SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-026-4249-2
Underwater bubble manipulation is critical for water electrolysis, heat transfer, and mineral flotation, yet existing strategies relying on buoyancy or Laplace gradient forces from asymmetric surface geometries suffer from limited flexibility and narrow applicability. This work introduces a temperature-responsive anisotropic cilia surface (TRAS) that achieves bidirectional long-range bubble transport by modulating elastic modulus and stiffness. The TRAS enables precise control over the asymmetric three-phase contact line and viscous resistance, facilitating reversible bubble motion. Experimental validation using aqueous ethanol droplets with varying surface tensions (73.16 mN/m for 0 vol% to 22.27 mN/m for 100 vol%) on cilia with center-to-center spacings of 0.2–1.0 mm reveals that transport direction depends on both cilia spacing and liquid surface tension. Droplets of 0 vol% and 20 vol% ethanol exhibit sustained reverse transport on hard cilia, while 60 vol%, 80 vol%, and 100 vol% solutions show sustained forward transport. Notably, 40 vol% ethanol droplets display bidirectional transport at 0.6 mm spacing, reverse transport at 0.8 and 1.0 mm, and forward transport at 0.2 and 0.4 mm. These results demonstrate that tuning surface tension and cilia spacing provides a versatile platform for directional bubble manipulation, with promising applications in heat transfer, electrochemistry, and gas handling systems.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-026-4369-1
Sodium-ion batteries (SIBs) are promising alternatives to lithium-ion batteries for large-scale energy storage due to sodium's abundance and low cost. Among cathode materials, polyanionic compounds like Na3V2(PO4)2O2F (NVPOF) offer high energy density and dual voltage plateaus at ~3.6 and 4.0 V, but suffer from low electronic conductivity and sluggish Na+ diffusion. Here, we report a dual-modulation strategy combining high-valence Nb5+ doping and polydopamine-derived carbon coating to synthesize Na3V1.94Nb0.06(PO4)2O2F-C (NVPOF-Nb-C) via a hydrothermal route. X-ray diffraction and Rietveld refinement confirm that Nb5+ doping induces slight lattice expansion without altering the tetragonal I4/mmm framework. Density functional theory calculations reveal that Nb5+ doping optimizes the crystal structure and reduces the Na+ diffusion barrier, while the uniform carbon coating enhances electron transport. Consequently, NVPOF-Nb-C exhibits remarkably improved electrochemical performance, including high reversible capacity, excellent rate capability, and ultralong cycling stability. In a full cell with hard carbon anode, it delivers a high energy density of 487.2 Wh kg−1 at 1C and retains 91.51% capacity after 3000 cycles at 20C. This work provides a synergistic strategy to overcome the intrinsic limitations of polyanionic cathodes for practical SIB applications.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3686-6
Fused silica (SiO2) exhibits exceptional thermal stability and dielectric properties, making it an attractive material for aerospace and military applications. However, its relatively poor mechanical performance has limited its widespread practical utilization. This study proposed an innovative approach to fabricate SiO2-hexagonal boron nitride (hBN) composite ceramics via spark plasma sintering (SPS), leveraging the high-temperature phase transformation of cubic boron nitride (cBN) to introduce randomly oriented hBN as a reinforcing phase within the SiO2 matrix. The randomly oriented hBN nanoplates allow cracks to propagate along stronger grain boundaries, rather than along weaker interlayers of hBN, significantly improving the overall strength and fracture toughness of the composite. The maximum flexural strength and fracture toughness achieved are 183.4 MPa and 2.06 MPa m1/2 respectively, which are 3.6 times and 4 times that of fused SiO2. Concurrently, the composites exhibit low dielectric constants (ε = 3.58–3.69) and dielectric losses (tan δ < 0.0087) at 1 MHz. This work successfully enhanced the mechanical performance of fused SiO2 while preserving its excellent dielectric characteristics, opening new possibilities for its potential applications in advanced structural and functional fields.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3925-5
Capacitive pressure sensors have garnered significant attention in electronic skin, human-machine interaction, health monitoring, and medical devices due to their remarkable properties like highly sensitive pressure perception, good repeatability, and rapid response capabilities. However, manufacturing capacitive pressure sensors that simultaneously achieve a broad linear detection range and high sensitivity remains a significant challenge. Herein, a novel hierarchically interlocked capacitive pressure sensor (HI-CPS) was designed by integrating a stretchable polyethylene glycol (PEG)-based nanofilm dielectric layer with hierarchically interlocked microstructures, demonstrating excellent linearity and high sensitivity over a wide sensing range. HI-CPS based on a one-layer nanofilm exhibits ultrahigh sensitivity (9.40 kPa−1) and an ultralow detection limit (0.1 Pa). When the dielectric layer comprises two layers of stacked nanofilms, the sensor not only maintains high sensitivity (3.17 kPa−1) but also achieves excellent linearity (R2 = 0.999) over a broad working range (<5 kPa), along with remarkable stability even after 10,000 cycles. Benefitting from the outstanding comprehensive performance, HI-CPS has been proven to be successfully implemented in monitoring various human biological signals, sign language recognition, and basketball shooting gesture correction. This strategy of assembling the tailored nanofilm with structural engineering has significant potential application in building high-performance pressure detection and recognition devices.
