Anisotropic laser-feedback polarimetry enables highly sensitive, self-calibrating birefringence measurement in low-transmittance materials
Peer-Reviewed Publication
Updates every hour. Last Updated: 5-Apr-2026 12:15 ET (5-Apr-2026 16:15 GMT/UTC)
Accurate measurements of dual parameters of phase retardance and retardance axis of birefringent materials are of fundamental importance to their fabrication and applications. However, current techniques typically exhibit limited versatility, suffering from high complexity, insufficient accuracy, and low efficiency. In this study, an anisotropic laser feedback polarization effect is proposed and demonstrated for birefringence measurement, featuring simultaneous dual-parameter demodulation, unified polarization modulation-analysis architecture, high detection sensitivity, user-friendly operation, and versatile functionality. Importantly, such system can be self-calibrated with its own physical phenomena to reduce the installation derivation. To showcase the powerful effectiveness, we perform the static birefringence, dynamic birefringence variation, and spatial birefringence distribution, which remarkably exhibits the standard deviation of 0.0453° and 0.0939° for phase retardance and retardance axis azimuth, with the limit allowable sample transmittance around 10-5. This work demonstrates comprehensive applicability across diverse birefringence scenarios, extending the application of anisotropic laser feedback polarization effect, while establishing a novel strategy for birefringence measurement.
An increase in nitrogen dioxide (NO₂) levels in the air is associated with a 7% rise in the risk of cardiac arrest over four days. Particulate matter (PM₂.₅ and PM₁₀) elevates the risk on the same day as the peak.
Generative AI (GenAI) is rapidly transforming higher education. This study explores the imperative for curriculum reform to effectively integrate these powerful tools of GenAI into education and prepare students for an AI-driven world.
This study examined the evolution of digital education policy in the United Kingdom (UK) from 2008 to 2024 based on a discourse analysis of 21 policy documents retrieved from the UK government’s official website, GOV.UK.
The work introduces a large-language-model framework that predicts a student’s future performance and generates natural-language diagnoses from only a handful of activity records, thereby eliminating the data hunger and opacity that have long limited knowledge-tracing systems.