Integrated System Built for Small-Molecule Semiconductors via High-Throughput Approaches.

High-throughput synthesis of solution-processable structurally variable small-molecule semiconductors is both an opportunity and a challenge. A large number of diverse molecules provide a possibility for quick material discovery and machine learning based on experimental data. However, the diversity of the molecular structure leads to the complexity of molecular properties, such as solubility, polarity, and crystallinity, which poses great challenges to solution processing and purification. Here, we first report an integrated system for the high-throughput synthesis, purification, and characterization of molecules with a large variety. Based on the principle "Like dissolves like," we combine theoretical calculations and a robotic platform to accelerate the purification of those molecules. With this platform, a material library containing 125 molecules and their optical-electronic properties was built within a timeframe of weeks. More importantly, the high repeatability of recrystallization we design is a reliable approach to further upgrading and industrial production.

Graph neural networks for materials science and chemistry.

Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.

A proposal for statistical evaluation of the detection of gunshot residues on a suspect.

The possibility of accidental contamination of a suspect by gunshot residues (GSRs) is considered. If two hypotheses are taken into account ("the suspect has shot a firearm" and "the suspect has not shot a firearm"), the likelihood ratio of the conditional probabilities of finding a number n of GSRs is defined. Choosing two Poisson distributions, the parameter lambda of the first one coincides with the mean number of GSRs that can be found on a firearm shooter, while the parameter mu of the second one is the mean number of GSRs that can be found on a nonshooter. In this scenario, the likelihood ratio of the conditional probabilities of finding a number n of GSRs in the two hypotheses can be easily calculated. The evaluation of the two parameters lambda and mu and of the goodness of the two probability distributions is performed by using different sets of data: "exclusive" lead-antimony-barium GSRs have been detected in two populations of 31 and 28 police officers at diverse fixed times since firearm practice, and in a population of 81 police officers who stated that they had not handled firearms for almost 1 month. The results show that the Poisson distributions well fit the data for both shooters and nonshooters, and that the probability of detection of two or more GSRs is normally greater if the suspect has shot firearms.

X-ray mapping technique: a preliminary study in discriminating gunshot residue particles from aggregates of environmental occupational origin.

The possibility of detection of lead-antimony-barium aggregates from non-firearm sources is confirmed according to the tests performed on brake pads, and firework and automobile workers. Moreover, information on particles taken from cartridge cases shows the relative feeble importance of the morphology in distinguishing gunshot residues (GSRs). Furthermore, also the presence in the spectrum of other elements (e.g., iron) is not so conclusive. In this panorama, the possibility of discriminating gunshot residue particles from other non-firearm lead-antimony-barium aggregates is investigated: the proposed method is based on X-ray mapping technique--currently applied used in Reparto Carabinieri Investigazioni Scientifiche in Rome, the forensic service of Italian Carabinieri--according to which the spatial distribution of the emission energy of each element of the sample is pictured. Gunshot residues present the same lead-antimony-barium distribution (or at least the same antimony-barium distribution with lead nodules), as some other environmental occupational aggregates do not (different plaques of lead, antimony, and barium). So, X-ray mapping technique can offer a new fundamental evaluation parameter in analysis of gunshot residues with scanning electron microscopy/energy-dispersive (SEM/EDS) spectrometry, and new standards could be considered.