Robotic Nanoparticle Synthesis via Solution-based Processes

We present a screw geometry-based manipulation planning framework for the robotic automation of solution-based synthesis, exemplified through the preparation of gold and magnetite nanoparticles. The synthesis protocols are inherently long-horizon, multi-step tasks, requiring skills such as pick-and-place, pouring, turning a knob, and periodic visual inspection to detect reaction completion. A central challenge is that some skills, notably pouring, transferring containers with solutions, and turning a knob, impose geometric and kinematic constraints on the end-effector motion. To address this, we use a programming by demonstration paradigm where the constraints can be extracted from a single demonstration. This combination of screw-based motion representation and demonstration-driven specification enables domain experts, such as chemists, to readily adapt and reprogram the system for new experimental protocols and laboratory setups without requiring expertise in robotics or motion planning. We extract sequences of constant screws from demonstrations, which compactly encode the motion constraints while remaining coordinate-invariant. This representation enables robust generalization across variations in grasp placement and allows parameterized reuse of a skill learned from a single example. By composing these screw-parameterized primitives according to the synthesis protocol, the robot autonomously generates motion plans that execute the complete experiment over repeated runs. Our results highlight that screw-theoretic planning, combined with programming by demonstration, provides a rigorous and generalizable foundation for long-horizon laboratory automation, thereby enabling fundamental kinematics to have a translational impact on the use of robots in developing scalable solution-based synthesis protocols.

Exploring and Analyzing Wildland Fire Data Via Machine Learning Techniques

This research project investigated the correlation between a 10 Hz time series of thermocouple temperatures and turbulent kinetic energy (TKE) computed from wind speeds collected from a small experimental prescribed burn at the Silas Little Experimental Forest in New Jersey, USA. The primary objective of this project was to explore the potential for using thermocouple temperatures as predictors for estimating the TKE produced by a wildland fire. Machine learning models, including Deep Neural Networks, Random Forest Regressor, Gradient Boosting, and Gaussian Process Regressor, are employed to assess the potential for thermocouple temperature perturbations to predict TKE values. Data visualization and correlation analyses reveal patterns and relationships between thermocouple temperatures and TKE, providing insight into the underlying dynamics. The project achieves high accuracy in predicting TKE by employing various machine learning models despite a weak correlation between the predictors and the target variable. The results demonstrate significant success, particularly from regression models, in accurately estimating the TKE. The research findings contribute to fire behavior and smoke modeling science, emphasizing the importance of incorporating machine learning approaches and identifying complex relationships between fine-scale fire behavior and turbulence. Accurate TKE estimation using thermocouple temperatures allows for the refinement of models that can inform decision-making in fire management strategies, facilitate effective risk mitigation, and optimize fire management efforts. This project highlights the valuable role of machine learning techniques in analyzing wildland fire data, showcasing their potential to advance fire research and management practices.