The Royal Society of Chemistry最新文献

Flame-retardant coatings provide effective fire protection for various substrates, yet developing eco-friendly alternatives that combine strong adhesion, high-efficiency flame retardancy, and excellent thermal insulation remains a formidable challenge. Inspired by the nesting behavior of birds, a fully biomass-based fire-retardant coating without traditional flame-retardant elements was constructed through a green multiple-groups synergy strategy for "one stone for multiple birds" that concurrently incorporates nanostructuring, strong adhesion, thermal insulation, and universal flame retardancy. In this design, gallic acid (GA) self-assembles into nanofibrous-like supramolecular aggregates through π-π stacking, mimicking structural "twigs". Meanwhile, chitosan acts as a cohesive binder, replicating the adhesive function of "saliva". The resulting coating exhibits a bird nest-like interpenetrating structure with nanopores (<250 nm), which reduces the thermal conductivity of rigid polyurethane foam (RPUF) to 24.85 mW (m K)^-1 from 28.57 mW (m K)^-1. The synergy of decarboxylation/carbonization and radical scavenging imparts self-intumescent barrier properties and universal flame retardancy to diverse materials (fabric, RPUF, paper, wood), yielding a limiting oxygen index of 25-30%, and smoke and toxic gas suppression. This work presents a biomimetic strategy for sustainable, high-performance flame-retardant coatings with broad applicability.

The widespread use of plastics and their improper disposal have released a large number of micro- and nano-plastics (MNPs) into various environmental media. Although the release of MNPs from individual plastic products has been widely reported, there is a lack of a holistic assessment framework to determine the overall release of plastic products to soil, water, and air during their life cycle. Therefore, based on big data, neural network algorithms, and material flows, a new open platform for the comprehensive assessment of the release of MNPs from plastic products will be developed. The proposed emission inventory platform consists of three main modules: a global polymer product production dataset, an assessment of the emission processes, influencing factors, and emission factors of MNPs, and an emission inventory of MNP releases to the environment. The global data on polymer production, use, and waste disposal, and collate data on the degradation behavior of different plastic types under various environmental conditions will be collected. Next, big data analysis will be applied to train the patterns of MNP production and emissions, and algorithms such as neural networks will be used to simulate the complex processes and mechanisms of MNP emissions. Finally, a comprehensive emission inventory model will be established. The proposed dynamic MNPs emission assessment platform integrates material flow analysis and experimentally validated release kinetics. Utilizing machine learning techniques and laboratory and field datasets, the platform can derive dynamic, environment-specific emission factors to support specific emission estimates, source prioritization, and targeted emission reduction strategies.

Stable oil-in-dispersion emulsions are achieved at ultralow additive levels (0.006 wt% EG-NPs and 0.03 mM SDS) through a same-charge cooperative mechanism between anionic SDS and negatively charged EG-NPs. Electrostatic repulsion ensures stability, enabling reversible control and broad oil compatibility, thereby offering a sustainable, low-concentration, and environmentally benign emulsification strategy.

Engineering unique architectures at the nanoparticle and colloidal scales represents a promising strategy for harnessing physicochemical interparticle interactions, particularly to enhance near-field light focusing. Although electric fields tend to concentrate at regions of high curvature, such as sharp tips, the presence of the latter features alone does not substantially strengthen the near-field enhancement. Instead, directly assembling two sharp tips in a tip-to-tip configuration represents an effective way to maximize near-field focusing by generating highly localized electromagnetic "hot spots". To achieve this goal, we introduce an innovative approach for obtaining a tip-to-tip assembly of octahedral nanoparticles. This strategy involves encapsulating solid octahedral nanoparticles within cubic shells, serving as structural building blocks, to form point contacts between the flat surfaces of the cubic shell and the sharp tips of the octahedron. By arranging these distinctive structures in a serial configuration, we achieve a controlled tip-to-tip alignment. Within this architecture, the inner tips induce charge concentration on the flat planes, while the serial arrangement further enhances near-field focusing across adjacent building blocks. This configuration exhibits distinct near-field characteristics compared to assemblies composed of simple solid cubes or isolated octahedral nanoparticles, thus providing a novel strategy for optimizing near-field interactions in nanoscale systems.

Early detection of cancer biomarkers is crucial for improving patient survival rates. Herein, a highly sensitive label-free electrochemical immunosensor for prostate-specific antigen (PSA) was constructed based on in situ grown Ag₂S nanoparticle-decorated MoS₂ nanosheets (Ag₂S@MoS₂). The Ag₂S@MoS₂ nanocomposite synergistically combined the large surface area of MoS₂ with the high conductivity of Ag₂S, significantly amplifying the electroactive surface area and enhancing electron transfer. Upon formation of the insulating PSA-antibody immunocomplex, electron transfer was hindered, leading to a measurable current decrease. Under the optimal conditions, the sensor demonstrated a wide logarithmic linear range from 0.5 pg mL^-1 to 50 ng mL^-1 and an ultralow detection limit of 0.17 pg mL^-1 (based on 3σ/S). More importantly, the immunosensor demonstrated high selectivity, reproducibility, and, crucially, reliable accuracy in the detection of PSA in spiked human serum samples. This work not only provided a highly sensitive tool for PSA detection but also established a versatile and powerful sensing platform based on synergistic interface engineering, with broad potential for the detection of other disease biomarkers.

