ACS Publications最新文献

The impact of droplets on a liquid pool is widely present in various fields such as inkjet printing, spray coating, and oil recovery. While the influence of the droplet phase, such as viscosity and surface tension, on impact dynamics has been extensively investigated, the role of the pool phase, especially in the viscoelastic scenario, remains less understood. In this work, we experimentally and numerically study the impact dynamics of oil droplets on an immiscible liquid pool of polymer aqueous solutions. We find that after droplet impact, the elasticity of the viscoelastic liquid pool could enhance the curvature of the cavity, reduce its depth, and shorten the length of the forward jet, which are further confirmed by direct numerical simulations. Intriguingly, we also find a nonmonotonic dependence of the threshold of Reynolds number for droplet bouncing on the polymer concentration, which can be linked to the critical overlap concentration of the polymer solution. Our findings enrich the understanding of droplet impact onto viscoelastic liquids and may contribute to the development of advanced technologies, for instance, 3D printing and droplet encapsulation.

Mechanotransduction is a fundamental cellular process. Intercellular communication of mechanotransduction integrates cells, irrespective of whether they are of the same or different types, into a cohesive functional unit, which plays a critical role in tissue and organ regeneration, cell differentiation, cell division, and responses to external stimuli. However, research on intercellular communication in mechanotransduction remains underexplored owing to the absence of highly efficient techniques for the real-time, in situ acquisition of biochemical information at the single-cell level. In this work, we developed an electrochemical analysis method to investigate the pathways and dynamics of lamellipodium-mediated intercellular communication. Specifically, an electric field-driven strategy was developed to fabricate microdisk electrochemical sensors based on poly(3,4-ethylenedioxythiophene)/single-walled carbon nanotubes (PEDOT/SWCNTs), followed by decoration with Au nanoparticles to prepared Au/PEDOT/SWCNTs. The resulting Au/PEDOT/SWCNTs microdisk electrochemical sensor exhibits exceptional electrochemical performance. As a concept application, this Au/PEDOT/SWCNTs microdisk electrochemical sensor was employed to monitor NO release during intercellular communication of mechanotransduction between human umbilical vein endothelial cells (HUVECs). Our findings demonstrated that lamellipodia can transmit mechanical stimulation from a stimulated HUVEC to a recipient HUVEC connected via lamellipodia, thereby triggering NO production and release in the recipient cells. The transmission takes approximately 70 ± 20 ms, with a transmission efficiency of approximately 77.2%. This study provides novel insights into the lamellipodia-mediated intercellular communication in mechanotransduction and offers a method for investigating such processes.

Atomistic structures of materials offer valuable insights into their functionality. Determining these structures remains a fundamental challenge in materials science, especially for systems with defects. While both experimental and computational methods exist, each has limitations in resolving nanoscale structures. Core-level spectroscopies, such as X-ray absorption (XAS) or electron energy-loss spectroscopies (EELS), have been used to determine the local bonding environment and structure of materials. Recently, machine learning (ML) methods have been applied to extract structural and bonding information from XAS/EELS data. However, frameworks relying solely on a single data stream, defined as characterization data derived from a single element using one technique, are often insufficient because multiple local environments can yield similar spectral features, making it challenging to differentiate between competing structural hypotheses. In this work, we address this challenge by integrating multimodal ab initio simulations, experimental data acquisition, and ML techniques for structure characterization. Our goal is to determine local structures and properties using EELS and XAS data from multiple elements and edges. To showcase our approach, we use various lithium nickel manganese cobalt (NMC) oxide compounds which are used for lithium ion batteries, including those with oxygen vacancies and antisite defects, as the sample material system. We successfully inferred local element content, ranging from lithium to transition metals, with quantitative agreement with experimental data. Beyond local element inference, we find that ML model based on multimodal spectroscopic data is able to determine whether local defects such as oxygen vacancy and antisites are present, a task which is impossible for single mode spectra or other experimental techniques. Furthermore, our framework is able to provide physical interpretability, bridging spectroscopy with the local atomic and electronic structures.

