Y. Zhao

Y. Zhao, G. Meng

Dielectric elastomer actuators (DEAs) have garnered extensive attention especially in soft robotic applications over the past few decades owing to the advantages of lightweight, large strain, fast response and high energy density. However, because the DEAs suffer from nonlinear elasticity, inherent viscoelastic creep, hysteresis and vibrational dynamics, the modeling, control and self-sensing of DEAs are challenging, thereby hindering the practical applications of DEAs. In order to address these challenges, numerous studies have been conducted. In this review, various physics-based modeling methods and phenomenological modeling methods for predicting the electromechanical response of DEAs are presented and discussed. Different control methods for DEAs are reviewed, which are classified into open-loop feedforward control, feedback control, feedforward-feedback control and adaptive feedforward control. Physics-based self-sensing methods and data-driven self-sensing methods for reconstructing the DEA displacement without the need for additional sensors are discussed. Finally, the existing problems and new opportunities for the further studies are summarized.

S. Luza-Vega, Y. Zhao, E. Martínez-Pañeda

Cathode particle fracture is widely recognised as a major degradation mechanism in lithium-ion batteries, yet cracking also permits electrolyte wetting of newly exposed internal surfaces, modifying interfacial reaction pathways. The mechanistic role of electrolyte wetting in redistributing reactions within cracked particles remains unclear. Here, we isolate this effect through a controlled comparison between (i) a fully coupled electro-chemo-mechanical model resolving lithium concentration, electrostatic potential, and stress fields in both the active material and the electrolyte inside and outside cracks, and (ii) a single-particle chemo-mechanical model employing the conventional uniform flux assumption. The coupled model predicts strong spatial heterogeneity in interfacial reaction rates, with flux amplification approximately 8x relative to the imposed uniform flux at the crack tip. Reaction redistribution, and thus lithium flux, is governed predominantly by local solid-state lithium concentration and stress variations, while electrolyte potential gradients inside cracks remain secondary under the conditions considered. Uniform flux models can underpredict delivered capacity by 25% at 1C-rate; this discrepancy increases at higher rates. They also underestimate tensile stresses throughout the delithiation process by 10%, directly affecting crack driving conditions. These results demonstrate that neglecting crack-electrolyte coupling leads to systematic underestimation of both utilisation limits and fatigue-relevant stress histories.

S. Chapagain, Y. Zhao, T. K. Rohleen et al.

Individuals who identify as sexual and gender minorities, including lesbian, gay, bisexual, transgender, queer, and others (LGBTQ+) are more likely to experience poorer health than their heterosexual and cisgender counterparts. One primary source that drives these health disparities is minority stress (i.e., chronic and social stressors unique to LGBTQ+ communities' experiences adapting to the dominant culture). This stress is frequently expressed in LGBTQ+ users' posts on social media platforms. However, these expressions are not just straightforward manifestations of minority stress. They involve linguistic complexity (e.g., idiom or lexical diversity), rendering them challenging for many traditional natural language processing methods to detect. In this work, we designed a hybrid model using Graph Neural Networks (GNN) and Bidirectional Encoder Representations from Transformers (BERT), a pre-trained deep language model to improve the classification performance of minority stress detection. We experimented with our model on a benchmark social media dataset for minority stress detection (LGBTQ+ MiSSoM+). The dataset is comprised of 5,789 human-annotated Reddit posts from LGBTQ+ subreddits. Our approach enables the extraction of hidden linguistic nuances through pretraining on a vast amount of raw data, while also engaging in transductive learning to jointly develop representations for both labeled training data and unlabeled test data. The RoBERTa-GCN model achieved an accuracy of 0.86 and an F1 score of 0.86, surpassing the performance of other baseline models in predicting LGBTQ+ minority stress. Improved prediction of minority stress expressions on social media could lead to digital health interventions to improve the wellbeing of LGBTQ+ people-a community with high rates of stress-sensitive health problems.