Ultrafast Electron Transfer from CuInS Quantum Dots to a Molecular Catalyst for Hydrogen Production: Challenging Diffusion Limitations.

Systems integrating quantum dots with molecular catalysts are attracting ever more attention, primarily owing to their tunability and notable photocatalytic activity in the context of the hydrogen evolution reaction (HER) and CO reduction reaction (CORR). CuInS (CIS) quantum dots (QDs) are effective photoreductants, having relatively high-energy conduction bands, but their electronic structure and defect states often lead to poor performance, prompting many researchers to employ them with a core-shell structure. Molecular cobalt HER catalysts, on the other hand, often suffer from poor stability.

Volumetric modulated arc therapy dose prediction and deliverable treatment plan generation for prostate cancer patients using a densely connected deep learning model.

Background And Purpose: Radiation therapy treatment planning is a manual, time-consuming task that might be accelerated using machine learning algorithms. In this study, we aimed to evaluate if a triplet-based deep learning model can predict volumetric modulated arc therapy (VMAT) dose distributions for prostate cancer patients.

Materials And Methods: A modified U-Net was trained on triplets, a combination of three consecutive image slices and corresponding segmentations, from 160 patients, and compared to a baseline U-Net.

Imidazolium-modification enhances photocatalytic CO reduction on ZnSe quantum dots.

Colloidal photocatalysts can utilize solar light for the conversion of CO to carbon-based fuels, but controlling the product selectivity for CO reduction remains challenging, in particular in aqueous solution. Here, we present an organic surface modification strategy to tune the product selectivity of colloidal ZnSe quantum dots (QDs) towards photocatalytic CO reduction even in the absence of transition metal co-catalysts. Besides H, imidazolium-modified ZnSe QDs evolve up to 2.