As an emerging modality for treatment, photothermal therapy demonstrates significant potential for clinical application. However, the inflammatory reaction after photothermal therapy can lead to tumor recurrence and metastasis. As a novel photothermal agent, biliverdin (BV) also demonstrates a remarkable anti-inflammatory effect. In this study, goat milk-derived extracellular vesicles (GEVs) is used to encapsulate BV. The objective was to enhance tumor uptake of the photothermal agent while alleviating the inflammatory responses associated with photothermal therapy, thereby achieving superior therapeutic outcomes. N₃-GEV@BV was successfully synthesized. Additionally, it exhibited notable efficacy in photothermal therapy and demonstrated anti-inflammatory effects in vitro. Utilizing a pretargeting strategy, N₃-GEV@BV can accomplish PET/CT imaging in both subcutaneous and orthotopic tumor models. After photothermal treatment, the tumor volume in the N₃-GEV@BV+laser group exhibited a significant decrease relative to the other groups, with reductions of up to 1/13 observed. Furthermore, compared to N₃-GEV@ICG, mice injected with N₃-GEV@BV exhibited lower expression levels of inflammatory factors in both the serum and tumor tissues. As an integrated nanoprobe for diagnosis and treatment, N₃-GEV@BV can successfully mediate the photothermal therapy of tumor tissue. Notably, it contributes to enhanced tumor prognosis by mitigating the inflammatory response induced by photothermal therapy, underscoring its broad potential for application.

α-L-Rhamnosidases are a class of glycosyl hydrolases (GHs) that catalyze the hydrolysis of terminal α-L-rhamnose residues from diverse glycoconjugates. While extensively characterized in bacterial and fungal sources, no archaeal α-L-rhamnosidases have been characterized to date. Herein, we report the identification and characterization of the first thermostable archaeal α-L-rhamnosidase (ArRha), derived from the metagenomic data set of Pisciarelli solfatara hot spring. ArRha, classified in glycoside hydrolase family GH78, efficiently hydrolyzes α-1,2 and α-1,6 rhamnosyl linkages in flavonoid glycosides with notable biological activities. The novel enzyme showed remarkable temperature stability, wide-range pH activity, organic solvent tolerance, and no metal dependence. Combined with a thermostable β-glucosidase, ArRha converts naringin to prunin and naringenin in sweet and blood orange juices, achieving >95% conversion within 2 h at 65 °C. This represents the first report of a hyperthermostable archaeal GH78 α-L-rhamnosidase with promising applications in industrial enzymatic juice debittering and sustainable flavonoid biotransformation.

Machine learning potentials (MLPs) have emerged as powerful simulation tools for heterogeneous catalysis. While current MD-active-learning workflows excel at fitting a specific system/reaction pathway through iterative structural sampling, the transition toward more general and transferable MLPs, designed to handle diverse structures and reactive events within a defined chemical space, presents fundamentally new challenges. Such generality often requires highly diverse, nonequilibrium training data, for which standard practices may confront challenges regarding training discipline and evaluation logic. Here, using our recently developed REICO method as an example to generate such data sets, we systematically investigate the distinct pathologies that arise when training on such diverse data, revealing critical deviations from standard system-specific MLP training. We further provide detailed recommendations on data cleaning, model selection, and error metrics for both numerical performance and physical validation, offering practical guidance for training MLPs with diverse and hybrid data sets.

Organic room-temperature phosphorescence (RTP) materials have significant application potential in anticounterfeiting and biological imaging due to their large Stokes shifts and long phosphorescence lifetimes. Conventional RTP materials, which typically contain aromatic structures, often involve complex preparation processes and exhibit limited biocompatibility. In this work, a series of nontraditional intrinsic cluster-emitting polymeric materials with RTP properties were developed by utilizing strong ionic bonds and spatial confinement effects. Specifically, RTP-emitting derivatives of poly(maleic anhydride-alt-vinyl acetate) (PMV) were obtained via alkaline hydrolysis. The introduction of montmorillonite (MMT) enabled the construction of a nacre-mimetic structure through electrostatic interactions and ionic cross-linking between the layered inorganic framework of MMT and ionic sites on polymer chains, combined with the spatial confinement effect of MMT. By optimizing the reaction conditions, the resulting materials show improved photophysical properties, with a maximum phosphorescence lifetime of 30.5 ms and a maximum quantum yield of 16.09%. This study provides an alternative strategy for developing high-performance nontraditional luminescent polymers that do not require aromatic structures or heavy atoms.

