Journal of the American Ceramic Society最新文献

Microstructure homogeneity and grain size reduction are needed to ensure the current uniformity and reliability required in metal oxide varistors for DC circuit breakers. Cold sintering (CS)–a low-temperature digestive liquid and high-pressure densification process–has previously shown reduced grain growth, but it is not clear how the lower processing temperatures will impact the bismuth intergranular phases that induce current nonlinearity. This study investigates the impact of CS on the microstructure and nonlinearity of bismuth added to ZnO varistors. ZnO powders with 0–1 mol% Bi₂O₃ are either CS at 300°C and 300 MPa or high temperature sintered (HTS) at 1100°C. CS samples reach >97% of the theoretical density. The 1 mol% Bi₂O₃ CS samples have limited grain growth with a final average grain size of 120 ± 85 nm, while the HTS sample shows over 1.991% increase to a grain size of 2.99 ± 1.17 µm. No Bi-rich intergranular phase is observed in the CS samples, but bismuth clearly increases resistivity with increasing bismuth concentration, with a higher voltage onset for nonlinearity and an increase in nonlinearity with increasing bismuth. This work demonstrates the ability to use cold sintering to create dense ZnO ceramics with reduced grain growth and controlled nonlinearity.

Heterostructured materials hold great potential for achieving superior mechanical properties. However, the majority of current studies on heterostructured ceramics have predominantly focused on layered systems. Here, we report a new 3YSZ-(ZrO₂-SiO₂) dual-heterostructured ceramic composites (DHCCs) comprising 3 mol% yttria-stabilized zirconia (3YSZ) domains and ZrO₂-SiO₂ domains. The DHCCs exhibited a dual-level hierarchical structure. Specifically, the 3YSZ domain consisted of submicron ZrO₂ grains, while the ZrO₂-SiO₂ domain was composed of ZrO₂ nanograins dispersed within an amorphous SiO₂ matrix. To fabricate the DHCCs, hybrid powders were first synthesized by a chemical co-precipitation method, followed by sintering to produce dense DHCCs. The phase composition, microstructure, and mechanical properties of the DHCCs were characterized. Compared with the homogeneously structured ZrO₂-SiO₂ ceramic, the DHCCs exhibited an 85.6% increase in strength and a 47.2% improvement in fracture toughness. Finally, the underlying strengthening and toughening mechanisms of the DHCCs were elucidated. These findings demonstrate that architecting dual-heterogeneous structures represents an effective strategy for achieving robust mechanical properties.

As a third-generation semiconductor material with a wide bandgap, SiC possesses characteristics such as high-temperature resistance, corrosion resistance, and high-frequency capability. Notably, its mechanical hardness is second only to that of diamond. In this study, we focused on analyzing the mechanical responses in nano-scale of the 4H-SiC single crystals on different crystallographic planes of (0001), (2$��\overline{1}�\overline{1}�$0) and (0$��1�\overline{1}�$0), specifically examining the anisotropic nano-scaled hardness, reduced modulus and dislocation first pop-in events. These findings can serve as a reference for semiconductor device packaging processes. It is found that there could be anisotropy effects for modulus, hardness, pop-in load and stress, and dislocation nucleation activation volume. It is found that the activation volume for the onset of plasticity in SiC is remarkably small, on the atomic scale. With increasing elastic modulus, the activation volume of the dislocation first pop-in (or the first dislocation nucleation) becomes smaller.

Nanoindentation testing conducted at low and high strain rates holds great promise as a technique for measuring strain rate sensitivity and exploring the plastic deformation mechanisms of ceramic materials that cannot be studied in standard tension tests because of their inherent brittleness. In this study, we employ a high strain rate nanoindentation testing system, combined with transmission electron microscopy, to investigate the intrinsic mechanisms of plastic deformation in two ceramic materials. The investigations encompass a wide range of indentation strain rates, spanning from a low of 10⁻² s⁻¹ to a high of 10⁴ s⁻¹, using single crystal MgO (111) and Al₂O₃ (0001) as model materials. The results reveal that MgO exhibits a clear dependence of hardness on strain rate, while for sapphire, the influence of strain rate on hardness is negligible. The mechanisms underlying the distinct strain-rate responses of the two materials were revealed through microstructural investigations beneath the indent. Overall, the results demonstrate the promise that nanoindentation testing techniques have for characterizing the plastic deformation of ceramics over a wide range of strain rates.

The strength of Portland cement is primarily due to the products of its hydration: Calcium silicate hydrates (C-S-H). The poorly ordered nature of these phases at the atomic scale is a limiting factor for the applicability of quantum-mechanical simulations relying on periodic boundary conditions (PBC). This is because large supercells are usually needed within PBC to capture the structural complexity of amorphous materials, with associated high, when not prohibitive, computational costs. Here, we devise a strategy to design small and symmetric PBC-compliant structural models of C-S-H with different Ca/Si ratios, allowing efficient quantum-mechanical atomistic simulations via the density functional theory (DFT). The mechanical and dynamical stability of the proposed models is assessed from DFT calculations. The effectiveness of the symmetric models in mimicking the actual disordered C-S-H phase is validated against experimental Raman spectra and bulk moduli.

