arXiv subjects

Peiren Wang

Publications and source records attributed to Peiren Wang.

2 recordsLinked to original sources

arXiv2026

Strong lead-free bioinspired piezoceramics for durable energy transducers

Durable, high-performance and eco-friendly lead-free piezoceramics are essential for next-generation sustainable energy transducers and electromechanical systems. While significant performance enhancements have been made, through chemical composition, texture, or crystal defects, piezoceramics are intrinsically weak mechanically, which negatively impact their working conditions and durability. What's more, improving comprehensive mechanical durability without sacrificing piezoelectric performance remains a key challenge. Here, we design bioinspired Bi0.5Na0.5TiO3 (BNT) ceramics using a scalable colloidal process that enables multiscale control over the microstructure. The design comprises plate-like monocrystalline BNT bricks stacked to induce a crystallographic texture along the poling direction, bonded together by a silica-based mortar, forming the brick-and-mortar phase. This deliberate microstructure design yields 2- to 3-fold increase in flexural strength, and 1.6- to 2-fold increase in fracture toughness compared with a BNT synthesized conventionally, comparable to common structural ceramics, without sacrificing the piezoelectric performance. In addition, the bioinspired BNT exhibit dramatically enhanced ferroelectric fatigue resistance, with a 10- to 15-folds improvement in the number of field-induced electromechanical cycles before failure. These gains originate from anisotropic residual stress fields, revealed by Raman spectroscopy and XRD, which delay crack initiation events. Furthermore, we demonstrated enhanced transducing capability and electromechanical fatigue resistance using a cantilever beam-based piezoelectric transducer under bending mode. Given its non-chemical-compositional origin, this bioinspired strategy could be broadly applicable to other piezoelectric material systems for applications where both functional and structural performance are critical.

cond-mat.mtrl-sci↗

arXiv2025

Seizure-NGCLNet: Representation Learning of SEEG Spatial Pathological Patterns for Epileptic Seizure Detection via Node-Graph Dual Contrastive Learning

Complex spatial connectivity patterns, such as interictal suppression and ictal propagation, complicate accurate drug-resistant epilepsy (DRE) seizure detection using stereotactic electroencephalography (SEEG) and traditional machine learning methods. Two critical challenges remain:(1)a low signal-to-noise ratio in functional connectivity estimates, making it difficult to learn seizure-related interactions; and (2)expert labels for spatial pathological connectivity patterns are difficult to obtain, meanwhile lacking the patterns' representation to improve seizure detection. To address these issues, we propose a novel node-graph dual contrastive learning framework, Seizure-NGCLNet, to learn SEEG interictal suppression and ictal propagation patterns for detecting DRE seizures with high precision. First, an adaptive graph augmentation strategy guided by centrality metrics is developed to generate seizure-related brain networks. Second, a dual-contrastive learning approach is integrated, combining global graph-level contrast with local node-graph contrast, to encode both spatial structural and semantic epileptogenic features. Third, the pretrained embeddings are fine-tuned via a top-k localized graph attention network to perform the final classification. Extensive experiments on a large-scale public SEEG dataset from 33 DRE patients demonstrate that Seizure-NGCLNet achieves state-of-the-art performance, with an average accuracy of 95.93%, sensitivity of 96.25%, and specificity of 94.12%. Visualizations confirm that the learned embeddings clearly separate ictal from interictal states, reflecting suppression and propagation patterns that correspond to the clinical mechanisms. These results highlight Seizure-NGCLNet's ability to learn interpretable spatial pathological patterns, enhancing both seizure detection and seizure onset zone localization.

eess.SP↗