Electrocatalytic CO₂ reduction (eCO₂R) under acidic conditions mitigates carbon crossover and energy losses, yet selective multicarbon synthesis remains challenging due to competing hydrogen evolution. Conventional efforts manipulate the electrochemical double layer to enrich alkali cations but reach steric limits at industrially relevant current densities, compromising selectivity and stability. Here, we introduce an ion-gated porous overlayer (IGPO) that extends beyond nanometric constraints, creating a volumetric ion-management zone decoupling catalytic surfaces from bulk electrolyte dynamics. Our hierarchical architecture comprises porous carbon nanocages (PCNs) and polymeric triazine nanocage layers on the Cu catalyst. Theoretical modeling reveals this design displaces K⁺ concentration peaks from the catalyst to outer PCN surfaces while attenuating H₃O⁺ across the porous network. Protonated triazine groups enforce the Donnan exclusion of H₃O⁺ and retard OH^- egress, sustaining locally alkaline microenvironments. Incorporating single-atom nickel sites enables in situ CO generation, enhancing multicarbon formation through tandem catalysis. The optimized electrode achieves 61.1% Faradaic efficiency for ethylene and 86.2% for total C₂₊ products at 400 mA cm^-2 under acidic conditions, with stable operation exceeding 220 h. This ion-gated strategy provides a generalizable framework for overcoming selectivity-stability trade-offs, advancing carbon-neutral chemical manufacturing.

Gene therapy has emerged as a powerful approach for treating diverse diseases, including genetic disorders, retinal diseases, and certain cancers. Real-time, noninvasive in vivo tracking of gene expression is essential for evaluating therapeutic efficacy. β-galactosidase (β-gal), a hydrolase encoded by the Escherichia coli lacZ gene or the human GLB1 gene, is widely used as a reporter of gene expression. In humans, β-gal deficiency underlies several fatal neurodegenerative disorders, including GM1 gangliosidosis. Here, we report the development of a β-gal-activated, human serum albumin (HSA)-binding gadolinium(III)-based MR contrast agent for noninvasive assessment of adeno-associated virus (AAV) gene therapy in GM1 gangliosidosis mice. The probe exhibited a gradual increase in MR relaxation rate upon incubation with β-gal in the presence of 4.5% HSA. Following intravenous administration, AAV-treated GM1 mice demonstrated distinct MR signal enhancement and kinetic profiles compared to untreated β-gal-deficient controls. This study establishes an enzyme-activated, protein-binding MR imaging strategy for real-time, noninvasive monitoring of AAV gene therapy.

The advancement of personalized medicine is increasingly reliant on wearable health monitoring technologies. While hydrogels offer great promise for skin-integrated sensors due to their biocompatibility, flexibility, and conductivity, developing materials that simultaneously possess robust mechanical properties, reliable adhesion, and antimicrobial efficacy remains a challenge. To address the critical need for monitoring nocturnal respiratory abnormalities in heart failure patients, we have successfully developed a hydrogel composed of acrylic acid (AA), carboxymethyl cellulose (CMC), double-bond-modified sodium lignosulfonate (DLS), and polydopamine-modified nanohydroxyapatite loaded with silver particles (PDA-nHAP@Ag). The developed hydrogel sensor exhibits a suite of superior properties, including high conductivity (1.1 S/m), excellent mechanical strength (0.41 MPa tensile stress, 1295% strain), and high sensitivity (gauge factor of 5.58), enabling stable skin adhesion. It demonstrated outstanding biocompatibility (>98% cell viability) and potent antibacterial activity (97% inhibition against both E. coli and S. aureus). Furthermore, the sensor showed remarkable durability, maintaining signal stability over 2500 cycles in tests monitoring joint bending and laryngeal movements. In practical application, the system captured electrocardiogram signals with superior waveform clarity compared to commercial electrodes. Most importantly, through multichannel signal analysis, it achieved precise classification of breathing patterns (fast, normal, slow) in heart failure patients with an accuracy of 97.8%. This work overcomes the limitations of conventional single-function systems by integrating electrophysiological monitoring, motion sensing, and respiratory analysis, providing a viable pathway toward next-generation intelligent health monitoring platforms.