We report the characterization of carbon nanodots (CNDs) synthesized under mild and controlled conditions, that is, in a microwave reactor. The CNDs thus synthesized exhibit homogeneous and narrowly dispersed optical properties. They are thus well suited as a testbed for studies of the photophysics of carbon-based nanoscopic emitters. In addition to steady-state investigations, time-correlated single-photon counting, fluorescence up-conversion, and transient pump probe absorption spectroscopy were used to elucidate the excited-state dynamics. Moreover, quenching the CND-based emission with electron donors or acceptors helped shed light on the nature of individual states. Density functional theory and semiempirical configuration-interaction calculations on model systems helped understand the fundamental structure-property relationships for this novel type of material.

No abstract

No abstract

Covalent organic frameworks (COFs), formed by reversible condensation of rigid organic building blocks, are crystalline and porous materials of great potential for catalysis and organic electronics. Particularly with a view of organic electronics, achieving a maximum degree of crystallinity and large domain sizes while allowing for a tightly π-stacked topology would be highly desirable. We present a design concept that uses the 3D geometry of the building blocks to generate a lattice of uniquely defined docking sites for the attachment of consecutive layers, thus allowing us to achieve a greatly improved degree of order within a given average number of attachment and detachment cycles during COF growth. Synchronization of the molecular geometry across several hundred nanometers promotes the growth of highly crystalline frameworks with unprecedented domain sizes. Spectroscopic data indicate considerable delocalization of excitations along the π-stacked columns and the feasibility of donor-acceptor excitations across the imine bonds. The frameworks developed in this study can serve as a blueprint for the design of a broad range of tailor-made 2D COFs with extended π-conjugated building blocks for applications in photocatalysis and optoelectronics.

Chemical compound space refers to the vast set of all possible chemical compounds, estimated to contain 1060 molecules. While intractable as a whole, modern machine learning (ML) is increasingly capable of accurately predicting molecular properties in important subsets. Here, we therefore engage in the ML-driven study of even larger reaction space. Central to chemistry as a science of transformations, this space contains all possible chemical reactions. As an important basis for ‘reactive’ ML, we establish a first-principles database (Rad-6) containing closed and open-shell organic molecules, along with an associated database of chemical reaction energies (Rad-6-RE). We show that the special topology of reaction spaces, with central hub molecules involved in multiple reactions, requires a modification of existing compound space ML-concepts. Showcased by the application to methane combustion, we demonstrate that the learned reaction energies offer a non-empirical route to rationally extract reduced reaction networks for detailed microkinetic analyses.