Environmental Chemistry•2026•DOI: 10.7524/j.issn.0254-6108.2025032704
Chlorogenic acid (CGA), a key component of the anti-COVID drug Lianhua Qingwen, is recalcitrant to biodegradation and tends to bioaccumulate, posing risks to aquatic ecosystems. Conventional water treatment methods are inadequate for its removal. This study investigated the degradation of CGA using a magnetic field coupled Fe-C activated persulfate (MF/Fe-C/PS) advanced oxidation process. The degradation efficiencies of Fe-C, PS, Fe-C/PS, and MF/Fe-C/PS systems were compared, and the dominant reactive species and their contributions were identified. The effects of initial pH, persulfate (PS) concentration, Fe-C dosage, and inorganic anions on degradation kinetics were examined, along with the degradation pathway and disinfection byproduct (DBP) formation potential. Results showed that MF/Fe-C/PS achieved 99% degradation of CGA within 60 min under optimal conditions: pH=3, PS concentration 1.5 mmol·L−1, and Fe-C dosage 0.4 g·L−1. Coexisting Cl−, Br−, and I− inhibited CGA oxidation to varying degrees, as did natural organic matter (FA and BAS). The reactive species SO4−·, ·OH, and 1O2 contributed 41.6%, 30.5%, and 27.9%, respectively. Degradation mechanisms included hydrolysis, dehydroxylation, decarboxylation, and benzene ring cleavage. Pre-oxidation by MF/Fe-C/PS significantly reduced the DBP formation potential during subsequent chlorination/chloramination. Energy per order (EE/O) analysis indicated favorable economic efficiency.
Journal of Fuel Chemistry and Technology•2026•DOI: 10.1016/S1872-5813(26)60655-X
Defect-induced nonradiative recombination critically restricts the power conversion efficiency (PCE) and stability of perovskite solar cells (PSCs). Lewis base additives show great promise in defect passivation, but current screening methods rely heavily on empirical trial and error and lack clear design principles, making it difficult to efficiently discover high-performance candidate materials. Here, we present a machine learning (ML) framework to intelligently screen Lewis base molecules for defect passivation. We trained six ensemble models on a dataset of 146 experimental data points, with Light Gradient Boosting Machine (LightGBM) yielding the best classification performance (87% accuracy). Shapley Additive Explanations (SHAP) interpretability analysis subsequently identifies the highest occupied molecular orbital (HOMO) energy (−7.5 to −6.3 eV), additive concentration (2.5 to 6.5 mg/mL), and simplified molecular backbones (O atom ≤ 2, C atom < 5) as critical design criteria. The ML prediction was experimentally validated: (S)-pyrrolidine-3-carboxylic acid ((S)-PCA) and 2-methyl-1,3-cyclopentanedione (MCPD) (Class Ⅱ) improved PCE by 2.22% and 2.01%, respectively, while 3-hydroxymethyl-3-methylbutanenitrile (3-HMBN) (Class Ⅰ) showed minimal gain. Density functional theory (DFT) calculations further confirmed the stronger binding affinities and elevated defect formation energies of Class Ⅱ additives. Notably, the champion (S)-PCA device achieved a PCE of 24.05%. This work established an ML-accelerated paradigm for the rational design of defect passivators, bridging data science and photovoltaics.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-026-4186-7
The development of ultrahigh-temperature technologies, such as nuclear reactors, rocket nozzles, scramjet propulsion systems, and hypersonic vehicles, demands materials with melting points (Tm) exceeding 3273 K. However, the highest reported Tm among non-radioactive oxides is 3125 K (MgO), limiting progress. Existing predictive models for oxide Tm suffer from a trade-off between physical insight and practical utility: thermodynamic approaches require complex calculations, Lindemann's criterion relies on elusive parameters like Debye temperature, and semi-empirical correlations lack transferability. Machine learning models offer predictive power but lack interpretability and reliable extrapolation. Here, we simplify a previously derived semi-empirical model based on bond-breaking probability, which links Tm to effective potential barrier Ueff, proportional to bond length (d), bond density (Nb), and bond ionicity (fi). By analyzing a dataset of 68 oxides, we establish a simplified linear relationship between Tm and Ueff, expressed as Tm = 0.052Ueff + 122.6 K, with Ueff in kJ/mol. This model achieves a mean absolute error of 76 K and a coefficient of determination (R²) of 0.97, outperforming existing empirical models. The model's physical transparency and simplicity enable rapid screening of novel oxides, guiding the design of materials with Tm exceeding 3273 K. Our findings provide a practical tool for accelerating the discovery of ultrahigh-temperature oxides, addressing a critical bottleneck in next-generation thermal protection systems.