The LA Fires Spewed Out Toxic Nanoparticles. Nicholas Spada is one of the only scientists in the world using a nuclear x-ray process to study deadly nanoparticles in wildfire smoke. What he's uncovered in California is a nightmare. Nicholas Spada was used to fielding urgent requests when wildfire smoke blanketed cities. Winter was supposed to be the quiet period when wildfires die down and researchers like Spada perform instrument maintenance, write grant proposals and go home for dinner. Instead, 2025's so-called offseason ignited January 7, when the Santa Ana winds came howling through Los Angeles, bringing gusts upwards of 100 miles per hour, after more than eight months without meaningful rainfall. By nightfall, thousands of homes in Los Angeles' swanky Pacific Palisades neighborhood and the Altadena community north of the city were gone. The next morning, Spada was fielding call after call at the University of California, Davis, from fellow air researchers at universities across the country who were packing instruments and other gear and heading for Los Angeles, many on their own dime. They would be studying urban fires--not normal wildfires or even urban-wildland interface fires--but urban fires in which most of the fuel was manmade: lawn chemicals, asbestos insulation, lead paint, lithium batteries. They asked Spada which instruments to bring, what measurements to take, where to set up downwind and when he would be there. The calls quickly morphed into a WhatsApp group that's still going strong, as results continue to roll in sporadically all these months later. Spada, a trim, energetic man with a close-trimmed beard and reddish hair, is a project scientist at UC Davis' Air Quality Research Center. He is one of only a handful of scientists in the world proficient at using a nuclear method for detecting toxic substances in air particles to understand their impact on human health and the environment.

Soft pneumatic actuators (SPAs) produce motions for soft robots with simple pressure input, however they require to be appropriately designed to fit the target application. Available design methods employ kinematic models and optimization to estimate the actuator response and the optimal design parameters, to achieve a target actuator's shape. Within SPAs, Bellow-SPAs excel in rapid prototyping and large deformation, yet their kinematic models often lack accuracy due to the geometry complexity and the material nonlinearity. Furthermore, existing shape-matching algorithms are not providing an end-to-end solution from the desired shape to the actuator. In addition, despite the availability of computational design pipelines, an accessible and user-friendly toolbox for direct application remains elusive. This paper addresses these challenges, offering an end-to-end shape-matching design framework for bellow-SPAs to streamline the design process, and the open-source toolbox SPADA (Soft Pneumatic Actuator Design frAmework) implementing the framework with a GUI for easy access. It provides a kinematic model grounded on a modular design to improve accuracy, Finite Element Method (FEM) simulations, and piecewise constant curvature (PCC) approximation. An Artificial Neural Network-trained surrogate model, based on FEM simulation data, is trained for fast computation in optimization. A shape-matching algorithm, merging 3D PCC segmentation and a surrogate model-based genetic algorithm, identifies optimal actuator design parameters for desired shapes. The toolbox, implementing the proposed design framework, has proven its end-to-end capability in designing actuators to precisely match 2D shapes with root-mean-square errors of 4.16, 2.70, and 2.51mm, and demonstrating its potential by designing a 3D deformable actuator.