A series of (carbene)Au(I)(aryl) complexes are reported. The nature of the lowest excited state in these complexes changes character from metal-to-ligand charge transfer (MLCT) to interligand charge transfer (ICT) with increasing electron-donating strength of the aryl ligand. Complexes that have the MLCT lowest excited state undergo a Renner-Teller bending distortion upon excitation. Such a distortion leads to a large rate of nonradiative decay, on the order of 108 s-1. Renner-Teller-based nonradiative decay does not occur in chromophores with an ICT emissive state. Introducing a julolidine moiety and ortho-methyl substituents to the aryl group makes the molecule rigid and hinders the rotation along the Au-Caryl-coordinate bond. Consequently, the nonradiative decay rates of these ICT emitters are decreased and become lower than the radiative decay rate constants (kr = 105 s-1). Thus, high-luminescent efficiencies (ΦPL = 0.61 and 0.77) along with short lifetimes (τ less then 2 μs) are obtained for yellow and green emitters, respectively. Thermally assisted delayed fluorescence behavior is observed, owing to the small exchange energy (ΔEST less then 1600 cm-1) in these emitters.The concept of forensic sciences as mere trace analysis has been modified by the idea of forensic intelligence, which entails applying data to make decisions within the investigative process. Many countries are engaged in combating drug trafficking and drug use because they are related to public health and safety issues. Prohibiting the consumption of traditional drugs has led new psychoactive substances (NPSs) to emerge. NPSs consist of compounds that resemble the initially banned substance and which aim to mimic the traditional drug recreational effects while circumventing drug legislation.  https://www.selleckchem.com/products/monastrol.html  For example, synthetic cannabinoids are sprayed on herbal products to reproduce the cannabis recreational effects. According to the United Nations Office on Drugs and Crime (UNODC), the toxic effects of synthetic cannabis types are unknown, and harm and fatalities associated with the use of these drugs have been reported. Information on the characterization related to these species is lacking. The rate at which NPSs appear poses a significant challenge because employing conventional methods to understand the characteristics of these compounds may require time and be costly. This work uses in silico practices as an alternative to understand how NPSs related to cannabis behave. We apply quantum chemistry methods to evaluate several synthetic cannabinoids recognized in forensic samples. More specifically, we generate infrared spectra that can be employed as a benchmark for NPSs. We apply a multivariate classification to evaluate the results. We conclude that in silico methods are an alternative that provide information about the spectra of undetected substances. This information can help to identify new drugs, to increase knowledge about them, and to feed information procedures.Layered Na-based oxides with the general composition of NaxTMO2 (TM transition metal) have attracted significant attention for their high compositional diversity that provides tunable electrochemical performance for electrodes in sodium-ion batteries. The various compositions bring forward complex structural chemistry that is decisive for the layered stacking structure, Na-ion conductivity, and the redox activity, potentially promising new avenues in functional material properties. In this work, we have explored the maximum Na content in P2-type layered oxides and discovered that the high-content Na in the host enhances the structural stability; moreover, it promotes the oxidation of low-valent cations to their high oxidation states (in this case Ni2+). This can be rationalized by the increased hybridization of the O(2p)-TM(3d-eg*) states, affecting both the local TM environment as well as the interactions between the NaO2 and TMO2 layers. These properties are highly beneficial for the Na storage capabilities as required for cathode materials in sodium-ion batteries. It leads to excellent Na-ion mobility, a large storage capacity (>100 mAh g-1 between 2.0-4.0 V), yet preventing the detrimental sliding of the TMO2 layers (P2-O2 structural transition), as reflected by the ultralong cycle life (3000 (dis)charge cycles demonstrated). These findings expand the horizons of high Na-content P2-type materials, providing new insights of the electronic and structural chemistry for advanced cathode materials.Drug research and development is a time-consuming and high-cost task, pressing an urgent demand to identify novel indications of approved drugs, referred to as drug repositioning, which provides an economical and efficient way for drug discovery. With increasing volumes of large-scale chemical, genomic, and pharmacological data sets generated by the high-throughput technique, it is crucial to develop systematic and rational computational approaches to identify new indications of approved drugs. In this paper, we introduce HNet-DNN, which utilizes a deep neural network (DNN), to predict new drug-disease associations based on the features extracted from the drug-disease heterogeneous network. Instead of the straightforward concatenation of chemical and phenotypic features as the input of DNN, we used these raw features of drugs and diseases to construct a drug-drug similarity network and a disease-disease similarity network, and then built a drug-disease heterogeneous network by integrating known drug-disease associations. Subsequently, we extracted topological features for drug-disease associations from the heterogeneous network and used them to train a DNN model. Our intensive performance evaluations demonstrated that HNet-DNN effectively exploits the features of the heterogeneous network to boost the predictive performance of drug-disease associations. Compared with a couple of typical classifiers and competitive approaches, our method not only achieved state-of-the-art performance but also effectively alleviated the overfitting problem. Moreover, we ran HNet-DNN to predict new drug-disease associations and carried out case studies to verify the effectiveness of our method.