Nitrogen-rich carbon dots as the antisolvent additive for perovskite-based photovoltaic devices.

Solution-processed perovskite solar cells (PSCs) have demonstrated a tremendous growth in power conversion efficiency (PCE). A high-quality, defect-free perovskite-based active layer is a key point to enhance PSC performance. Introduction of additives and interlayers have proved to be an effective tool to passivate surface defects, control crystal growth, and improve PSC stability.

Influence of chemical treatment and interaction with matrix on room temperature phosphorescence of carbon dots.

In this work, composite materials were formed based on various matrices (polymer and porous cellulose matrix) and carbon dots (CDs) with intense room-temperature phosphorescence (RTP). The effect of post-synthesis chemical treatment with citric acid or urea on the optical properties of composites was studied: the increase in carboxy and carbonyl groups led to an increase of RTP signals that could be seen with the naked eye over several seconds. The fabricated composites demonstrated good stability and reversibility of RTP signals by mild heating.

Optical Properties Prediction for Red and Near-Infrared Emitting Carbon Dots Using Machine Learning.

Functional nanostructures build up a basis for the future materials and devices, providing a wide variety of functionalities, a possibility of designing bio-compatible nanoprobes, etc. However, development of new nanostructured materials via trial-and-error approach is obviously limited by laborious efforts on their syntheses, and the cost of materials and manpower. This is one of the reasons for an increasing interest in design and development of novel materials with required properties assisted by machine learning approaches.

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures.

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy.

Green and Red Emissive N,O-Doped Chiral Carbon Dots Functionalized with l-Cysteine.

Although chirality plays an important role in the natural world, it has also attracted much scientific attention in nanotechnology, in particular, spintronics and bioapplications. Chiral carbon dots (CDs) are promising nanoparticles for sensing and bioimaging since they are biocompatible, ecofriendly, and free from toxic elements. Herein, green and red emissive chiral CDs are fabricated via surface modification treatment of achiral CDs at room temperature.