Cell Reports Physical Science最新文献

Porphyrinic metal-organic frameworks (MOFs) offer high surface areas and tunable catalytic and optoelectronic properties, making them versatile candidates for applications in phototherapy, drug delivery, photocatalysis, electronics, and energy storage. However, a key challenge for industrial integration is the rapid, cost-effective production of suitable sizes. This study introduces Zr(IV) alkoxides as metal precursors, achieving ultrafast (∼minutes) and high-yield (>90%) synthesis of three well-known Zr-based porphyrinic MOF nanocrystals: MOF-525, PCN-224, and PCN-222, each with distinct topologies. By adjusting linker-to-metal and modulator-to-metal ratios, we attain precise control over single-phase formation. Demonstrating alkoxides' potential, we synthesized nanosized PCN-224 at room temperature within seconds using a continuous multifluidic method. This advancement greatly simplifies porphyrinic MOF production, enabling broader industrial and scientific applications.

Graph neural networks (GNNs) have emerged as powerful tools for representation learning. Their efficacy depends on their having an optimal underlying graph. In many cases, the most relevant information comes from specific subgraphs. In this work, we introduce a GNN-based framework (graph-partitioned GNN [GP-GNN]) to partition the GNN graph to focus on the most relevant subgraphs. Our approach jointly learns task-dependent graph partitions and node representations, making it particularly effective when critical features reside within initially unidentified subgraphs. Protein liquid-liquid phase separation (LLPS) is a problem especially well-suited to GP-GNNs because intrinsically disordered regions (IDRs) are known to function as protein subdomains in it, playing a key role in the phase separation process. In this study, we demonstrate how GP-GNN accurately predicts LLPS by partitioning protein graphs into task-relevant subgraphs consistent with known IDRs. Our model achieves state-of-the-art accuracy in predicting LLPS and offers biological insights valuable for downstream investigation.

Disordered single-stranded RNA (ssRNA) molecules, like their well-folded counterparts, have crucial functions that depend on their structures. However, since native ssRNAs constitute a highly heterogeneous conformer population, their structural characterization poses challenges. One important question regards the role of sequence in influencing ssRNA structure. Here, we adopt an integrated approach that combines solution-based measurements, including small-angle X-ray scattering (SAXS) and Förster resonance energy transfer (FRET), with experimentally guided all-atom molecular dynamics (MD) simulations, to construct structural ensembles of a 30-nucleotide RNA homopolymer (rU30) and a 30-nucleotide RNA heteropolymer with an A-/C-rich sequence. We compare the size, shape, and flexibility of the two different ssRNAs. While the average properties align with polymer-physics descriptions of flexible polymers, we discern distinct, sequence-dependent conformations at the molecular level that demand a more detailed representation than provided by polymer models. These findings emphasize the role of sequence in shaping the overall properties of ssRNA.

Controlled tailoring of atomically thin MXene interlayer spacings by surfactant/intercalants (e.g., polymers, ligands, small molecules) is important to maximize their potential for application. However, challenges persist in achieving precise spacing tunability in a well-defined stacking, combining long-term stability and dispersibility in various solvents. Here, we discovered that lignin can be used as surfactants/intercalants of Ti₃C₂T_x MXenes. The resulting MXene@lignin complexes exhibit superior colloidal stability and oxidation resistance in both water and different organic solvents. More important, we reveal a dynamic interaction between MXene and lignin that enables a wide-range fine interlayer distance tuning at a sub-nanometer scale. Such dynamic interaction is sparse in the reported organic surfactants/intercalants containing single types of functional groups. We also demonstrate the tunability of electrical conductivity, infrared emissivity, and electromagnetic interference shielding effectiveness. Our approach offers a starting point to explore the potential of MXene-biomacromolecule composites for electronics and photonics applications.

The rules of how amino acids dictate the physical properties of biomolecular condensates are still incomplete. Here, we study condensates formed by tetrapeptides of the form XXssXX. Eight peptides form four types of condensates at different concentrations and pHs: droplets (X = F, L, M, P, V, and A), amorphous dense liquids (X = L, M, P, V, and A), amorphous aggregates (X = W), and gels (X = I, V, and A). The peptides exhibit differences in phase equilibrium and material properties, including a 368-fold range in the threshold concentration for phase separation and a 3,856-fold range in viscosity. All-atom molecular dynamics simulations provide physical explanations of these results. The present work also reveals widespread critical behaviors-including critical slowing down manifested by amorphous dense liquids and critical scaling obeyed by fusion speed-with broad implications for condensate functions.

