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Adsorption-induced swelling occurs in a wide spectrum of natural and engineered porous materials. A key underlying mechanism is the monotonic reduction of solid-fluid surface energy upon fluid adsorption, which lowers the contractive adsorption stress and causes the porous skeleton to swell (Bangham and Fakhoury, 1928). Some mesoporous materials, however, deviate from the monotonic swelling pattern predicted by this mechanism, exhibiting an abrupt shrinkage at intermediate adsorbate partial pressures before swelling resumes and continues to full saturation. This behavior is commonly attributed to capillary condensation of the adsorbate from the vapor to the liquid phase within the pores. Understanding the stresses and the shrinkage induced by capillary condensation is critical in various industrial applications including micro-/nanofabrication, geotechnical engineering in collapsible soils, and sorption-driven actuation technologies. This work aims to develop a unified poromechanics theory that captures the full sequence of adsorption-induced deformation, including initial swelling, contraction during capillary condensation, and resumed expansion near full saturation. The formulation begins with a thermodynamic analysis of an unsaturated deformable porous solid acknowledging the energetics of the solid-fluid (sl), solid-vapor (sv), and liquid-vapor (lv) interfaces. The resulting free energy balance permits the simultaneous derivation of the liquid retention characteristics curve and the coupled mechanical effects driven by adsorption and partial saturation. Within this framework, two strategies for constructing constitutive relations are examined: one explicitly resolves the dynamic evolution of sl-sv-lv interfacial areas to emphasize the underlying physics, while the other partially lumps the surface energies into a macroscopic capillary potential to facilitate model calibration using standard laboratory tests. The models are evaluated using datasets from two markedly different solid-fluid systems: N2 gas adsorption on a hierarchical porous silica at 77 K and water adsorption on a carbon xerogel at 298 K. Both approaches effectively capture the complex, non-monotonic strain isotherms exhibited by the adsorbent. The adsorption-desorption hysteresis is also addressed in a thermodynamically consistent framework. The proposed theory demonstrates both robustness and unifying power in explaining the complex strain isotherms of porous materials along adsorption and desorption paths, covering the entire spectrum from vacuum-dry to fully liquid-saturated states. 

In-bed postures offer valuable information about an individual's sleep quality and overall health conditions, particularly for patients with sleep apnea. However, current in-bed posture classification systems lack privacy-friendly and easy-to-install options. Furthermore, existing solutions do not consider variations between patients and are typically trained only once, neglecting the utilization of time consistency and unlabeled data from new patients. To address these limitations, this paper builds on a seismic sensor to introduce a novel sleep posture framework, which comprises two main components, namely, the Multi-Granularity Supervised Contrastive Learning (MGSCL) module and the ensemble Online Adaptation (oa) module. Unlike most existing contrastive learning frameworks that operate at the sample level, MGSCL leverages multi-granular information, operating not only at the sample level but also at the group level. The oa module enables the model to adapt to new patient data while ensuring time consistency in sleep posture predictions. Additionally, it quantifies model uncertainty to generate weighted predictions, further enhancing performance. Evaluated on a dataset of 100 patients collected at a clinical research center, MGSCLoa achieved an average accuracy of 91.67% and an average F1 score of 91.53% with only 40 seconds of labeled data per posture. In a Phase 2 evaluation with 11 participants over 13 nights in home settings, the framework reached an average accuracy of 85.37% and a weighted F1 score of 83.59% using just 3 minutes of labeled data per common posture for each participant. These results underscore the potential of seismic sensor-based in-bed posture classification for assessing sleep quality and related health conditions. 

Plastic-bonded granular materials (PBM) are widely used in industrial sectors, including building construction, abrasive applications, and defense applications such as plastic-bonded explosives. The mechanical behavior of PBM is highly nonlinear, irreversible, rate dependent, and temperature sensitive governed by various micromechanical attributions such as grain crushing and binder damage. This paper presents a thermodynamically consistent, microstructure-informed constitutive model to capture these characteristic behaviors of PBM. Key features of the model include a breakage internal variable to upscale the grain-scale information to the continuum level and to predict grain size evolution under mechanical loading. In addition, a damage internal state variable is introduced to account for the damage, deterioration, and debonding of the binder matrix upon loading. Temperature is taken as a fundamental external state variable to handle non-isothermal loading paths. The proposed model is able to capture with good accuracy several important aspects of the mechanical properties of PBM, such as pressure-dependent elasticity, pressure-dependent yield strength, brittle-to-ductile transition, temperature dependency, and rate dependency in the post-yielding regime. The model is validated against multiple published datasets obtained from confined and unconfined compression tests, covering various PBM compositions, confining pressures, temperatures, and strain rates. 

Fabric-based jamming phase diagram, whereF_tandZ_tare the fabric anisotropy (deviatoric invariant of the 2nd order fabric tensor) and the coordination number (mean invariant of the 2nd order fabric tensor) of the total-contact network, respectively. 

Abstract Subcritical crack growth (SCG) plays an important role in many geological processes such as delayed earth rupture and rock weathering. The complex dependency of SCG on the in‐crack fluid chemistry, however, is still poorly understood. In this study, we utilize the newly developed surface force‐based fracture theory (SFFT) to elucidate the relative contributions of surface forces and solute transport to the crack growth kinetics of calcite in NaCl solutions. Expanding on Barenblatt's cohesive crack model, SFFT introduces an effective stress intensity at the crack tip that encompasses all the relevant intermolecular forces across the crack in addition to the external far‐field stresses. The nonlinear system of equations portraying the crack opening profile, the solute distribution in a propagating crack, and the crack growth velocity are numerically solved via an implicit scheme. After carefully calibrating the model for calcite‐water systems, the SFFT is used to predict the SCG response of calcite at different NaCl concentrations, based on various hypotheses. These predictions are then compared to existing SCG data from the literature. We demonstrate that the experimentally observed variation of SCG rate with NaCl concentration cannot be explained solely by DLVO forces (electrostatic and Van der Waals interactions). This can be remediated by introducing an exponentially decaying hydration force with a nonlinear, nonmonotonic dependence on NaCl concentration. Furthermore, we demonstrate that accounting for both diffusive and advective transport of ions is important in explaining the absence of a stage‐II SCG response for calcite in electrolyte solutions. 

This study explores the tensile behavior and dynamical heterogeneity of sodium montmorillonite under extreme conditions using molecular dynamics simulations, providing insights to advance the development of clay minerals for engineering applications.