Colloidal fluids can exhibit complex phase behavior and determining phase diagrams via experiments or computer simulations can be laborious. We demonstrate that the dispersion relation ω(k), obtained from dynamical density functional theory for the uniform density system, is a highly versatile tool for predicting where in the phase diagram complex crystals form. The sign of ω(k) determines whether density modes with wave number k grow or decay over time. We demonstrate the predictive power by investigating the complex phase behavior of particles interacting via core-shoulder pair potentials. With complementary Monte Carlo simulations, we show that regions of the phase diagram where ω(k) has one or several unstable (growing) wave numbers are also where crystalline phases occur. Going further, by tuning these unstable wave numbers via the interaction-potential and state-point parameters, we design systems with quasicrystals in the phase diagram. We identify a system with a certain shoulder range exhibiting at least ten different phases. Our general approach accelerates considerably the mapping of complex phase diagrams, crucial for the design of new materials.
To address the challenge of recycling energy from low-density acoustic waves found in everyday sounds such as speech and music, we developed a soft acoustic energy harvester based on the giant magnetoelastic effect. This harvester efficiently captures energy from various environmental sound sources. It operates by combining the giant magnetoelastic effect with a spray-coating and magnetic pre-orientation process, enabling it to convert multi-directional acoustic waves into electrical energy across a wide frequency range (0-900 Hz). The magnetoelastic generator achieves a short-circuit current density of 98 μA cm-2 at a low internal impedance of 300 Ω, representing a significant improvement in current output that achieves a 100-fold increase compared to existing counterparts for acoustic energy harvesting. With inherent waterproofness and dustproofness, it can function effectively in humid or dusty conditions without extra encapsulation. The acoustic energy harvester demonstrates excellent scalability, making it suitable for diverse applications in sustainable energy systems.
Estuaries are highly dynamic systems with strong physicochemical and biological gradients that drive ecosystem functions. Increasing anthropogenic pressures have altered carbon cycling and degradation processes and reduced key ecosystem functions, leading to a marked decline in global estuarine health. This study investigates the individual and combined effects of two common anthropogenic stressors (microplastics and nitrogen) across a gradient of soft-sediment habitats with contrasting infaunal communities that reflect dominant functional traits: head-down deposit-feeding polychaetes, deep-dwelling facultative-feeding bivalves, and a mix of both. In situ rapid organic matter assays (ROMA) were used to assess whole-community organic matter degradation using media with different stressor combinations (nitrogen addition, microplastics, or both). Separate models were developed for each treatment, with predictors selected using the Akaike Information Criterion (AIC) to achieve model parsimony without compromising model fit. Our results clearly demonstrate that, in this system, single stressor models may not adequately capture organic matter cycling in sediments following exposure to multiple stressors. In single-stressor treatments, the role of sedimentary organic matter content on organic matter degradation increased significantly in plastic-treated media, and the density of a head-down deposit feeding polychaete was significantly related to the extinction rate of organic matter with sediment depth in nitrogen treated media. These relationships were decoupled when a secondary stressor was added in the multiple-stressor treatment. While direct effects of nitrogen and microplastic addition were not detected, the treatment-specific models indicate that environmental drivers of degradation vary across stressor contexts, highlighting nuanced estuarine responses to anthropogenic pressures.
Understanding how amorphous solids yield under shear is central to predicting material failure, yet prescribing reliable local yielding criteria remains a fundamental challenge. Here we introduce the soft matrix method, which creates a minimally constrained and elastically coupled environment that allows localized regions of an amorphous solid to yield naturally. This method overcomes key limitations of earlier approaches and provides a robust platform for probing failure mechanisms in soft disordered materials. Using this framework, we analyze localized yielding by systematically varying the size of the local probe region in our microscopic simulations, and we uncover an intrinsic length scale (ζ) that governs local failure, showing that ζ grows with the age of the system. The age dependence appears not only in the distribution of local yield stresses but also in the pseudogap exponent θ, which quantifies the marginal stability of amorphous solids. These insights offer a pathway toward improved elastoplastic modeling of disordered materials.
Metal‒ligand delocalization in metal complexes participating in various chemical processes was probed from the ligand side using nitrogen K-edge X-ray absorption spectroscopy (XAS). The electronic structures, spin multiplicities, and hydration structures of metal complexes in solutions with different metal ion centers, such as iron and cobalt protoporphyrin IX complexes, were investigated by N K-edge XAS. The influence of a phytol chain in chlorophyllain the solid phase was studied by the XAS measurements with a radiation-induced effect which may cleave the phytol chain. Time-resolved N K-edge XAS measurements at the 100-ps timescale were used to examine the photorelaxation process of iron phenanthroline complexes in aqueous solution. Future directions of studies of various chemical and biological processes that involve metal complexes using N K-edge XAS measurements of ligands are discussed.
