Reciprocal communication between FAPs and muscle cells via distinct extracellular vesicle miRNAs in muscle regeneration.

Muscle regeneration is a complex process relying on precise teamwork between multiple cell types, including muscle stem cells (MuSCs) and fibroadipogenic progenitors (FAPs). FAPs are also the main source of intramuscular adipose tissue (IMAT). Muscles without FAPs exhibit decreased IMAT infiltration but also deficient muscle regeneration, indicating the importance of FAPs in the repair process. Here, we demonstrate the presence of bidirectional crosstalk between FAPs and MuSCs via their secretion of extracellular vesicles (EVs) containing distinct clusters of miRNAs that is crucial for normal muscle regeneration. Thus, after acute muscle injury, there is activation of FAPs leading to a transient rise in IMAT. These FAPs also release EVs enriched with a selected group of miRNAs, a number of which come from an imprinted region on chromosome 12. The most abundant of these is miR-127-3p, which targets the sphingosine-1-phosphate receptor S1pr3 and activates myogenesis. Indeed, intramuscular injection of EVs from immortalized FAPs speeds regeneration of injured muscle. In late stages of muscle repair, in a feedback loop, MuSCs and their derived myoblasts/myotubes secrete EVs enriched in miR-206-3p and miR-27a/b-3p. The miRNAs repress FAP adipogenesis, allowing full muscle regeneration. Together, the reciprocal communication between FAPs and muscle cells via miRNAs in their secreted EVs plays a critical role in limiting IMAT infiltration while stimulating muscle regeneration, hence providing an important mechanism for skeletal muscle repair and homeostasis.

scMLC: an accurate and robust multiplex community detection method for single-cell multi-omics data.

Clustering cells based on single-cell multi-modal sequencing technologies provides an unprecedented opportunity to create high-resolution cell atlas, reveal cellular critical states and study health and diseases. However, effectively integrating different sequencing data for cell clustering remains a challenging task. Motivated by the successful application of Louvain in scRNA-seq data, we propose a single-cell multi-modal Louvain clustering framework, called scMLC, to tackle this problem. scMLC builds multiplex single- and cross-modal cell-to-cell networks to capture modal-specific and consistent information between modalities and then adopts a robust multiplex community detection method to obtain the reliable cell clusters. In comparison with 15 state-of-the-art clustering methods on seven real datasets simultaneously measuring gene expression and chromatin accessibility, scMLC achieves better accuracy and stability in most datasets. Synthetic results also indicate that the cell-network-based integration strategy of multi-omics data is superior to other strategies in terms of generalization. Moreover, scMLC is flexible and can be extended to single-cell sequencing data with more than two modalities.

IL-27 Gene Therapy Ameliorates IPEX Syndrome Caused by Germline Mutation of Foxp3 Gene: A Major Role for Induction of IL-10.

Inactivating mutations of Foxp3, the master regulator of regulatory T cell development and function, lead to immune dysregulation, polyendocrinopathy, enteropathy, X-linked (IPEX) syndrome in mice and humans. IPEX is a fatal autoimmune disease, with allogeneic stem cell transplant being the only available therapy. In this study, we report that a single dose of adeno-associated virus (AAV)-IL-27 to young mice with naturally occurring Foxp3 mutation (Scurfy mice) substantially ameliorates clinical symptoms, including growth retardation and early fatality. Correspondingly, AAV-IL-27 gene therapy significantly prevented naive T cell activation, as manifested by downregulation of CD62L and upregulation of CD44, and immunopathology typical of IPEX. Because IL-27 is known to induce IL-10, a key effector molecule of regulatory T cells, we evaluated the contribution of IL-10 induction by crossing IL-10-null allele to Scurfy mice. Although IL-10 deficiency does not affect the survival of Scurfy mice, it largely abrogated the therapeutic effect of AAV-IL-27. Our study revealed a major role for IL-10 in AAV-IL-27 gene therapy and demonstrated that IPEX is amenable to gene therapy.

Selection-free precise gene repair using high-capacity adenovector delivery of advanced prime editing systems rescues dystrophin synthesis in DMD muscle cells.

Prime editors have high potential for disease modelling and regenerative medicine efforts including those directed at the muscle-wasting disorder Duchenne muscular dystrophy (DMD). However, the large size and multicomponent nature of prime editing systems pose substantial production and delivery issues. Here, we report that packaging optimized full-length prime editing constructs in adenovector particles (AdVPs) permits installing precise DMD edits in human myogenic cells, namely, myoblasts and mesenchymal stem cells (up to 80% and 64%, respectively). AdVP transductions identified optimized prime-editing reagents capable of correcting DMD reading frames of ∼14% of patient genotypes and restoring dystrophin synthesis and dystrophin-ß-dystroglycan linkages in unselected DMD muscle cell populations. AdVPs were equally suitable for correcting DMD iPSC-derived cardiomyocytes and delivering dual prime editors tailored for DMD repair through targeted exon 51 deletion. Moreover, by exploiting the cell cycle-independent AdVP transduction process, we report that 2- and 3-component prime-editing modalities are both most active in cycling than in post-mitotic cells. Finally, we establish that combining AdVP transduction with seamless prime editing allows for stacking chromosomal edits through successive delivery rounds. In conclusion, AdVPs permit versatile investigation of advanced prime editing systems independently of their size and component numbers, which should facilitate their screening and application.

