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| Section | What the authors did | Main take‑away | |---------|----------------------|----------------| | | Reviewed the need for rapid feedback in SAXS, especially for non‑equilibrium studies. | Real‑time visualization can dramatically improve experimental efficiency. | | System Architecture | Built a three‑layer stack: (1) detector acquisition (Pilatus 2M), (2) GPU‑accelerated pre‑processing (background subtraction, azimuthal integration), (3) WebSocket‑based video streaming to a browser client. | The GPU step reduces processing time from ~2 s/frame to <100 ms/frame. | | Update (UPD) Module | Implements a configurable “update” interval (0.1–2 s) that pushes integrated 1‑D scattering curves and derived parameters (radius of gyration, Porod exponent) to a dashboard. | Users can set custom alerts (e.g., “stop scan if Rg changes > 5 %).” | | Software Implementation | Written in Python 3.11, using Numba for JIT kernels and Dash/Plotly for the UI. | The codebase is modular and can be adapted to other detector types (Eiger, Dectris). | | Experimental Validation | Demonstrated the platform on three samples: (i) PS‑b‑PMMA block‑copolymer annealing, (ii) lysozyme aggregation, (iii) Au‑nanorod growth. | In each case, the live video revealed transient intermediate states that were later confirmed by ex‑post analysis. | | Performance Benchmarks | Measured end‑to‑end latency (detector → browser) of 120 ms at 100 Hz frame rate. | The system comfortably handles the full frame rate of modern fast detectors. | | Conclusions & Outlook | The video‑UPD approach is ready for routine deployment; future work includes AI‑based anomaly detection and multi‑modal integration (SAXS + WAXS). | Provides a pathway toward fully autonomous SAXS experiments. |