Understanding how nanoparticles move near liquid-solid interfaces is central to nanoscale transport in catalysis, biology, and soft materials. Here, we uncover the physical mechanisms governing anomalous surface diffusion of PEG-coated gold nanorods (AuNRs) near the silicon nitride (SiNx) membrane in liquid-phase transmission electron microscopy (LPTEM). By systematically tuning the ionic environment (H2O, 5 mM H2SO4, 1.5 mM NaCl, 5 mM PBS), we show how electrostatic screening and ion-specific surface interactions modulate the interaction landscape, altering the strength and abundance of binding sites that govern the confinement and mobility of nanoparticles. Statistical analyses and deep learning classification of particle trajectories reveal a tunable transition between fractional Brownian motion (FBM) in strongly interacting systems (H2O, H2SO4) and annealed transient time motion (ATTM) in screened environments (NaCl, PBS). These results establish electrostatic screening and specific ion effects as external controls that program near-surface transport, shifting the diffusion mechanism from FBM to ATTM and tuning the particle mobility. To further elucidate the interfacial dynamics, we introduce a passive nanorheology framework in LPTEM, modeling the near-surface environment of FBM-classified conditions as an effective viscoelastic medium. Leveraging translational and rotational trajectories as nanoscale rheological probes, we reconstruct frequency-dependent viscoelastic moduli to extract relaxation times and elastic-to-viscous crossover moduli that report on interaction strength at the SiNx interface. Together, these advances provide both control and diagnosis of interfacial mechanical response in LPTEM, positioning it as a quantitative tool for probing nanoscale transport in complex soft-matter and interfacial systems.
Ferronematics, a class of materials capable of forming macroscopic ferromagnetic phases and offering magnetic control, were first proposed by Brochard and de Gennes over five decades ago and have recently been realized in molecular liquid crystals. However, this powerful tool remains untapped in colloidal chiral liquid crystals. Here, we report a lyotropic ferromagnetic chiral nematic liquid crystal by homogeneously dispersing anisotropic barium hexaferrite nanoplates into a cellulose nanocrystal matrix, which enables the formation of a macroscopic chiral monodomain with responsive features under weak magnetic fields (∼50 mT). We demonstrate that the nanoplates lock their surface normal to the chiral director, guiding domain alignment and coarsening while imparting a robust ferromagnetic response. This work provides a scalable platform for low-field fabrication of monodomains with applications spanning photonics, soft actuators, sensors, radiative cooling materials, and intelligent soft matter.
Atomic force microscopy-based infrared (AFM-IR) microscopy has emerged as a powerful tool for nanoscale chemical imaging, combining the topographical precision of AFM with the molecular specificity of IR spectroscopy. However, its performance is still limited by conventional metal-coated AFM probes, which provide only modest near-field enhancement, ultimately restricting both spatial resolution and chemical sensitivity. In this work, we present a nanowire-based AFM-IR approach that overcomes these limitations by introducing a probe design: Chemically synthesized noble metal nanowires are affixed to the tip of a standard AFM cantilever. These nanowires support Fabry-Pérot resonances, functioning as mid-IR antennas that generate strongly confined optical near-field, thereby enhancing spatial resolution and sensitivity. The probe design also enables stable AFM-IR operation on both hard and soft materials. We demonstrate significantly improved imaging and spectroscopic performance, achieving spatial resolution below 10 nm and sensitivity at the submonolayer level. These findings establish nanowire-based AFM-IR microscopy as a highly promising platform for superresolution vibrational spectroscopy, with broad applications ranging from soft matter and two-dimensional materials to biomolecular analysis.
Relied on three-dimensional metamaterial-based particulate-fluid system, an acoustic weave platform increases the sound pressure amplitude by frequency bandgap peak gain (Q-factor 4.5) in acoustically regular air media system with ways that a conventional method cannot. The interesting morphology alternations of the aggregation, fluidization, and trapping for numerous expanded-polystyrene particles (1-7.5 mm) were experimentally observed by engineering acoustic field in the low-frequency range of <0.8 kHz, improving the weak phenomenon in the absence of acoustic-metamaterial design. With vertical square-waveguide arrayed uniformly 12 of Helmholtz sound sources, the platform modulates the acoustic wave-packet movement and amplifies resonantly the time-spatial Y-shape-bifurcated-aggregation 54.7°-long-short-range-attraction wave phenomenon of complex macro soft-matter particles. Through experiment coinciding with simulation and theory, the main behaviors' phenomena were accurately explained by acoustic radiation force and secondary radiation force joint with the modulated three-dimensional acoustic field. The particle fluidization and trapping occur on contrary acoustic gradient fields at 220 and 250 Hz, respectively. There exist several vertically parallel "chiral" layer thin-film-aggregation stripes of millimeter-scale particles also obviously appearing at 220 Hz, more intuitively displaying the quasi-waves' constructive and destructive interferences of mm-scale particles themselves for wave-particle duality theory. The macro wavelike character helps to conveniently modulate collectively the environmental behaviors of fly-ash.
