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Purpose To develop and test a three‐dimensional (3D) deep learning model for predicting 3D voxel‐wise dose distributions for intensity‐modulated radiotherapy (IMRT). Methods A total of 122 postoperative rectal cancer cases treated by IMRT were considered in the study, of which 100 cases were randomly selected as the training–validating set and the remaining as the testing set. A 3D deep learning model named 3D U‐Res‐Net_B was constructed to predict 3D dose distributions. Eight types of 3D matrices from CT images, contoured structures, and beam configurations were fed into the independent input channel, respectively, and the 3D matrix of dose distributions was taken as the output to train the 3D model. The obtained 3D model was used to predict new 3D dose distributions. The predicted accuracy was evaluated in two aspects: (a) The dice similarity coefficients (DSCs) of different isodose volumes, the average dose difference of all voxels within the body, and 3%/5 mm global gamma passing rates of organs at risks (OARs) and planned target volume (PTV) were used to address the spatial correspondence between predicted and clinical delivered 3D dose distributions; (b) The dosimetric index (DI) including homogeneity index, conformity index, V50, V45 for PTV and OARs between predicted and clinical truth were statistically analyzed with the paired‐samples t test. The model was also compared with 3D U‐Net and the same architecture model without beam configurations input (named as 3D U‐Res‐Net_O). Results The 3D U‐Res‐Net_B model predicted 3D dose distributions accurately. For the 22 testing cases, the average prediction bias ranged from −1.94% to 1.58%, and the overall mean absolute errors (MAEs) was 3.92 ± 4.16%; there was no statistically significant difference for nearly all DIs. The model had a DSCs value above 0.9 for most isodose volumes, and global 3D gamma passing rates varying from 0.81 to 0.90 for PTV and OARs, clearly outperforming 3D U‐Res‐Net_O and being slightly superior to 3D U‐Net. Conclusions This study developed a more general deep learning model by considering beam configurations input and achieved an accurate 3D voxel‐wise dose prediction for rectal cancer treated by IMRT, a potentially easier clinical implementation for more comprehensive automatic planning.

The PI-SERS signals of R6G molecules on a MoS2/Au substrate are enhanced at 2.39 GPa by a two-step charge transfer process.

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The mixed Pb–Sn perovskites have the ideal bandgap of ≈1.2 eV for photovoltaic application. However, the undesirable p‐doping introduced by Sn2+ oxidation restrains the device's power conversion efficiency (PCE) and stability. Herein, an additive strategy with p‐phenyl dimethylammonium iodide (PhDMADI) is proposed, which has a bulky divalent organic cation and facilitates the formation of Dion–Jacobson phase‐based quasi‐2D perovskites at the grain boundaries. It is found that this unique 2D/3D bulk heterojunction structure is beneficial to suppress the oxidation of Sn2+ and isolate the moisture and oxygen, resulting in a good stability of the solar cell. Moreover, the quasi‐2D perovskites can passivate defects effectively. The trap density of the perovskite film has decreased by one order of magnitude, thus the carrier lifetime is increased more than twice. These enhanced properties enable us to fabricate a device of 20.5% PCE with great stability.

A novel buffer layer CuAlO2 (CAO) with smooth and compact surface was applied in Cu2ZnSn­(S,Se)4-based (CZTSSe) solar cells to optimize back electrode interface (BEI). It is found that introduction of CAO exerts a remarkable effect on the crystalline quality of absorber and the thickness of interfacial layer Mo­(S,Se)2 (MSSe) at BEI. When the thickness of CAO buffer layer was optimized to 10.6 nm, CZTSSe film exhibits preferable crystallinity with larger grains without pin holes. Also, MSSe decreases significantly to ∼244 nm, and it is smaller than that (∼463 nm) of the sample without CAO. With this interface optimization, the solar cell with 10.6 nm thick CAO shows the higher shunt resistance, lower reversion saturation current density and smaller series resistance, leading to an increase in short-circuit current density (from 26.91 to 30.66 mA·cm–2) as well as fill factor (from 46.60% to 49.93%) compared to that of the sample without CAO. As a consequence, power conversion efficiency of the corresponding devices increases from 4.12% to 5.10%. The influence mechanism of CAO buffer layer on the photovoltaic properties of CZTSSe solar cell is discussed in detail, and this approach presents a wide range of possibilities for the further development of interface optimization in solar cells.

As a newly emerging approach for surface-enhanced Raman spectroscopy (SERS), pressure-induced SERS (PI-SERS) has been attracting increasing interest for its applications in Raman signal enhancement at extreme conditions. However, how to efficiently realize the PI-SERS enhancement and elucidate the corresponding mechanism remain open questions. Herein, we demonstrate the PI-SERS enhancement up to 8.04 GPa using monolayer molybdenum disulfide (ML-MoS2) as a SERS substrate and three organic molecules with similar energy levels but different symmetries as probes. The combined theory and experiment results show that a pressure-induced increase in the Fermi level of the ML-MoS2 substrate and a decrease in the highest occupied molecular orbital–lowest unoccupied molecular orbital (HOMO–LUMO) energy gap of probe molecules lead to a transition from the multiple resonance-related SERS enhancement to charge transfer (CT)-dominated PI-SERS selective enhancement, depending on the incident laser energy and the pressure applied. Such PI-SERS selective enhancement has been discussed in the framework of CT-induced strengthening of electron–phonon coupling, as well as a possible match of the structural symmetries between probe molecules and the substrate. This study provides deep insights into our understanding of PI-SERS enhancement, and the revealed mechanism can be extended to other molecules for SERS at extreme conditions.

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