arXiv subjects

Xuyang Feng

Publications and source records attributed to Xuyang Feng.

2 recordsLinked to original sources

arXiv2026

Leveraging rapid sintering to retain metastable zirconia in copper

Cermets combining metastable ceramics and ductile metals promise superior toughness and strength. However, retaining metastability often requires high-temperature sintering that coarsens microstructures and relaxes matrix constraint. Here we introduce an ultrafast high-temperature sintering (UHS) strategy to overcome this trade-off in zirconia-copper cermets. By applying Joule heating at around 100 degrees C per second to 900 degrees C with only a 20 second hold, we obtained cermets containing up to 50 weight percent of metastable austenite in zirconia at room temperature within a fine-grained and homogeneous microstructure. The rapid sintering kinetically favors semi-thermal austenite formation while suppressing copper grain growth and matrix relaxation, thereby stabilizing the high-temperature phase and simultaneously preserving microstructural refinement. This approach offers significant potential for copper-based composites in applications such as transformation toughening, self-healing, and crack detection.

cond-mat.mtrl-sci↗

arXiv2026

Beyond Conditional Computation: Retrieval-Augmented Genomic Foundation Models with Gengram

Current genomic foundation models (GFMs) rely on extensive neural computation to implicitly approximate conserved biological motifs from single-nucleotide inputs. We propose Gengram, a conditional memory module that introduces an explicit and highly efficient lookup primitive for multi-base motifs via a genomic-specific hashing scheme, establishing genomic "syntax". Integrated into the backbone of state-of-the-art GFMs, Gengram achieves substantial gains (up to 14%) across several functional genomics tasks. The module demonstrates robust architectural generalization, while further inspection of Gengram's latent space reveals the emergence of meaningful representations that align closely with fundamental biological knowledge. By establishing structured motif memory as a modeling primitive, Gengram simultaneously boosts empirical performance and mechanistic interpretability, providing a scalable and biology-aligned pathway for the next generation of GFMs. The code is available at https://github.com/zhejianglab/Genos, and the model checkpoint is available at https://huggingface.co/ZhejiangLab/Gengram.

q-bio.GN↗