Silicon- and Germanium-Functionalized Perylene Diimides: Synthesis, Optoelectronic Properties, and Their Application as Non-fullerene Acceptors in Organic Solar Cells.

Organic solar cells have been continuously studied and developed through the last decades. A major step in their development was the introduction of fused-ring non-fullerene electron acceptors. Yet, beside their high efficiency, they suffer from complex synthesis and stability issues. Perylene-based non-fullerene acceptors, in contrast, can be prepared in only a few steps and display good photochemical and thermal stability. Herein, we introduce four monomeric perylene diimide acceptors obtained in a three-step synthesis. In these molecules, the semimetals silicon and germanium were added in the bay position, on one or both sides of the molecules, resulting in asymmetric and symmetric compounds with a red-shifted absorption compared to unsubstituted perylene diimide. Introducing two germanium atoms improved the crystallinity and charge carrier mobility in the blend with the conjugated polymer PM6. In addition, charge carrier separation is significantly influenced by the high crystallinity of this blend, as shown by transient absorption spectroscopy. As a result, the solar cells reached a power conversion efficiency of 5.38 %, which is one of the highest efficiencies of monomeric perylene diimide-based solar cells recorded to date.

Is Dose Volume a Better Predictor of Osteoradionecrosis Risk Than Total Dose for Patients Who Have Received Head and Neck Radiation?

PURPOSE: Oral and maxillofacial surgeons frequently encounter patients who require extractions following exposure to head and neck radiation, and they must assess the risk of extraction and consider alternatives such as deliberate root retention. The purpose of this study was to determine whether dose volume would be a better predictor for osteoradionecrosis (ORN) than total dose. METHODS: This is a retrospective cohort study of patients diagnosed with ORN following head and neck radiation (administered between January 2006 and December 2018) and a comparison group selected based on site and dosage who did not develop ORN. The predictor variables were total radiation dose and mandibular dose volume, and the outcome variable was ORN occurrence. Covariates included age, sex, cancer stage and site, radiation therapy type, smoking status, alcohol use, adjuvant chemotherapy use, medical comorbidities, and concomitant tumor surgery. Logistic regression models were employed and area under receiver operating characteristic curve (AUROC) and model accuracy (Acc) were used to determine the better predictor. RESULTS: A total of 56 patients were included in the study: 27 ORN positive (ORN+) and 29 matched controls who did not develop ORN (ORN-). Most patients were male (76.8%), considered smokers (78.6%), used alcohol (80.4%), were in stage IV (66.1%), received chemotherapy (75.0%), and received intensity modulated radiation therapy radiation (55.4%). The statistical models with V50 Gy (cc) and V65 Gy (cc) dosage variables exhibited greater predictability of ORN occurrence than total dose (AUROC: 0.90 vs 0.76 and model accuracy: 0.82 vs 0.75, respectively). CONCLUSIONS: The results suggest that following head and neck radiation, dose volume may be a better predictor of ORN risk than total dose. This finding is significant, both for the oral and maxillofacial surgeon who is preoperatively assessing ORN risk following radiation exposure, and for the radiation oncologist striving to minimize the risk associated with their treatment.

Multi-Institutional Validation of a Knowledge-Based Planning Model for Patients Enrolled in RTOG 0617: Implications for Plan Quality Controls in Cooperative Group Trials.

PURPOSE: This study aimed to evaluate the feasibility of using a single-institution, knowledge-based planning (KBP) model as a dosimetric plan quality control (QC) for multi-institutional clinical trials. The efficacy of this QC tool was retrospectively evaluated using a subset of plans submitted to Radiation Therapy Oncology Group (RTOG) study 0617. METHODS AND MATERIALS: A single KBP model was created using commercially available software (RapidPlan; Varian Medical Systems, Palo Alto, CA) and data from 106 patients with non-small cell lung cancer who were treated at a single institution. All plans had prescriptions that ranged from 60 Gy in 30 fractions to 74 Gy in 37 fractions and followed the planning guidelines from RTOG 0617. Two sets of optimization objectives were created to produce different trade-offs using the single KBP model predictions: one prioritizing target coverage and a second prioritizing lung sparing (LS) while allowing an acceptable variation in target coverage. Three institutions submitted a high volume of clinical plans to RTOG 0617 and provided data on 25 patients, which were replanned using both sets of optimization objectives. Model-generated, dose-volume histogram predictions were used to identify patients who exceeded the lung clinical target volume (CTV) V20Gy >37% and would benefit from the LS objectives. Overall plan quality differences between KBP-generated plans and clinical plans were evaluated at RTOG 0617-defined dosimetric endpoints. RESULTS: Target coverage and organ at risk sparing was significantly improved for most KBP-generated plans compared with those from clinical trial data. The KBP model using prioritized target coverage objectives reduced heart Dmean and V40Gy by 2.1 Gy and 5.2%, respectively. Similarly, using LS objectives reduced the lung CTV Dmean and V20Gy by 2.0 Gy and 2.9%, respectively. The KBP predictions correctly identified all patients with lung CTV V20Gy > 37% (5 of 25 patients) and significantly reduced the dose to the lung CTV by applying the LS optimization objectives. CONCLUSIONS: A single-institution KBP model can be applied as a QC tool for multi-institutional clinical trials to improve overall plan quality and provide decision-support to determine the need for anatomy-based dosimetric trade-offs.