Tracking genomic sequences as microbial biomarkers in wastewater has been used to determine community prevalence of infectious diseases, contributing to public health surveillance programs worldwide. Here, we report upon a low-cost, rapid, and user-friendly paper microfluidic platform for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza detection, using loop-mediated isothermal amplification, with signal read using a mobile phone camera. Sample-to-answer results were collected in less than 1.5 h, providing rapid multiplexed detection of viruses in wastewater, with a detection limit of <20 copies mL⁻¹. The device was subsequently used for on-site testing of SARS-CoV-2 in wastewater samples from four quarantine hotels at London Heathrow Airport, showing comparable results to those obtained using polymerase chain reaction. This sensing platform, which enables rapid and localized testing without requiring samples to be sent to centralized laboratories, provides a potentially important public health tool for pandemic preparedness, with a variety of future wastewater surveillance applications in community settings.

A common failure mode for solid-state lithium-metal batteries is solid-electrolyte fracture during lithium plating, but fracture initiation is complicated to diagnose. Here, an electrochemically and mechanically coupled steady-state lithium-plating model is implemented numerically to study fracture initiation at the lithium/solid-electrolyte interface. The solid electrolyte is treated as a linear elastic solid, while lithium is modeled as a Newtonian fluid. An electrochemical connection between the two phases is made via the stress-modified Butler-Volmer equation at the Gaussian-curved interface, where lithium protrudes into the solid electrolyte. The model simulations demonstrate that the couplings result in significantly different electrochemical and mechanical behaviors from those predicted by the model without the couplings. The J-integrals—an indicator of fracture—of the coupled and uncoupled models are six orders of magnitude apart. The coupled model supports a shear-traction-driven fracture concentrated at the asperity base instead of the commonly attributed pressure-driven fracture at the asperity tip. Finally, our sensitivity analysis reveals that lithium pseudo-viscosity and asperity geometry are important parameters determining fracture initiation.

Doping is a key strategy for enhancing the charge mobility and thermoelectric properties of polymers. While advancements utilizing the anion exchange technique have notably enhanced doping efficiency, there is a need for further optimization of the doping process. This study introduces a two-step doping approach combining solid-state diffusion with anion exchange, applied to poly[2,5-bis(3-tetradecylthiophen-2-yl)thieno[3,2-b]thiophene] (PBTTTC14) films. Initial 2,3,5,6-tetrafluoro-7,7,8,8-tetracyano-quinodimethane (F4TCNQ) diffusion doping followed by anion exchange with F4TCNQ/ionic liquid achieved higher conductivity than one-step anion exchange doping. Spectral and structural analyses elucidated the enhanced doping mechanism. Additionally, adjusting the molecular weight (MW) of PBTTTC14 from 11,867 to 175,199 improved doping levels and conductivity, reaching 1,103.8 S cm⁻¹. A medium MW (MW = 99,407) optimized thermoelectric performance by balancing conductivity and Seebeck coefficients. These findings provide insights into controlling doping and performance of conductive semiconductor polymers through a two-step doping process and MW engineering.

Collagen fibrils are the building blocks of many tissues from fish scales and tendons to bone. Synchrotron small-angle X-ray scattering (SAXS) with in situ mechanical testing is a powerful tool to investigate collagen fibril deformation. There is a need to combine data from SAXS studies to investigate structure-function relationships. A literature search used the concepts of mechanical properties, collagen, and SAXS, with 52 articles meeting the eligibility criteria. Here, we report that mineralized tissues transfer a greater proportion of tissue-scale deformation to the fibril: 67% for cortical bone, 49% for tendon, 10% for ligament, and 3% for skin. Across non-mineralized tissues, tissues with less complexity and greater elastin content transfer less deformation to the fibril. The meta-analysis finds 20%–40% lower fibril strain in human aging and disease compared to controls, which contributes toward fracture risk. This synthesis demonstrates how variations in composition and structure tune material properties in collagen-based tissues.