Abstract: A power generation device may include a radiation source, an emitter, and a collector. The emitter may be formed adjacent to the radiation source. The emitter may include a high-density material. The collector may be adjacent to the radiation source and include a low-density material. The emitter is between the radiation source and the collector. An insulator may be positioned between the emitter and the collector. An emitter of a nuclear battery and a method of forming an emitter of a nuclear battery are also disclosed.

Abstract: The present disclosure describes aspects of a machine-learned (ML) spectrum analysis configured to distinguish between a plurality of radioisotope types and/or a plurality of emission levels of respective radioisotope types within spectrum data. The ML spectrum analyzer may utilize an artificial neural network (ANN) having an output layer configured to produce prediction data for respective labels, each label corresponding to a respective radioisotope. The prediction data may be configured to quantify an amount of each respective radioisotope within a subject of the spectrum.

Abstract: This application relates generally to improving energy resolution of measured energy data. One or more embodiments includes a method including obtaining first energy data representative of amounts of energy measured at a first number of energy levels. The method may also include generating second energy data based on the first energy data. The second energy data may be representative of amounts of energy at a second number of energy levels. The second energy data may exhibit a higher energy resolution than the first energy data. Related devices, systems and methods are also disclosed.