Josephayers4959

ConspectusRoom-temperature phosphorescence (RTP) with a long afterglow from purely organic molecular aggregates has recently attracted many investigations because traditionally only inorganic and transition-metal complexes can emit phosphorescence at room temperature. Purely organic molecules can exhibit phosphorescence only at cryogenic temperatures and under inert conditions in solution. However, recently, a number of organic compounds have been found to demonstrate bright RTP upon aggregation, sometimes with a remarkable morphology dependence. We intended to rationalize such aggregation-induced organic RTP through theoretical investigation and quantum chemistry calculations by invoking intermolecular interaction effects. And we have identified the molecular descriptors for the molecular design of RTP materials.In this Account, we started with the proposition of the mechanism of intermolecular electrostatic-interaction-induced RTP at the molecular level by using molecular dynamics simulations, hybrid quantuize the phosphorescence efficiency and lifetime, respectively, derived from fundamental photophysical processes and requirements to obey the El-Sayed rule and generate phosphorescence. For a prototypical RTP system consisting of a carbonyl group and π-conjugated segments, the excited states can be regarded as an admixture of n → π* (with portion α) and π → π* (with portion β). The intersystem crossing (ISC) rate of S1 → Tn is mostly governed by the modification of the product of α and β, and the nonradiative rate of T1 → S0 is determined by the β value of T1. Thus, we employ γ = α × β and β to describe the phosphorescence efficiency and lifetime, respectively, which have been successfully applied in the molecular design of efficient and long-lived RTP systems in experiments. The molecular descriptors outlined in this Account, which are easily obtained from simple quantum chemistry calculations, are expected to play important roles in the machine-learning-based molecular screening in the future.The properties of natural lipid bilayers are vital to the regulation of many membrane proteins. Scaffolded nanodiscs provide an in vitro lipid bilayer platform to host membrane proteins in an environment that approximates native lipid bilayers. However, the properties of scaffold-enclosed bilayers may depart significantly from those of bulk cellular membranes. Therefore, to improve the usefulness of nanodiscs it is essential to understand the properties of lipids restricted by scaffolds. We used computational molecular dynamics and modeling approaches to understand the effects of nanodisc size, scaffold type (DNA or protein), and hydrophobic modification of DNA scaffolds on bilayer stability and degree to which the properties of enclosed bilayers approximate bulk bilayers. With respect to achieving bulk bilayer behavior, we found that charge neutralization of DNA scaffolds was more important than the total hydrophobic content of their modifications bilayer properties were better for scaffolds having a large number of short alkyl chains than those having fewer long alkyl chains. Further, complete charge neutralization of DNA scaffolds enabled better lipid binding, and more stable bilayers, as shown by steered molecular dynamics simulations that measured the force required to dislodge scaffolds from lipid bilayer patches. Considered together, our simulations provide a guide to the design of DNA-scaffolded nanodiscs suitable for studying membrane proteins.A selective photoelectrochemical (PEC) sensor has been designed for the signal-on detection of H2S using g-C3N4 nanosheets that were treated with N2 plasma for depositing Cd probes. It was discovered that the yielded Cd/N@g-C3N4 nanocomposites could present enhanced photocurrents of specific responses to H2S under visible light irradiation, in contrast to the ones without the pretreatment of N2 plasma showing no H2S response. Herein, the Cd probes deposited on g-C3N4 nanosheets might react with H2S to generate CdS on Cd/N@g-C3N4, forming the efficient heterojunctions. Especially, the plasma-derived N contents might act as the "bridge" to promote charge transfer between the generated CdS and g-C3N4, resulting in the "signal-on" PEC responses to H2S. KRIBB11 inhibitor A selective PEC sensor was thereby developed for sensing H2S of concentrations linearly ranging from 40.0 to 10,000 pM, with a detection limit of about 21 pM. Also, the feasibility of sensing H2S in industrial waste gas was demonstrated by recovery tests. More importantly, this N2 plasma treatment route for g-C3N4 nanosheets may open a new door toward the construction of a Cd probe-based heterojunction for the signal-on PEC sensing platform, which is promising for the wide application in the fields of environmental monitoring, food safety, and biomedical analysis.Hepatic steatosis (fatty liver) is a severe liver disease induced by the excessive accumulation of fatty acids in hepatocytes. In this study, we developed reliable in silico models for predicting hepatic steatosis on the basis of an in vivo data set of 1041 compounds measured in rodent studies with repeated oral exposure. The imbalanced nature of the data set (18, with the "steatotic" compounds belonging to the minority class) required the use of meta-classifiers-bagging with stratified under-sampling and Mondrian conformal prediction-on top of the base classifier random forest. One major goal was the investigation of the influence of different descriptor combinations on model performance (tested by predicting an external validation set) physicochemical descriptors (RDKit), ToxPrint features, as well as predictions from in silico nuclear receptor and transporter models. All models based upon descriptor combinations including physicochemical features led to reasonable balanced accuracies (BAs between 0.65 and 0.69 for the respective models). Combining physicochemical features with transporter predictions and further with ToxPrint features gave the best performing model (BAs up to 0.7 and efficiencies of 0.82). Whereas both meta-classifiers proved useful for this highly imbalanced toxicity data set, the conformal prediction framework also guarantees the error level and thus might be favored for future studies in the field of predictive toxicology.