Designing effective thermal/environmental barrier coatings (T/EBCs) relies on multifunctional optimization through incorporating multiple rare earth (RE) elements into β- and γ-RE₂Si₂O₇. However, the vast compositional space poses significant challenges for the effective selection of multi-component RE disilicates [(nRE_xi)₂Si₂O₇]. This study develops an autoencoder-based multi-task domain adaptation model to predict the mechanical and thermal properties of (nRE_xi)₂Si₂O₇. By leveraging self-supervised learning on unlabeled data, the model reduces feature distribution discrepancies between source and target domains compared to previous models, resulting in high prediction accuracy and generalizability for both equimolar and non-equimolar compositions without the need for labels. Model visualization identifies lattice constant c and average ionic radius of RE ($�\overline{r}$) as key factors. (nRE_xi)₂Si₂O₇ with smaller values of c and $�\overline{r}$ shows reduced apparent bulk coefficient of thermal expansion, lower elastic constants, and increased minimum thermal conductivity. Guided by predictions, novel (Er_1/4Y_1/4Lu_1/4Yb_1/4)₂Si₂O₇ and (Er_1/6Tm_1/6Y_1/15Gd_1/15Lu_4/15Yb_4/15)₂Si₂O₇ are uncovered as high potential for T/EBC applications, whose superior elastic properties, enhanced damage tolerance, low thermal conductivity/residual stresses, and ceramic matrix composite-matched thermal expansion are demonstrated by experiments with a small error (<10%) versus predictions. This deep learning framework integrates physical mechanisms into material design, accelerating the development of high-performance (nRE_xi)₂Si₂O₇ and providing valuable guidance for understanding the properties and mechanisms of T/EBC materials.

This study systematically investigates the influence of sintering aid particle size and powder characteristics on the phase composition, microstructure, and properties of Si₃N₄ ceramics. Results demonstrate that sintering aid size critically governs liquid-phase homogeneity, α → β phase transformation, and grain growth. Smaller aids facilitate more uniform liquid distribution, enhancing densification while suppressing abnormal grain growth. In contrast, larger additives induce localized Y-rich regions or Mg-rich regions, accelerating β-Si₃N₄ nucleation and grain coarsening. Powder properties, especially oxygen content and particle morphology, further dictate liquid-phase formation and phase transformation. High-oxygen powders increase weight loss via the formation of volatile SiO (g) and restrict grain growth via steric hindrance, whereas rod-like β-phase seeds in the raw material trigger abnormal growth, forming a bimodal microstructure. Using raw powders with low oxygen content and rod-shaped grains, along with nanoscale MgO (20 nm) and micrometer-sized Y₂O₃ (2.5 µm) as sintering aids, the SN-C6 sample achieved a bimodal microstructure with elongated β-Si₃N₄ grains, yielding superior performance with a thermal conductivity of 125.5 W·m^‒1·K^‒1, flexural strength of 685 ± 31 MPa, and fracture toughness of 10.1 ± 0.4 MPa·m^1/2 after sintering at 1910°C for 12 h, followed by a 1600°C heat treatment for 8 h.

High thermal stability and a wide temperature range are critical for thermistors used in extreme environments. Y_2/3Cu₃Ti₄O₁₂ (YCTO) ceramics, known for their high room-temperature resistance and structural stability, are promising candidates for such applications. However, inherent defects and lattice distortions limit its performance at high temperatures. Herein, to address this significant challenge, we propose a sol–gel derived strategy for inducing cubic growth of Y_2/3Cu₃Ti₄O₁₂-CB (YCTO-CB) grains using coconut water and bone glue. The growth of {100} crystal facets has a positive effect on the inhibition of lattice deformation and dislocation, which plays a vital role in improving the oxidation resistance and suppressing the resistance attenuation. The YCTO-CB ceramics exhibit superior thermosensitive characteristics in broad temperature range of 25°C–600°C, and have a significantly lower aging coefficient (0.27%) compared to Y_2/3Cu₃Ti₄O₁₂-B (YCTO-B ceramics prepared by adding bone glue only) ceramics (8.1%). The study also reveals a unique “coffee-ring” growth mechanism, where sintering-induced dislocation repair and atomic rearrangement lead to the formation of low-energy {100} facets. These findings highlight the potential of crystal facet engineering and bio-additives in optimizing thermosensitive materials for high-temperature applications, offering valuable insights for advancing thermistor technology in industrial electronics.

Glass development is driven by scientific insights into their material genes as well as engineering practices in response to industry needs. Incubating domain knowledge into well-formatted datasets and databases, and making them open and visible can accelerate the process, facilitating automated material discovery, and design in the data-driven era. In this work, we implement a machine-learning framework to collect information from published research articles. The enriched product, SciGlass+, transcends the publicly available glass database SciGlass by including both scientific data and research metadata. With these enriched contents, research patterns of glasses are analyzed to identify milestone events in technological development and evaluate their sustainability impact. The integration of experimental data from literature and those from computer simulations provides refined insights into the composition–property relationship, thereby supporting a theory-driven and artificial intelligence-assisted approach to glass development. The practical applications of SciGlass+ are demonstrated in screening and designing glasses for electronic packaging and low-carbon emission materials.

We developed a machine-learning interatomic potential (MLIP) based on Moment Tensor Potentials for atomistic simulations in the Si-C-N-H system. The MLIP was trained on ordered and disordered configurations—including crystalline phases, polymers, amorphous models, and high-temperature ab initio molecular dynamics trajectories of chemical reactions—and achieves Density–Functional–Theory-level accuracy for energies, forces, and stresses. We apply the MLIP to complex processes that were previously inaccessible at scale: self-diffusion in Si₃N₄, negative thermal expansion in Si(NCN)₂, fracture in SiC/Si₃N₄ composites, and the polymer-to-ceramic transformation of polysilazanes. Large-scale simulations over nanoseconds capture structural evolution during pyrolysis and reproduce experimental observations, such as the formation of nanometer-sized graphitic segregations in SiCN ceramics. Our results demonstrate that the MLIP extends quantum-level accuracy to technologically relevant problems, providing new opportunities for predictive modeling of ceramic materials and their transformations.