Hardware

High-flux neutron beams and high-efficiency detectors enable rapid neutron diffraction measurements at the Engineering Materials Diffractometer (VULCAN) at the Spallation Neutron Source (SNS), Oak Ridge National Laboratory (ORNL). To optimize beam time utilization, efficient sample exchange, alignment, and automated measurements are essential. Recent advances in artificial intelligence (AI) have expanded the capabilities of robotic systems. Here, we report the development of a Robotic Interactive Control and Handling (RICH) system for sample handling at VULCAN, designed to support high-throughput experiments and reduce overhead time. The RICH system employs a six-axis desktop robot integrated with AI-based computer vision models capable of recognizing and localizing samples in real time from instrument and depth-resolving cameras. Vision algorithms combine these detections to align samples with designated measurement positions or place them within complex sample environments such as furnaces. This integration of machine learning-assisted vision with robotic handling demonstrates the feasibility of autonomous sample detection and preparation, offering a pathway toward fully unmanned neutron scattering experiments.

Field instruments are an emerging topic because they facilitate the extraction of qualitative and quantitative information from samples at a sampling site. This work focused on the development of an open-source potentiostat, analyzing the construction feasibility and quality of a lab-made device with an all-in-one design, low cost, good response, and portability, compared to expensive, commercial, laboratory-oriented devices. The design and development of the hardware, as well as the corresponding software, was considered, allowing the device to be expandable in the future through upgrades. Thus, an economical and portable functional prototype was developed, with good linear response and the capacity to perform, in this first approach, cyclic, square wave, and stripping voltammetry with a range and sweep speed between ±2.048 V and 1000 mV/s, respectively, with waveform frequencies up to 110 Hz and an accuracy of ±1 nA.

The S11D mining complex in Brazil, situated in Pará state, extracts 20 million tons of iron each quarter. Connecting via a standard 802.11b/g/n wireless network is crucial for mine operations across vast distances. A local team employs a network monitoring tool called the Ekahau Site Survey to guarantee the proper functioning of the network. However, due to the harsh terrain and the dangerous nature of S11D operations, this tool fails to gather data from all points of interest, resulting in interpolated maps that may not accurately represent the network’s overall quality. In this work, we propose a platform that can be attached to mobile machines during operations to automatically collect network parameters, such as channelization, RSSI, latency, packet loss, and bandwidth, without requiring human intervention. Using these network data, we generate an RSSI map using Kriging, which the local team can use. Comparison tests conducted in the laboratory and the field demonstrate that the platform performs similarly to Ekahau in capturing network parameters, ensuring its use in day-to-day operations for mapping.