Atomic force microscopy (AFM) enables label-free nanoscale imaging and nanomechanical profiling but remains constrained by low throughput, operator dependence, and variability in data interpretation. Artificial intelligence (AI) transforms AFM into a scalable and adaptive platform. Initially applied in materials science for super-resolution imaging, tip deconvolution, segmentation, and force-curve analysis, AI approaches are now being extended to biological AFM. These methods support robust denoising of soft matter maps, automated recognition of heterogeneous structures, and three-dimensional reconstruction of biomolecular assemblies. This review provides an end-to-end workflow of AI-enabled AFM─from probe optimization and adaptive control to multimodal data integration─highlighting advances relevant to mechanobiology and biomedical engineering. By surveying studies with amyloid fibrils, extracellular vesicles, membranes, and living cells, we show how AI-AFM convergence enhances reproducibility, throughput, and clinical utility. AI-driven AFM is poised to enable disease modeling, therapeutic screening, and precision diagnostics, establishing itself as a next-generation tool for biomedical discovery.
Molecular engineering has traditionally followed a structure-function paradigm based on well-defined, folded architectures. However, intrinsically disordered proteins and regions (IDPs/IDRs) reveal that nature also exploits disorder as a functional design strategy. Here, we argue that intrinsic disorder can be understood as a biomimetic design principle for molecular and materials engineering. From a soft matter perspective, IDRs function through statistical ensembles, weak multivalent interactions, and collective behavior rather than fixed structure, with sequence features encoding a molecular grammar that governs phase behavior, viscoelasticity, and responsiveness. These principles closely parallel those found in associative polymers and colloidal systems. Recent advances in coarse-grained modeling, machine learning, and inverse design further enable disorder to be treated as a controllable engineering variable. By reframing intrinsic disorder as a programmable and bioinspired design strategy, this Perspective highlights its potential for the development of adaptive and responsive biomimetic materials.
Sol-gel phase transitions are complex far-from-equilibrium processes characterized by limited reproducibility, whose origin remains poorly understood and rarely quantified. We investigated the thermally induced sol-gel transition of agar using turbidimetry. A phenomenological model was applied to extract key kinetic parameters (maximum absorbance, maximum rate, and characteristic times) from 96 independent replicates. Variability was quantified and compared with that of an enzymatic reaction exhibiting similar sigmoidal kinetics, allowing for separation of experimental, intrinsic, and nonergodic contributions. Agar gelation displays markedly higher variability. The total variability (CV ≈ 16%) exceeds both the experimental error (1-2%) and the nonergodic contribution (≈2%), demonstrating that it predominantly arises from intrinsic process dynamics. Variability increases sharply during early stages of gelation and then evolves more gradually, indicating that stochastic nucleation and network formation pathways drive divergent kinetic trajectories despite identical initial conditions. Variability in gelation is therefore not a measurement artifact but an intrinsic hallmark of the sol-gel transition. This inherent stochasticity limits the predictive power of deterministic models, particularly at meso- and microscopic scales, and should be considered a fundamental feature of gel-forming systems. Our approach provides a quantitative framework for characterizing variability in phase transitions and may be extended to more complex biological and soft matter systems.
Chronic neural interfaces are essential for advancing brain-computer interfaces, neuroprosthetics, and neuromodulation technologies. However, a long-standing trade-off between performance and longevity persists due to the scarcity of materials that simultaneously achieve superior electrical performance, mechanical compliance, and biocompatibility. Here, we overcome this limitation with an all-organic, ultraflexible electrocorticography (ECoG) design that features a thickness of only 9 µm, achieving low electrode-tissue impedance and durability in vivo. Central to this design is a conductive hydrogel featuring an interfacial percolation (CHIP) microstructure, with tunable hydration levels and softness, achieving a highest in-plane electrical conductivity of 2,512 S cm-1. We further developed an in-plane swelling control with a dry, soft-protective etching strategy that preserves the structural integrity during hydrogel processing. The resulting all-organic ECoG array conforms to the cortical surface, minimizing foreign body response and providing exceptional signal quality, with the longest record up to 550 d.
