Search for: All records

Coherent multidimensional spectroscopy provides experimental access to molecular structure and subpicosecond dynamics in solution. Dynamics are typically inferred from the evolution of lineshapes over a function of waiting time. Numerous spectral analysis methods, such as center/nodal line slope, have been developed to extract these dynamics. However, the extracted dynamics can depend heavily on subjective choices, such as the region selected for CLS analysis or the chosen models. In this study, we introduce a novel approach to extracting dynamics from ultrafast two-dimensional infrared (2D IR) spectra by using dynamic mode decomposition (DMD). As a data-driven method, DMD directly extracts spatiotemporal structures from the complex 2D IR spectra. We evaluated the performance of DMD in simulated and experimental spectra containing overlapped peaks. We show that DMD can retrieve the dynamics of overlapped transitions and cross peaks that are typically challenging to extract with traditional methods. In addition, we demonstrate that combining conditional generative adversarial neural networks with DMD can recover dynamics even at low signal-to-noise ratios. DMD methods do not require preliminary assumptions and can be readily extended to other multidimensional spectroscopies. 

BoxCARS and pump-probe geometries are common implementations of two-dimensional infrared (2D IR) spectroscopy. BoxCARS is background-free, generally offering greater signal-to-noise ratio, which enables measuring weak vibrational echo signals. Pulse shapers have been implemented in the pump-probe geometry to accelerate data collection and suppress scatter and other unwanted signals by precise control of the pump-pulse delay and carrier phase. Here, we introduce a 2D-IR optical setup in the BoxCARS geometry that implements a pulse shaper for rapid acquisition of background-free 2D IR spectra. We show a signal-to-noise improvement using this new fast-scan BoxCARS setup versus the pump-probe geometry within the same configuration.