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◆ Journal of Materials Research and Technology2026-04-11· Materials science

One-step reactive sintering of La2Ti2O7 from La2O3–TiO2: Spark plasma sintering vs pressureless consolidation and interpretable machine-learning density prediction

Bagas Haqi Arrosyid, Faried Latief, Nicky Chen, Hendri Erka Setya, Muhammad Tsabit Ayman, Ratih Amalia, Muhammad Fahroji, Nanda Hendra Pratama, Didik Aryanto, Muhammad Iqbal, Alfian Noviyanto

原始摘要(英文原文)· Original abstract
La 2 Ti 2 O 7 (LTO) is a layered perovskite significant for high-temperature dielectric and ferroelectric applications. Traditional methods for achieving dense ceramics involve a two-step process, but we present a one-step reactive sintering approach using mixed La 2 O 3 –TiO 2 precursors to directly form dense, single-phase LTO. We compare spark plasma sintering (SPS) and pressureless sintering (PS) at 1000–1200 °C. X-ray diffraction analysis confirms monoclinic LTO formation at high temperatures, while low-temperature PS shows limited consolidation. SPS achieves 92.47% relative density at 1200 °C in 15 min, whereas PS reaches 86.93% after 120 min with more porosity. To identify the dominant densification factors, structural and microstructural descriptors derived from X-ray diffraction and scanning electron microscope were integrated into regression models. Among the evaluated models, partial least squares regression provided the best predictive performance. Interpretable machine-learning analysis revealed that density is governed primarily by pore-network descriptors, specifically Porosity × STD, Phase Count, Porosity (%), and STD/Size, rather than by nominal processing variables alone, whereas nominal processing variables primarily influence microstructural evolution. These results demonstrate that calcination-free reactive SPS is an efficient route for producing dense LTO and establish a descriptor-based framework for understanding and optimizing reactive ceramic sintering.
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One-step reactive sintering of La2Ti2O7 from La2O3–TiO2: Spark plasma sintering vs pressureless consolidation and interpretable machine-learning density prediction — 科研速览 Science Skim