Titanium (Ti) alloys have low thermal conductivity, suffer from tool wear and deformation of workpieces and are difficult-to-machine metals. This contributes to surface roughness, S_a > 240 nm of Ti alloys after mechanical polishing with a low material removal rate (MRR). With the addition of assisting energy fields, the MRR is usually lower than 7 μm h^-1. Nevertheless, there is a high demand to achieve S_a < 50 nm on a free surface blade to save energy and reduce the resistance of fluids. To address this challenge, novel photocatalytic shear-thickening chemical mechanical polishing (PSTCMP) was developed using a custom-made polisher. The new PSTCMP slurry contained ceria, corn starch, sodium bicarbonate and deionized water. After PSTCMP, the S_a and thickness of the damaged layer of a free surface blade of a Ti alloy decreased from 501.71 to 38.46 nm and from 634.79 to 7.83 nm, respectively, representing reductions of 92% and 99%. The MRR is 12.52 μm h^-1. To the best of our knowledge, both the S_a and MRR are the best published to date for a Ti alloy blade with a free surface. PSTCMP mechanisms were interpreted using first-principles molecular dynamics, X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy. Hydroxyl radicals were generated under ultraviolet irradiation on ceria with a size of 4.2 nm, oxidizing the surface of the Ti alloy and forming Ti-OH and Ti-O groups. A Ce-O-Ti interface bridge was produced between Ti-OH and Ce-OH, induced by the hydrolysis of ceria. Our findings provide a new way to fabricate nanometer-scale surface roughness on a free surface blade of a Ti alloy with a high MRR.

In complex reaction systems such as energy conversion and environmental catalysis, the traditional "structure-performance" relationship framework centered around active sites is gradually revealing limitations, such as insufficient regulation dimensions and rigid reaction pathways. In recent years, curvature engineering and spin degree of freedom regulation have emerged as new paradigms for structural and electronic dimension control, showing potential to break through the limitations of conventional catalytic pathway selectivity and reaction rates. Although previous studies have reported catalytic responses to strain induced by curvature and spin-polarization effects, the intrinsic coupling mechanism between the two remains underexplored and lacks systematic summarization and theoretical unification. This review proposes a "curvature-spin-catalytic dynamics triple coupling frontier mechanism", aiming to elucidate how non-uniform geometric perturbations at the nanoscale collaboratively drive electronic structure reconstruction, spin state transitions, and reaction barrier adjustments. The physical origins, microscopic pathways, and experimental characterization related to this coupling mechanism are integrated across scales. Beginning with lattice distortion-induced d-orbital reorganization and crystal field regulation, the discussion extends to enhanced orbital-spin coupling, spin-filtered electron transfer, and pathway differentiation, further connecting to dynamic feedback, self-regulating active platform construction, and multi-physical field responsive regulation. This review also summarizes key advances in related in situ characterization techniques, first-principles simulation systems, and multi-field coupling configurations. This review not only fills the gap in the catalysis field regarding the triple coupling mechanism of structure-electron-reaction pathways, but also provides a paradigm framework and cross-scenario guidance for the development of next-generation programmable and responsive catalytic systems.

Optically active indolines are valuable structural motifs present in numerous naturally occurring and biologically active molecules. Although several methodologies have been reported in the literature for the synthesis of chiral indolines, many of them rely on the hydrogenation of indoles using expensive metal catalysts. In this report, a copper(II)-catalysed enantioselective (4 + 1) cycloaddition of aza-o-quinone methides (aza-o-QMs) with bromomalonates to access indolines is described. The reactive aza-o-QMs are generated in situ from simple and easily accessible 2-chloromethyl arylsulfonamides under basic conditions, and subsequently undergo cyclization with the in situ formed bromomalonate anion to deliver diverse chiral indoline derivatives in up to 69% yields and 96 : 4 er. Scale up and further derivatizations occurred without erosion of enantioselectivity, showing the robustness of this methodology.

As compelling evidence of global warming, heatwaves are expected to elevate O₃ mixing ratios in highly polluted urban areas. In summer 2018, Seoul, an Asian megacity, experienced elevated O₃ levels in conjunction with a temperature surge (maximum of 38.9 °C). This study quantitatively estimates the O₃-climate penalty through measurements of nitrogen oxide species, volatile organic compounds (VOCs), and the boundary-layer height, as well as model simulations. The results highlight an acceleration of O₃ concentration increment with increased temperature and elevated ozone production efficiency in NO_x-saturated conditions. Furthermore, it emphasizes the importance of dynamic boundary-layer processes and increased VOC concentrations resulting from fugitive emissions during the heatwave.

Biological barriers protect the human body by selectively blocking foreign material. Designing particles with coatings that efficiently transport across these barriers can increase the effectiveness and feasibility of advanced therapeutics. In particular, the mucus barrier protects the intestines, lungs, eyes, etc., complicating oral, inhaled, or ocular drug delivery. Heuristics for particle design are currently limited to the rate of diffusion within the barrier. Relying on first-principles theories for colloidal scale interactions, a cohesive model of the transport of particles through biological barriers is developed based on the barrier permeability, which incorporates essential contributions from both partitioning and diffusion. Analytical models are developed to predict partition coefficients based on particle-pore interaction potentials. Particle-pore hydrodynamics are considered to predict average diffusivities within mucus barriers. We show that kT-scale attractive interactions, that are either specific or non-specific, can yield optimal delivery of larger particles, to increase the mass flux across mucus barriers by an order of magnitude, and enable delivery of macromolecular cargo, due to enhanced partitioning. Our model indicates drug particle design rules to achieve transport rates comparable to or exceeding what is possible by viruses with highly evolved chemical and physical characteristics.