An emissive charge-transfer excited-state at the well-defined hetero-nanostructure interface of an organic conjugated molecule and two-dimensional inorganic nanosheet

Precise engineering of excited-state interactions between an organic conjugated molecule and a two-dimensional semiconducting inorganic nanosheet, specifically the manipulation of charge-transfer excited (CTE) states, still remains a challenge for state-of-the-art photochemistry. Herein, we report a long-lived, highly emissive CTE state at structurally well-defined hetero-nanostructure interfaces of photoactive pyrene and two-dimensional MoS2 nanosheets via an N-benzylsuccinimide bridge (Py-Bn-MoS2). Spectroscopic measurements reveal that no charge-transfer state is formed in the ground state, but the locally-excited (LE) state of pyrene in Py-Bn-MoS2 efficiently generates an unusual emissive CTE state. Theoretical studies elucidate the interaction of MoS2 vacant orbitals with the pyrene LE state to form a CTE state that shows a distinct solvent dependence of the emission energy. This is the first example of organic-inorganic 2D hetero-nanostructures displaying mixed luminescence properties by an accurate design of the bridge structure, and therefore represents an important step in their applications for energy conversion and optoelectronic devices and sensors

Prediction of Intracranial Aneurysm Rupture Risk Using Non-Invasive Radiomics Analysis Based on Follow-Up Magnetic Resonance Angiography Images: A Preliminary Study

This is the first preliminary study to develop prediction models for aneurysm rupture risk using radiomics analysis based on follow-up magnetic resonance angiography (MRA) images. We selected 103 follow-up images from 18 unruptured aneurysm (UA) cases and 10 follow-up images from 10 ruptured aneurysm (RA) cases to build the prediction models. A total of 486 image features were calculated, including 54 original features and 432 wavelet-based features, within each aneurysm region in the MRA images for the texture patterns. We randomly divided the 103 UA data into 50 training and 53 testing data and separated the 10 RA data into 1 test and 9 training data to be increased to 54 using a synthetic minority oversampling technique. We selected 11 image features associated with UAs and RAs from 486 image features using the least absolute shrinkage and the selection operator logistic regression and input them into a support vector machine to build the rupture prediction models. An imbalanced adjustment training and test strategy was developed. The area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were 0.971, 0.948, 0.700, and 0.953, respectively. This prediction model with non-invasive MRA images could predict aneurysm rupture risk for SAH prevention