The research on heteroaromatic azoswitches has been blossoming in recent years due to their astonishingly broad range of properties. Minimal chemical modifications can drastically change the demeanor of these switches, regarding photophysical and (photo)chemical properties, promoting them as ideal scaffolds for a vast variety of applications based on bistable light-addressable systems. However, most of the characteristics exhibited by heteroaryl azoswitches were found empirically, and only a few works focus on their rationalization. Herein we report on a mechanistic study employing phenylazoindoles as a model reference, combining spectroscopic experiments with comprehensive computational analysis. This approach will elucidate the intrinsic correlations between the molecular structure of the switch and its thermal behavior, allowing a more rational design transferable to various heteroaryl azoswitches.

The development and investigation of heteroazo switches has flourished in recent years. Because of their specific photophysical and photochemical properties, they find versatile applications from material science to medicine. However, a deep mechanistic understanding is needed to be able to predict the properties of such azoswitches. In particular, the effect of different substituents on the azo chromophore is of great interest as they are often crucial for embedding the molecular switch into a system of interest. Herein, we provide a detailed spectroscopic and computational study on the influence of substituents on 3-phenylazoindoles chosen as models. We will point out changes in absorption properties and analyze the photostationary state of the thermally labile Z isomers through computational means to provide a general structure−property relationship guideline for further use of these compounds.

Free-electron lasers could enable X-ray imaging of single biological macromolecules and the study of protein dynamics, paving the way for a powerful new imaging tool in structural biology, but a low signal-to-noise ratio and missing regions in the detectors, colloquially termed `masks', affect data collection and hamper real-time evaluation of experimental data. In this article, the challenges posed by noise and masks are tackled by introducing a neural network pipeline that aims to restore diffraction intensities. For training and testing of the model, a data set of diffraction patterns was simulated from 10 900 different proteins with molecular weights within the range of 10–100 kDa and collected at a photon energy of 8 keV. The method is compared with a simple low-pass filtering algorithm based on autocorrelation constraints. The results show an improvement in the mean-squared error of roughly two orders of magnitude in the presence of masks compared with the noisy data. The algorithm was also tested at increasing mask width, leading to the conclusion that demasking can achieve good results when the mask is smaller than half of the central speckle of the pattern. The results highlight the competitiveness of this model for data processing and the feasibility of restoring diffraction intensities from unknown structures in real time using deep learning methods. Finally, an example is shown of this preprocessing making orientation recovery more reliable, especially for data sets containing very few patterns, using the expansion–maximization–compression algorithm.

The research on heteroaromatic azoswitches has been blossoming in recent years due to their astonishingly broad range of properties. Minimal chemical modifications can drastically change the demeanor of these switches, regarding photophysical and (photo)chemical properties, promoting them as ideal scaffolds for a vast variety of applications based on bistable light-addressable systems. However, most of the characteristics exhibited by heteroaryl azoswitches were found empirically, and only a few works focus on their rationalization. Herein we report on a mechanistic study employing phenylazoindoles as a model reference, combining spectroscopic experiments with comprehensive computational analysis. This approach will elucidate the intrinsic correlations between the molecular structure of the switch and its thermal behavior, allowing a more rational design transferable to various heteroaryl azoswitches.

The secular debate on the origin of life on our planet represents one of the open challenges for the scientific community. In this endeavour, chemistry has a pivotal role in disclosing novel scenarios that allow us to understand how the formation of simple organic molecules would be possible in the early primitive geological ages of Earth. Amino acids play a crucial role in biological processes. They are known to be formed in experiments simulating primitive conditions and were found in meteoric samples retrieved throughout the years. Understanding their formation is a key step for prebiotic chemistry. Following this reasoning, we performed a computational investigation over 100′000 structural isomers of natural amino acids. The results we have found suggest that natural amino acids are among the most thermodynamically stable structures and, therefore, one of the most probable ones to be synthesised among their possible isomers.