Dysphagia and age-related oral processing limitations are rising with population aging and the growing burden of neurological diseases. Texture-modified diets remain the most common non-pharmacological intervention, yet conventional pureeing and thickening often yield meals with low visual appeal, variable textures, and diluted nutrient density, which contribute to reduced intake and malnutrition risk. Extrusion-based three-dimensional food printing, especially when combined with gel-derived edible inks, offers a digital route to standardize geometry, portioning, and texture while enabling individualized nutrition and sensory design. In the past three years, the field has progressed from simple single-ingredient pastes to engineered soft-matter systems including emulsion gels, high-internal-phase emulsion gels, Pickering-stabilized gels, bigels, and multi-material constructs enabled by dual and coaxial printing. These advances are underpinned by improved rheological windowing, microstructure engineering, and post-print gelation strategies such as ionic crosslinking, thermal setting, enzymatic bridging, and pH-triggered network formation. Meanwhile, dysphagia-oriented product development has matured from "shape recovery" demonstrations toward clinically relevant texture targets, leveraging the IDDSI tests to anchor swallowability. This review synthesizes the recent literature across materials science, food engineering, and clinical nutrition to connect gel microstructure to extrusion performance, post-processing stability, and oral processing outcomes that are relevant to older adults and dysphagia patients. We propose design principles for gel network selection, phase structuring, and process control that simultaneously satisfy print fidelity and swallowing safety targets.
Municipal solid waste landfills are highly heterogeneous ecosystems comprising solid waste, leachate, and subsoils wherein complex microbial consortia regulate organic matter degradation and contaminant transformation. However, comprehensive insights into their microbiome structure across multi-phase and depth profiles and responses to environmental gradients remain scarce. This study presents a rare, multidimensional characterization of landfill microbiomes that integrates deep drilling (to 50  m), 16S rRNA gene sequencing, and biogeochemical pathway analysis. To quantify the extent of waste stabilization, a solid-phase stabilization index (β) is proposed to link degradation stages with microbial succession. This index indicates a clear transition from a Firmicutes-dominated rapid degradation phase (β < 0.58) to a Proteobacteria-dominated stabilization phase (β > 0.83). Spatially, the microbiomes exhibit distinct solid-liquid niche partitioning, as evidenced by the prevalence of biofilm formers (Advenella and Brevundimonas) on solid waste surfaces and specific enrichment of the thermophilic planktonic Defluviitoga in the surrounding leachate. At the critical waste-soil interface, leachate infiltration exerts strong environmental filtering that drives a pronounced enrichment of the dual-tolerant Ralstonia, which constitutes up to 46.49% of the community. Cu, Be, and Cd emerge as the key heavy metals driving the evolution of subsoil microorganisms. These findings collectively provide an integrated framework that advances the mechanistic understanding of waste stabilization and leachate-soil interactions, offering new insights for assessing landfill maturity and understanding pollution fronts.
Sickle cell disease is associated with numerous musculoskeletal complications, but joint ankylosis and congenital skeletal fusion are rarely reported. We present the case of a young woman with sickle cell disease and complex multijoint pathology, including hip ankylosis of uncertain etiology and congenital hindfoot fusion. Beginning in adolescence, she underwent staged orthopedic reconstruction over more than a decade, including bilateral total hip arthroplasty, lower-extremity deformity correction, arthrodesis, treatment of prosthetic joint infection, management of recurrent soft-tissue ulceration, and multiple hardware removals. Her clinical course was complicated by impaired wound healing, infection risk, and chronic pain requiring multidisciplinary management. Despite these challenges, staged surgical intervention resulted in meaningful functional improvement and improved ambulatory capacity. This case highlights the feasibility of complex, longitudinal orthopedic reconstruction in patients with sickle cell disease while emphasizing the elevated risks of infection, wound complications, and pain management challenges.
