Enantioselective Paternò-Büchi Reactions: Strategic Application of a Triplet Rebound Mechanism for Asymmetric Photocatalysis.

The Paternò-Büchi reaction is the [2 + 2] photocycloaddition of a carbonyl with an alkene to afford an oxetane. Enantioselective catalysis of this classical photoreaction, however, has proven to be a long-standing challenge. Many of the best-developed strategies for asymmetric photochemistry are not suitable to address this problem because the interaction of carbonyls with Brønsted or Lewis acidic catalysts can alter the electronic structure of their excited state and divert their reactivity toward alternate photoproducts. We show herein that a triplet rebound strategy enables the stereocontrolled reaction of an excited-state carbonyl compound in its native, unbound state. These studies have resulted in the development of the first highly enantioselective catalytic Paternò-Büchi reaction, catalyzed by a novel hydrogen-bonding chiral Ir photocatalyst.

Predicting Emission Spectra of Heteroleptic Iridium Complexes Using Artificial Chemical Intelligence.

We report a deep learning-based approach to accurately predict the emission spectra of phosphorescent heteroleptic [Ir( C ∧ N ${{\rm{C}}^\wedge {\rm{N}}}$ )2( N ∧ N ${{\rm{N}}^\wedge {\rm{N}}}$ )]+ complexes, enabling the rapid discovery of novel Ir(III) chromophores for diverse applications including organic light-emitting diodes and solar fuel cells. The deep learning models utilize graph neural networks and other chemical features in architectures that reflect the inherent structure of the heteroleptic complexes, composed of C ∧ N ${{\rm{C}}^\wedge {\rm{N}}}$ and N ∧ N ${{\rm{N}}^\wedge {\rm{N}}}$ ligands, and are thus geared towards efficient training over the dataset. By leveraging experimental emission data, our models reliably predict the full emission spectra of these complexes across various emission profiles, surpassing the accuracy of conventional DFT and correlated wavefunction methods, while simultaneously achieving robustness to the presence of imperfect (noisy, low-quality) training spectra. We showcase the potential applications for these and related models for in silico prediction of complexes with tailored emission properties, as well as in "design of experiment" contexts to reduce the synthetic burden of high-throughput screening. In the latter case, we demonstrate that the models allow us to exploit a limited amount of experimental data to explore a wide range of chemical space, thus leveraging a modest synthetic effort.

Self-Assembling Micelles Based on an Intrinsically Disordered Protein Domain.

The self-assembly of micellar structures from diblock polymers that contain hydrophilic and hydrophobic domains has been of great interest for the encapsulation of drugs and other hydrophobic molecules. While most commercially used surfactants are derived from hydrocarbon sources, there have been recent efforts to replace these with biodegradable, nontoxic, biologically synthesized alternatives. Previous examples have primarily examined naturally occurring self-assembling proteins, such as silk and elastin-like sequences. Herein, we describe a new series of fusion proteins that have been developed to self-assemble spontaneously into stable micelles that are 27 nm in diameter after enzymatic cleavage of a solubilizing protein tag. The sequences of the proteins are based on a human intrinsically disordered protein, which has been appended with a hydrophobic segment. The micelles were found to form across a broad range of pH, ionic strength, and temperature conditions, with critical micelle concentration (CMC) values in the low micromolar range, 3 orders of magnitude lower than the CMC of commonly used surfactant sodium dodecyl sulfate (SDS). The reported micelles were found to solubilize hydrophobic metal complexes and organic molecules, suggesting their potential suitability for catalysis and drug delivery applications. Furthermore, the inherent flexibility in the design of these protein sequences enables the encoding of additional functionalities for many future applications. Overall, this work represents a new biomolecular alternative to traditional surfactants that are based on nonrenewable and poorly biodegradable hydrocarbon sources.

Rotaxane Probes for the Detection of Hydrogen Peroxide by 129 Xe HyperCEST NMR Spectroscopy.

The development of sensitive and chemically selective MRI contrast agents is imperative for the early detection and diagnosis of many diseases. Conventional responsive contrast agents used in 1 H MRI are impaired by the high abundance of protons in the body. 129 Xe hyperCEST NMR/MRI comprises a highly sensitive complement to traditional 1 H MRI because of its ability to report specific chemical environments. To date, the scope of responsive 129 Xe NMR contrast agents lacks breadth in the specific detection of small molecules, which are often important markers of disease. Herein, we report the synthesis and characterization of a rotaxane-based 129 Xe hyperCEST NMR contrast agent that can be turned on in response to H2 O2 , which is upregulated in several disease states. Added H2 O2 was detected by 129 Xe hyperCEST NMR spectroscopy in the low micromolar range, as well as H2 O2 produced by HEK 293T cells activated with tumor necrosis factor.

Covalent capture and electrochemical quantification of pathogenic E. coli.

Pathogenic E. coli pose a significant threat to public health, as strains of this species cause both foodborne illnesses and urinary tract infections. Using a rapid bioconjugation reaction, we selectively capture E. coli at a disposable gold electrode from complex solutions and accurately quantify the pathogenic microbes using electrochemical impedance spectroscopy.