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Simultaneous passivation of the defects at the surface and grain boundaries of perovskite films is crucial to achieve efficient and stable perovskite solar cells (PSCs). It is highly desirable to accomplish the above passivation through rational engineering of hole transport materials (HTMs) in combination with appropriate procedure optimization. Here, methylthiotriphenylamine-substituted copper phthalocyanine (SMe-TPA-CuPc) is reported as a dopant-free HTM for PSCs, exhibiting excellent efficiency, and stability. After thermal annealing, SMe-TPA-CuPc molecules diffused into the bulk of the perovskite film and effectively passivated the defects in the bulk and at the interface of the perovskite, owing to the strong interaction between the methylthio moiety and undercoordinated lead. The best-performing annealed SMe-TPA-CuPc-based device shows efficiency of 21.51%, which is higher than the unannealed SMe-TPA-CuPc-based device (power-conversion efficiency (PCE) of 20.75%) and reference doped spiro-OMeTAD-based device (PCE of 20.61%). Further modification of the perovskite of the annealed SMe-TPA-CuPc-based device by the QAPyBF4 additive result in even higher efficiency of 23.0%. It also shows excellent stability, maintaining 96% of its initial efficiency after 3624 h aging at 85 °C. This work highlights the great potential of phthalocyanine-based dopant-free HTMs and the defect passivation by thermal-induced molecular diffusion strategy for developing highly efficient and stable PSCs.

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As students’ behaviors are important factors that can reflect their learning styles and living habits on campus, extracting useful features of them plays a helpful role in understanding the students’ learning process, which is an important step towards personalized education. Recently, the task of predicting students’ performance from their campus behaviors has aroused the researchers’ attention. However, existing studies mainly focus on extracting statistical features manually from the pre-stored data, resulting in hysteresis in predicting students’ achievement and finding out their problems. Furthermore, due to the limited representation capability of these manually extracted features, they can only understand the students’ behaviors shallowly. To make the prediction process timely and automatically, we treat the performance prediction task as a short-term sequence prediction problem, and propose a two-stage classification framework, i.e., Sequence-based Performance Classifier (SPC), which consists of a sequence encoder and a classic data mining classifier. More specifically, to deeply discover the sequential features from students’ campus behaviors, we first introduce an attention-based Hybrid Recurrent Neural Network (HRNN) to encode their recent behaviors by giving a higher weight to the ones that are related to the students’ last action. Then, to conduct student performance prediction, we further involve these learned features to the classic Support Vector Machine (SVM) algorithm and finally achieve our SPC model. We conduct extensive experiments in the real-world student card dataset. The experimental results demonstrate the superiority of our proposed method in terms of Accuracy and Recall.

Interfacial nonradiative recombination loss is a huge barrier to advance the photovoltaic performance. Here, one effective interfacial defect and carrier dynamics management strategy by synergistic modulation of functional groups and spatial conformation of ammonium salt molecules is proposed. The surface treatment with 3‐ammonium propionic acid iodide (3‐APAI) does not form 2D perovskite passivation layer while the propylammonium ions and 5‐aminopentanoic acid hydroiodide post‐treatment lead to the formation of 2D perovskite passivation layers. Due to appropriate alkyl chain length, theoretical and experimental results manifest that COOH and NH3+ groups in 3‐APAI molecules can form coordination bonding with undercoordinated Pb2+ and ionic bonding and hydrogen bonding with octahedron PbI64−, respectively, which makes both groups be simultaneously firmly anchored on the surface of perovskite films. This will strengthen defect passivation effect and improve interfacial carrier transport and transfer. The synergistic effect of functional groups and spatial conformation confers 3‐APAI better defect passivation effect than 2D perovskite layers. The 3‐APAI‐modified device based on vacuum flash technology achieves an alluring peak efficiency of 24.72% (certified 23.68%), which is among highly efficient devices fabricated without antisolvents. Furthermore, the encapsulated 3‐APAI‐modified device degrades by less than 4% after 1400 h of continuous one sun illumination.