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Although significant advancements have been achieved in lead–tin (Pb–Sn) alloyed perovskite solar cells (PSCs), their power conversion efficiency (PCE) remains inferior to that of their Pb-based counterparts, primarily due to higher open-circuit voltage (Voc) losses and lower fill factors (FFs). Herein, we report both perovskite top and bottom interfacial improvements by incorporating a facile fluorophenylethylammonium iodide (p-FPEAI)/ethyl acetate (EA) solution during the film crystal growth. Based on the analysis of perovskite crystallization, film growth, and strain relaxation, the mechanisms behind these interfacial improvements have been well understood. Furthermore, p-FPEAI could reduce the defect density and nonradiative recombination losses, thus attributing to the improved Vocand FF. Finally, the treated device achieved a PCE of 20.14% with a Vocof up to 0.84 V, which is among the highest reported values so far for Pb–Sn alloyed PSCs without additional precursor additives. In addition, the unencapsulated p-FPEAI-treated device maintained its initial efficiency of approximately 92% after being kept in a nitrogen atmosphere for 1 month, in contrast to the control device which retained only 30% of its initial value. Our findings provide a comprehension for understanding the effect of bulky cations as antisolvents on fabricating highly efficient Pb–Sn alloyed perovskite solar cells.

Extracellular vesicles (EVs) harbor abundant glycans that mediate various functions, such as intercellular communication and disease advancement, which play significant roles in disease progression. However, the presence of EV heterogeneity in body fluids and the complex nature of the glycan structures have posed challenges for the detection of EV glycans. In this study, we provide a streamlined method integrated, membrane-specific separation with lectin-induced aggregation strategy (MESSAGE), for multiplexed profiling of EV glycans. By leveraging a rationally designed lectin-induced aggregation strategy, the expression of EV glycans is converted to size-based signals. With the assistance learning machine algorithms, the MESSAGE strategy with high sensitivity, specificity, and simplicity can be used for early cancer diagnosis and classification, as well as monitoring cancer metastasis via 20 μL plasma sample within 2 h. Furthermore, our platform holds promise for advancing the field of EV-based liquid biopsy for clinical applications, opening new possibilities for the profiling of EV glycan signatures in various disease states.

MicroRNAs (miRNAs) in tumor-derived extracellular vesicles (tEVs) are important cancer biomarkers for cancer screening and early diagnosis. Multiplex detection of miRNAs in tEVs facilitates accurate diagnosis but remains a challenge. Herein, we propose an encoded fusion strategy to profile the miRNA signature in tEVs for pancreatic cancer diagnosis. A panel of encoded-targeted-fusion beads was fabricated for the selective recognition and fusion of tEVs, with the turn-on fluorescence signals of molecule beacons for miRNA quantification and barcode signals for miRNA identification using readily accessible flow cytometers. Using this strategy, six types of pancreatic-cancer-associated miRNAs can be profiled in tEVs from 2 μL plasma samples (n= 36) in an isolation-free and lysis-free manner with only 2 h of processing, offering a high accuracy (98%) to discriminate pancreatic cancer, pancreatitis, and healthy donors. This encoded fusion strategy exhibits great potential for multiplex profiling of miRNA in tEVs, offering new avenues for cancer diagnosis and screening.