The non-radiative relaxation of the excitation energy from higher energy states to the lowest energy state in chlorophylls is a crucial preliminary step for the process of photosynthesis. Despite the continuous theoretical and experimental efforts to clarify the ultrafast dynamics of this process, it still represents the object of an intense investigation because the ultrafast timescale and the congestion of the involved states makes its characterization particularly challenging. Here we exploit 2D electronic spectroscopy and recently developed data analysis tools to provide more detailed insights into the mechanism of internal conversion within the Q-bands of chlorophyll a. The measurements confirmed the timescale of the overall internal conversion rate (170 fs) and captured the presence of a previously unidentified ultrafast (40 fs) intermediate step, involving vibronic levels of the lowest excited state.

Infrared spectroscopyo fl iquid biopsies is at imeand cost-effective approach that may advance biomedical diagnostics.H owever,t he molecular nature of disease-related changes of infrared molecular fingerprints (IMFs) remains poorly understood, impeding the methodsa pplicability.H ere we probe 148 human blood sera and reveal the origin of the variations in their IMFs.Tothat end, we supplemented infrared spectroscopyw ith biochemical fractionation and proteomic profiling,p roviding molecular information about serum composition. Using lung cancer as an example of am edical condition, we demonstrate that the disease-related differences in IMFs are dominated by contributions from twelve highly abundant proteins-that, if used as ap attern, may be instrumental for detecting malignancy.T ying proteomic to spectral information and machine learning advances our understanding of the infrared spectra of liquid biopsies, af ramework that could be applied to probing of any disease.

Infrared spectroscopy of liquid biopsies is a timeand cost-effective approach that may advance biomedical diagnostics. However, the molecular nature of disease-related changes of infrared molecular fingerprints (IMFs) remains poorly understood, impeding the methods applicability. Here we probe 148 human blood sera and reveal the origin of the variations in their IMFs. To that end, we supplemented infrared spectroscopy with biochemical fractionation and proteomic profiling, providing molecular information about serum composition. Using lung cancer as an example of a medical condition, we demonstrate that the disease-related differences in IMFs are dominated by contributions from twelve highly abundant proteins-that, if used as a pattern, may be instrumental for detecting malignancy. Tying proteomic to spectral information and machine learning advances our understanding of the infrared spectra of liquid biopsies, a framework that could be applied to probing of any disease.