Ultra-fast single-crystal CVD diamonds in the particle time-of-flight (PTOF) detector for low yield burn-history measurements on the NIF (invited).

The Particle Time of Flight (PTOF) diagnostic is a chemical vapor deposition diamond-based detector and is the only diagnostic for measuring nuclear bang times of low yield (<1013) shots on the National Ignition Facility. Recently, a comprehensive study of detector impulse responses revealed certain detectors with very fast and consistent impulse responses with a rise time of <50 ps, enabling low yield burn history measurements. At the current standoff of 50 cm, this measurement is possible with fast 14 MeV neutrons from deuterium-tritium (DT) fusion plasmas.

Diagnostic development and needs for laser driven MeV x-ray radiography.

Laser-driven MeV x-ray radiography of dynamic, dense objects demands a small, high flux source of energetic x-rays to generate an image with sufficient quality. Understanding the multi-MeV x-ray spectrum underscores the ability to extrapolate from the current laser sources to new future lasers that might deploy this radiography modality. Here, we present a small study of the existing x-ray diagnostics and techniques.

Neutron-induced backgrounds in coaxial cables on the South Pole neutron time-of-flight detector at the National Ignition Facility.

As neutron yields increase at fusion facilities, a universal symptom the community must deal with is MeV neutron-induced backgrounds in cables running to diagnostics. On the first Gain >1 plasmas in the world, the National Ignition Facility (NIF) neutron time-of-flight (nToF) diagnostic registered significant cable backgrounds that compromised key performance measurements. The South Pole nToF is uniquely located inside the NIF Target Bay shield walls, ∼18 m from the fusion source, and consequently has long coaxial cable runs (>20 m) that see significant neutron fluence.

Deriving and validating a risk prediction model for long COVID: a population-based, retrospective cohort study in Scotland.

Objectives: Using electronic health records, we derived and internally validated a prediction model to estimate risk factors for long COVID and predict individual risk of developing long COVID.

Design: Population-based, retrospective cohort study.

Setting: Scotland.