Reductive Activation of Aryl Chlorides by Tuning the Radical Cation Properties of N ‐Phenylphenothiazines as Organophotoredox Catalysts

Aryl chlorides as substrates for arylations present a particular challenge for photoredox catalytic activation due to their strong C(sp$^2$)−Cl bond and their strong reduction potential. Electron-rich N-phenylphenothiazines, as organophotoredox catalysts, are capable of cleaving aryl chlorides simply by photoinduced electron transfer without the need for an additional electrochemical activation setup or any other advanced photocatalysis technique. Due to the extremely strong reduction potential in the excited state of the N-phenylphenothiazines the substrate scope is high and includes aryl chlorides both with electron-withdrawing and electron-donating substituents. We evidence this reactivity for photocatalytic borylations and phosphonylations. Advanced time-resolved transient absorption spectroscopy in combination with electrochemistry was the key to elucidating and comparing the unusual photophysical properties not only of the N-phenylphenothiazines, but also of their cation radicals as the central intermediates in the photocatalytic cycle. The revealed photophysics allowed the excited-state and radical-cation properties to be fine-tuned by the molecular design of the N-phenylphenothiazines; this improved the photocatalytic activity

Light-driven charge accumulation of a molecular Cu(I) complex for storage of photoredox equivalents

The diurnal day/night cycle is presently of great interest for harvesting solar energy aimed at rendering suitable energy storage schemes. To this end we present a noble-metal free system based on a Cu(I) 4H-imidazolate complex, that is efficiently photoreduced in the presence of a sacrificial donor. The two-electron reduced species obtained can be stored in the dark for more than 14 hours. In a dark reaction, the photoredox equivalents can subsequently be transferred to the electron acceptors methyl viologen or oxygen, while the starting Cu(I) complex is almost completely regained. Repetition of this process revealed a charging capacity of 72% after four cycles. The implications of light-driven charge accumulation and prolonged storage times for solar battery and photoredox catalysis are discusse

Utility of a Molecular Classifier as a Complement to High-Resolution Computed Tomography to Identify Usual Interstitial Pneumonia

Rationale: Usual interstitial pneumonia (UIP) is the defining morphology of idiopathic pulmonary fibrosis (IPF). Guidelines for IPF diagnosis conditionally recommend surgical lung biopsy for histopathology diagnosis of UIP when radiology and clinical context are not definitive. A \u201cmolecular diagnosis of UIP\u201d in transbronchial lung biopsy, the Envisia Genomic Classifier, accurately predicted histopathologic UIP. Objectives: We evaluated the combined accuracy of the Envisia Genomic Classifier and local radiology in the detection of UIP pattern. Methods: Ninety-six patients who had diagnostic lung pathology as well as a transbronchial lung biopsy for molecular testing with Envisia Genomic Classifier were included in this analysis. The classifier results were scored against reference pathology. UIP identified on high-resolution computed tomography (HRCT) as documented by features in local radiologists\u2019 reports was compared with histopathology. Measurements and Main Results: In 96 patients, the Envisia Classifier achieved a specificity of 92.1% (confidence interval [CI],78.6\u201398.3%) and a sensitivity of 60.3% (CI, 46.6\u201373.0%) for histology-proven UIP pattern. Local radiologists identified UIP in 18 of 53 patients with UIP histopathology, with a sensitivity of 34.0% (CI, 21.5\u201348.3%) and a specificity of 96.9% (CI, 83.8\u2013100%). In conjunction with HRCT patterns of UIP, the Envisia Classifier results identified 24 additional patients with UIP (sensitivity 79.2%; specificity 90.6%). Conclusions: In 96 patients with suspected interstitial lung disease, the Envisia Genomic Classifier identified UIP regardless of HRCT pattern. These results suggest that recognition of a UIP pattern by the Envisia Genomic Classifier combined with HRCT and clinical factors in a multidisciplinary discussion may assist clinicians in making an interstitial lung disease (especially IPF) diagnosis without the need for a surgical lung biopsy