To develop a pathology-derived radiomics signature for detecting clinically significant prostate cancer (csPCa) and to evaluate its performance using lesion diameter-based simplified segmentations. In this retrospective single-center study, 175 participants (120 radical prostatectomy cases; 55 controls) underwent biparametric MRI during 2013-2022. Whole-mount histopathology was registered to MRI using a patient-specific, mold-based 3D pipeline to generate lesion-level ground truth. Six radiologists from different institutions marked lesion diameters per Prostate Imaging Reporting & Data System (PI-RADS) v2.1, blinded to pathology; automated circular segmentations were generated from these measurements and expanded (±1 slice). Features (PyRadiomics) were preprocessed, filtered, and benchmarked via nested cross-validation. Recursive feature elimination produced a 10-feature pathology-derived radiomics signature. Models (Signature, prostate-specific antigen density [PSAD], PI-RADS, and their combinations) were trained/evaluated using a soft-voting ensemble (logistic regression, random forest, and XGBoost) with patient-level grouping; thresholds were optimized using Youden's J. DeLong's and McNemar's tests were used for model comparisons. PSAD+Signature achieved 0.75 area under the curve (AUC) and 68% (211/312) accuracy. PI-RADS+PSAD achieved 0.77 AUC and 74% (230/312) accuracy. Signature-only achieved 0.66 AUC and 62% (194/312) accuracy. A higher AUC for PSAD+Signature versus Signature (|ΔAUC|=0.093, p=0.012) and for the tripartite model versus Signature (|ΔAUC| = 0.11, p=0.007) was found. PSAD+Signature and PI-RADS+PSAD had similar accuracy (p=0.06). A histopathology-trained radiomics signature demonstrated moderate standalone performance for lesion-level csPCa detection. When combined with PSAD, diagnostic performance improved and approached that of PI-RADS+PSAD, which achieved the highest absolute accuracy. The PSAD+Signature framework offers a simplified, spatially localized approach that may complement existing PI-RADS-based assessment while maintaining low implementation complexity.
Long-range piezoelectric transduction in flexible hydrogels is limited by the "trade-off" of conductive pathways between accelerating charge dissipation and suppressing dipole formation. Here, we introduce a surface-heterojunction strategy converting cellulose nanocrystals (CNCs) into conductive dipolar nanorods by deprotonation of poly(aminophenylboronic acid) (PABA) coating. Boronated ester bonding and hydrogen-bonds at CNCs/PABA interface collectively strengthen dipole moments while inducing n-type doping in PABA and thus enabling charge transport for 18.9-fold conductivity. Integrating molecular-level interfacial alignment of conjugated moieties, CNCs with higher aspect-ratio further promote percolation and reduce threshold by ∼14%. The charge transport range in hydrogel is then elongated, which improves its conductivity by 7.0 times and yields robust piezo-electric/resistive dual-mode responsiveness. The hydrogen-bond network also improves adhesion, and imparts intrinsic sensitivity to temperature, salinity, and pH, for distinguishing complex motions and physiological events. This heterojunction-driven co-engineering of dipoles and conductivity offers a generalizable materials concept for advancing high-fidelity soft bioelectronic sensing.
Magnetic hydrogels provide powerful opportunities for engineering anisotropic soft materials, yet their mechanical response is governed by two coupled but distinct processes: (i) magnetic particle self-assembly and (ii) hydrogel network formation. Here we demonstrate that the timing of magnetic field application, rather than the field geometry alone, is a critical determinant of mechanical reinforcement. Using uni-, bi- and tri-axial magnetic fields, we systematically probe how particle structuration interacts with gelation kinetics in an oxidized laminarin-gelatin dynamic covalent hydrogel. Suspensions of particles at the same concentrations show negligible viscoelasticity compared to the hydrogel, confirming that particle networks do not directly reinforce the matrix. Instead, we show that field-induced particle structures modulate the crosslinking pathway and gelation timescale, thereby altering the final mechanical properties. Stable anisotropic reinforcement is achieved only when percolation of particle structures occurs within a temporal window in which the polymer network is sufficiently developed to immobilize them. These findings reveal temporal synchronization between particle assembly and gelation as a previously overlooked design parameter, enabling external control of hydrogel mechanics and anisotropy.
Liquid crystals (LCs) possess anisotropic mechanical and optical properties with applications ranging from soft robotics to display technology. Despite advances in the precise synthesis of liquid crystalline materials, the microscopic origins of substituent effects, which impact functional performance, are not always well understood. Here, we use molecular dynamics (MD) simulations to investigate how methyl substitution affects the nematic phase behavior of liquid crystal monomers and dimers composed of phenyl benzoate cores flanked by aliphatic tails. Methylation induces a decrease in the nematic-isotropic transition temperature. Using data-driven analysis, we find that for monomers this decrease is associated with an increase in flexibility of core-adjacent aliphatic torsions that influence overall mesogen conformation. For dimers, this manifests as a shift from a continuum of accessible conformations in the isotropic phase to occupying more distinct hairpin and extended states in the nematic phase. The latter exhibits a bend angle consistent with experimental signatures of a modulated nematic phase. Together, these results show how minor changes in chemical structure can impact the conformational ensemble of liquid crystals, trading local conformational entropy for global nematic order, in turn influencing their macroscopic transition temperatures.