RESUMO
Poly(3-hexylthiophene) (P3HT) represents a promising hole transport material for emerging perovskite solar cells (PSCs) due to its appealing merits of high thermal stability and appropriate hydrophobicity. Nonetheless, large energy losses at the P3HT/perovskite interface lead to unsatisfied efficiency and stability of the devices. Herein, two ionic dendritic molecules, 3,3'-(2,7-bis(3,6-bis(bis(4-methoxyphenyl)amino)-9H-carbazol-9-yl)-9H-fluorene-9,9-diyl)bis(N,N,N-trimethylpropan-1-aminium) iodide and 3,3'-(2,7-bis(bis(4-(bis(4-methoxyphenyl)amino)phenyl)amino)-9H-fluorene-9,9-diyl)bis(N,N,N-trimethylpropan-1-aminium) iodide, namely, MPA-Cz-FAI and MPA-PA-FAI, are rationally designed as the interlayer to enhance interfacial compatibility. The dendritic backbone with conjugated structure endows the hole transport layer with high conductivity, derived from the more ordered microstructure with larger crystallization and higher connectivity of domain zones. Besides, a better energy level alignment is established between P3HT and perovskite, which enhances the charge extraction and transport yield. In addition, the peripheral methoxy groups enable effective defect passivation at the interface to suppress nonradiative recombination and the quaternary ammonium iodide serving as side chains enable efficient interfacial hole extraction contributing to enhanced charge collection yield. As a result, the dopant-free P3HT-based PSCs modified with MPA-Cz-PAI deliver a champion efficiency of 19.7%, significantly higher than that of the control devices (15.4%). More encouragingly, the unencapsulated devices demonstrate competitive environmental stability by retaining over 85% of its initial efficiency after 1500 h of storage under humid conditions (70% relative humidity). This work provides an effective molecular design strategy for interface engineering, envisaging a bright prospect for the further development of efficient and stable perovskite solar cells.
RESUMO
PURPOSE: To investigate the treatment response prediction feasibility and accuracy of an integrated model combining computed tomography (CT) radiomic features and dosimetric parameters for patients with esophageal cancer (EC) who underwent concurrent chemoradiation (CRT) using machine learning. METHODS: The radiomic features and dosimetric parameters of 94 EC patients were extracted and modeled using Support Vector Classification (SVM) and Extreme Gradient Boosting algorithm (XGBoost). The 94-sample dataset was randomly divided into a 70-sample training subset and a 24-sample independent test set while keeping the class proportions intact via stratification. A receiver operating characteristic (ROC) curve was used to assess the performance of models using radiomic features alone and using combined radiomic features and dosimetric parameters. RESULTS: A total of 42 radiomic features and 18 dosimetric parameters plus the patients' characteristic parameters were extracted for these 94 cases (58 responders and 36 non-responders). XGBoost plus principal component analysis (PCA) achieved an accuracy and area under the curve of 0.708 and 0.541, respectively, for models with radiomic features combined with dosimetric parameters, and 0.689 and 0.479, respectively, for radiomic features alone. Image features of GlobalMean X.333.1, Coarseness, Skewness, and GlobalStd contributed most to the model. The dosimetric parameters of gross tumor volume (GTV) homogeneity index (HI), Cord Dmax, Prescription dose, Heart-Dmean, and Heart-V50 also had a strong contribution to the model. CONCLUSIONS: The model with radiomic features combined with dosimetric parameters is promising and outperforms that with radiomic features alone in predicting the treatment response of patients with EC who underwent CRT. KEY POINTS: ⢠The model with radiomic features combined with dosimetric parameters is promising in predicting the treatment response of patients with EC who underwent CRT. ⢠The model with radiomic features combined with dosimetric parameters (prediction accuracy of 0.708 and AUC of 0.689) outperforms that with radiomic features alone (best prediction accuracy of 0.625 and AUC of 0.412). ⢠The image features of GlobalMean X.333.1, Coarseness, Skewness, and GlobalStd contributed most to the treatment response prediction model. The dosimetric parameters of GTV HI, Cord Dmax, Prescription dose, Heart-Dmean, and Heart-V50 also had a strong contribution to the model.