the pattern of surface curvature, they are also influenced by local variations in illumination caused by multiple light sources, cast shadows, or indirect reflections. Human observers can somehow distinguish between those two components of luminance variation, but the visual information that makes that possible has yet to be determined. 

Abstract Geopolymers, a class of alkali‐activated binders, are studied as sustainable alternatives to Ordinary Portland Cement due to their potential for CO₂emission reduction. However, the critical relationship between early‐age reaction kinetics, the development of material properties, and evolving chemical structure remains insufficiently explored, primarily because of the complexity of the underlying chemical reactions and the wide variety of geopolymer chemistries. To address this, we investigate the mechanism of early‐age (<72 h) strength development of a model metakaolin geopolymer by measuring curing kinetics using isothermal calorimetry, material property development via rheology, and chemical coordination at distinct extents of reaction via²⁹Si and²⁷Al NMR. A novel approach of collecting solid‐state²⁹Si and²⁷Al NMR spectra at low temperature (−17°C) successfully quenches the geopolymer reaction, allowing for spectrum collection at a desired extent of reaction despite long²⁹Si NMR spectrum collection times. Applying the Avrami kinetic model to deconvoluted calorimetry data enables independent analysis of dissolution and polycondensation/crosslinking reactions. From these data, the gel reaction product mass fraction is estimated, revealing an exponential relationship with the storage modulus in the activated metakaolin slurry. This study provides new insights into the interconnected dynamics of molecular chemistry, reaction kinetics, rheology, and strength development, offering a semi‐empirical framework for understanding property evolution in geopolymers more broadly. 

A comprehensive Bayesian analysis of the 3D dynamics of high-energy nuclear collisions is presented. We perform a systematic model-to-data comparison using simulations of large and small collision systems, and a broad range of measurements from the PHENIX, STAR, PHOBOS, and BRAHMS collaborations spanning nearly two decades of RHIC operations. In particular, we perform fully 3D multi-stage simulations including rapidity-dependent energy deposition with global energy conservation using the 3D Glauber model, along with relativistic viscous hydrodynamics with MUSIC. We calibrate the model on rapidity- and pT-differential observables and analyze the respective constraints on initial state and transport properties they provide. We emphasize the additional constraints provided by rapidity-dependent measurements, the differences in large and small system calibrations, and the tension exhibited by particular observables. We use our calibrated model to make predictions of observables in p-Au and 3He-Au collisions. Furthermore, we facilitate direct comparison of experimental measurements by highlighting the dependence of flow measurements on the rapidity of the regions of interest and reference, as well as the importance of the centrality selection. In particular, we examine the apparent differences between the STAR and PHENIX v2 and v3 measurements in small systems. 

Predicting the response of complex fluids to different flow conditions has been the focal point of rheology and is generally done via constitutive relations. There are, nonetheless, scenarios in which not much is known from the material mathematically, while data collection from samples is elusive, resource-intensive, or both. In such cases, meta-modeling of observables using a parametric surrogate model called multi-fidelity neural networks (MFNNs) may obviate the constitutive equation development step by leveraging only a handful of high-fidelity (Hi-Fi) data collected from experiments (or high-resolution simulations) and an abundance of low-fidelity (Lo-Fi) data generated synthetically to compensate for Hi-Fi data scarcity. To this end, MFNNs are employed to meta-model the material responses of a thermo-viscoelastic (TVE) fluid, consumer product Johnson’s® Baby Shampoo, under four flow protocols: steady shear, step growth, oscillatory, and small/large amplitude oscillatory shear (S/LAOS). In addition, the time–temperature superposition (TTS) of the material response and MFNN predictions are explored. By applying simple linear regression (without induction of any constitutive equation) on log-spaced Hi-Fi data, a series of Lo-Fi data were generated and found sufficient to obtain accurate material response recovery in terms of either interpolation or extrapolation for all flow protocols except for S/LAOS. This insufficiency is resolved by informing the MFNN platform with a linear constitutive model (Maxwell viscoelastic) resulting in simultaneous interpolation and extrapolation capabilities in S/LAOS material response recovery. The roles of data volume, flow type, and deformation range are discussed in detail, providing a practical pathway to multifidelity meta-modeling of different complex fluids. 

Electron transport in complex fluids, biology, and soft matter is a valuable characteristic in processes ranging from redox reactions to electrochemical energy storage. These processes often employ conductor–insulator composites in which electron transport properties are fundamentally linked to the microstructure and dynamics of the conductive phase. While microstructure and dynamics are well recognized as key determinants of the electrical properties, a unified description of their effect has yet to be determined, especially under flowing conditions. In this work, the conductivity and shear viscosity are measured for conductive colloidal suspensions to build a unified description by exploiting both recent quantification of the effect of flow-induced dynamics on electron transport and well-established relationships between electrical properties, microstructure, and flow. These model suspensions consist of conductive carbon black (CB) particles dispersed in fluids of varying viscosities and dielectric constants. In a stable, well-characterized shear rate regime where all suspensions undergo self-similar agglomerate breakup, competing relationships between conductivity and shear rate were observed. To account for the role of variable agglomerate size, equivalent microstructural states were identified using a dimensionless fluid Mason number, ${\mathit{Mn}}_f$ , which allowed for isolation of the role of dynamics on the flow-induced electron transport rate. At equivalent microstructural states, shear-enhanced particle–particle collisions are found to dominate the electron transport rate. This work rationalizes seemingly contradictory experimental observations in literature concerning the shear-dependent electrical properties of CB suspensions and can be extended to other flowing composite systems. 

We investigate medium-induced modifications to jet substructure observables that characterize hard components in central Pb-Pb collisions at $\sqrt{s_{NN}}=5.02$ TeV. Using a multistage Monte Carlo simulation of in-medium jet shower evolution, we explore flavor-dependent medium effects through simulations of inclusive and $\gamma$ -tagged jets. The results show that quark jets undergo a nonmonotonic modification compared with gluon jets in observables such as the Pb-Pb to $p\text{−}p$ ratio of the soft drop prong angle $r_g$ , the relative prong transverse momentum $k_{T,g}$ , and the groomed mass $m_g$ distributions. Due to this nonmonotonic modification, $\gamma$ -tagged jets, enriched in quark jets, provide surprisingly clear signals of medium-induced structural modifications, distinct from effects dominated by selection bias. Further systematic studies demonstrate that these effects are dominated by recoil medium response. This work highlights the potential of hard substructures in $\gamma$ -tagged jets as powerful tools for probing the jet-medium interactions in high-energy heavy-ion collisions, in particular by enabling detailed investigations of jet-medium parton scatterings via their associated medium response. All simulations for $\gamma$ -tagged jet analyses carried out in this paper used triggered events containing at least one hard photon, which highlights the utility of these observables for future Bayesian analysis.