The goal of the Chemical Dynamics Initiative (CDI) is to develop the scientific foundation necessary to understand and predict the temporal evolution of complex chemical systems in real-world or operational environments. This goal requires understanding fundamental chemical processes that drive change (i.e., dynamics) over a chemical system’s lifespan and transcends single domains or scientific disciplines. The CDI research teams are developing integrated capabilities with expertise in applied mathematics, theoretical chemistry, and measurement science leading to a common language across the initiative for addressing the study of chemical dynamics in complex systems.
- Clean Microfabrication Laboratory - The clean microfabrication laboratory creates a critical bridge between PNNL’s advanced material development and quantum applications, speeding work toward better understanding of materials and more efficient device manufacturing.
- Memristor Testbed - This testing infrastructure emulates the operation conditions of memristor devices in real-life applications and facilitates the fast characterization of electrical properties of novel functional materials as they are developed.
- Micro-CT Imaging - The increased spatial resolution of this instrument rounds out Pacific Northwest National Laboratory's (PNNL) suite of characterization capabilities and fulfills the need to visualize fine structures and enable research at an intermediate scale.
- miniSPLAT - This one-of-a-kind single particle mass spectrometer is used to characterize in situ and real-time physicochemical properties of individual particles, with unparalleled sensitivity, sizing precision, mass-spectral quality, and temporal resolution.
Dilution Refrigerator - An essential tool to better leverage PNNL’s capabilities toward answering fundamental science questions in the ultra-low-temperature regime, the dilution refrigerator can cool samples from room temperature to 0.01 degrees on the kelvin scale, just a fraction of a degree above absolute zero.
Few-Shot Machine Learning - Using just a few examples, an ML algorithm can quickly detect and quantify features of interest, such as defects, particles, or interfaces. This approach allows researchers to easily triage and statistically analyze microscopy data in a way that has not been possible before.
Sample Exposure Chamber - The custom-built exposure cell provides an ability to expose samples to a controlled concentrations of corrosive gasses with gravimetric analysis.
Radiotracer Atmospheric Dynamics Chamber - This unique environmental chamber enables researchers to investigate interconversions of radiolabeled molecules in the gas phase.
Real-Space and Real-Time Phononics - A new combined atomic force microscopy – optical spectroscopy capability characterizes ultralow frequency vibrations, or phonons, in low-dimensional and quantum materials.