Repository for Publications and Research Data
News from the ETH Library
Recently Added
Unfolding of Field-Free Spin–Orbit Torque Magnetization Switching in a van der Waals Heterostructure
Item type: Data Collection
Kercher, Niklas; Noël, Paul; Karadza, Emir; et al. (2026)
Fuel and Fatigue: Metabolic Programming of CD8 T Cells During Chronic Viral Infection
Item type: Doctoral Thesis
Tuchel, Marie-Elen (2026)
Mr. Robot to the Rescue? Exploring How Correct Cyber Security Content in Entertainment Media Influences Cyber Security Beliefs
Item type: Conference Paper
Hahn, Sara; Stöver, Alina; Böhm, Carlos Dean Ramiro; et al. (2026)
Entertainment media offers a unique opportunity to convey cyber security knowledge to a wide audience in an engaging way. This study explores its impact on shaping cyber security beliefs through two rounds of interviews with 39 participants, using clips from the series Mr. Robot, known for its realistic depiction of cyber security. The clips addressed topics such as social engineering, password vulnerabilities, misconceptions about hackers targeting only high-value individuals, and the subtle nature of attacks. Participants who viewed cyber security-focused clips demonstrated increased awareness of social engineering and weak passwords. However, beliefs about hacker targets and attack detectability remained unchanged, likely due to participants’ pre-existing high awareness. Our findings highlight the value of positive role models in media, fostering empathy for victims and mitigating victim-blaming. By integrating accurate cyber security behaviors into entertainment, media can offer a compelling, informal channel for promoting secure practices and addressing widespread misconceptions.
Unfolding
of Field-Free Spin–Orbit Torque Magnetization
Switching in a van der Waals Heterostructure
Item type: Journal Article
Kercher, Niklas; Noël, Paul; Karadza, Emir; et al. (2026)
Abstract
Heterostructures of 2D materials offer high tunability of magnetic properties, including current-induced magnetization switching by unconventional spin–orbit torques. The microscopic mechanisms governing spin-torque switching in van der Waals systems, however, remain insufficiently understood. Here, we investigate current-induced magnetization reversal in an Fe3GeTe2 (FGT) and WTe2 heterostructure using scanning harmonic magneto-optical Kerr effect microscopy. Spatially resolved magnetization maps show that field-free switching unfolds from asymmetric domain nucleation at the edges of the FGT flake, followed by domain wall propagation. This switching behavior is deterministic and bipolar, consistent with spin–orbit torques arising from the injection of both in-plane and out-of-plane spins from WTe2 into FGT. The critical currents for up-to-down and down-to-up switching differ because of asymmetric domain nucleation and directional domain wall pinning effects. Micromagnetic simulations reproduce the switching behavior observed experimentally, showing how the initial domain nucleation sites are determined by the sample geometry and combined influence of in-plane and out-of-plane spins and Dzyaloshinskii-Moriya interaction. Finally, by systematically varying the field-like and damping-like torque components and sweeping the spin polarization between in-plane and out-of-plane, we identify the torque configurations that yield the most efficient switching in van der Waals magnets with different shapes.
A GAN-based framework for generating STFT spectrograms of rare acoustic events in structural health monitoring
Item type: Journal Article
Farhadi, Sasan; Iavarone, Mariateresa; Corrado, Mauro; et al. (2026)
Structural health monitoring plays a crucial role in ensuring the safety, reliability, and longevity of bridge infrastructures through early damage detection. Although recent advances in deep learning-based models have enabled automated event detection, their performance is often limited by data scarcity, environmental noise, and class imbalance. To address these challenges, this study introduces a customized generative adversarial network (GAN) model, STFTSynth, designed particularly for generating short-time Fourier transform spectrograms derived from acoustic-event signals. In contrast to augmentation techniques such as MixUp, GANs can synthesize spectrograms that visually and statistically resemble real event representations, providing a basis for representation-level dataset enrichment. The proposed model integrates dense residual blocks for spatial consistency with bidirectional gated recurrent units for temporal dependency modeling. Model performance is evaluated against three baseline generative models using qualitative inspection and quantitative metrics, including structural similarity index measure, peak signal-to-noise ratio, and Fréchet inception distance. Results show that STFTSynth outperforms baseline models, producing high-resolution, temporally consistent spectrograms that align closely with real-world data. These findings highlight the potential of GAN-based spectrogram synthesis for representation-level enrichment of rare acoustic-event datasets in bridge monitoring, particularly when real examples such as prestressing wire breakage are limited.