KinomeMETA: a web platform for kinome-wide polypharmacology profiling with meta-learning.

Kinase-targeted inhibitors hold promise for new therapeutic options, with multi-target inhibitors offering the potential for broader efficacy while minimizing polypharmacology risks. However, comprehensive experimental profiling of kinome-wide activity is expensive, and existing computational approaches often lack scalability or accuracy for understudied kinases. We introduce KinomeMETA, an artificial intelligence (AI)-powered web platform that significantly expands the predictive range with scalability for predicting the polypharmacological effects of small molecules across the kinome. By leveraging a novel meta-learning algorithm, KinomeMETA efficiently utilizes sparse activity data, enabling rapid generalization to new kinase tasks even with limited information. This significantly expands the repertoire of accurately predictable kinases to 661 wild-type and clinically-relevant mutant kinases, far exceeding existing methods. Additionally, KinomeMETA empowers users to customize models with their proprietary data for specific research needs. Case studies demonstrate its ability to discover new active compounds by quickly adapting to small dataset. Overall, KinomeMETA offers enhanced kinome virtual profiling capabilities and is positioned as a powerful tool for developing new kinase inhibitors and advancing kinase research. The KinomeMETA server is freely accessible without registration at https://kinomemeta.alphama.com.cn/.

Astrocytes Modulate a Specific Paraventricular Thalamus→Prefrontal Cortex Projection to Enhance Consciousness Recovery from Anesthesia.

Current anesthetic theory is mostly based on neurons and/or neuronal circuits. A role for astrocytes also has been shown in promoting recovery from volatile anesthesia, while the exact modulatory mechanism and/or the molecular target in astrocytes is still unknown. In this study by animal models in male mice and electrophysiological recordings in vivo and in vitro, we found that activating astrocytes of the paraventricular thalamus (PVT) and/or knocking down PVT astrocytic Kir4.1 promoted the consciousness recovery from sevoflurane anesthesia. Single-cell RNA sequencing of the PVT reveals two distinct cellular subtypes of glutamatergic neurons: PVT GRM and PVT ChAT neurons. Patch-clamp recording results proved astrocytic Kir4.1-mediated modulation of sevoflurane on the PVT mainly worked on PVT ChAT neurons, which projected mainly to the mPFC. In summary, our findings support the novel conception that there is a specific PVT→prefrontal cortex projection involved in consciousness recovery from sevoflurane anesthesia, which is mediated by the inhibition of sevoflurane on PVT astrocytic Kir4.1 conductance.

CAKE: a flexible self-supervised framework for enhancing cell visualization, clustering and rare cell identification.

Single cell sequencing technology has provided unprecedented opportunities for comprehensively deciphering cell heterogeneity. Nevertheless, the high dimensionality and intricate nature of cell heterogeneity have presented substantial challenges to computational methods. Numerous novel clustering methods have been proposed to address this issue. However, none of these methods achieve the consistently better performance under different biological scenarios. In this study, we developed CAKE, a novel and scalable self-supervised clustering method, which consists of a contrastive learning model with a mixture neighborhood augmentation for cell representation learning, and a self-Knowledge Distiller model for the refinement of clustering results. These designs provide more condensed and cluster-friendly cell representations and improve the clustering performance in term of accuracy and robustness. Furthermore, in addition to accurately identifying the major type cells, CAKE could also find more biologically meaningful cell subgroups and rare cell types. The comprehensive experiments on real single-cell RNA sequencing datasets demonstrated the superiority of CAKE in visualization and clustering over other comparison methods, and indicated its extensive application in the field of cell heterogeneity analysis. Contact: Ruiqing Zheng. (rqzheng@csu.edu.cn).

KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling.

Kinase inhibitors are crucial in cancer treatment, but drug resistance and side effects hinder the development of effective drugs. To address these challenges, it is essential to analyze the polypharmacology of kinase inhibitor and identify compound with high selectivity profile. This study presents KinomeMETA, a framework for profiling the activity of small molecule kinase inhibitors across a panel of 661 kinases. By training a meta-learner based on a graph neural network and fine-tuning it to create kinase-specific learners, KinomeMETA outperforms benchmark multi-task models and other kinase profiling models. It provides higher accuracy for understudied kinases with limited known data and broader coverage of kinase types, including important mutant kinases. Case studies on the discovery of new scaffold inhibitors for membrane-associated tyrosine- and threonine-specific cdc2-inhibitory kinase and selective inhibitors for fibroblast growth factor receptors demonstrate the role of KinomeMETA in virtual screening and kinome-wide activity profiling. Overall, KinomeMETA has the potential to accelerate kinase drug discovery by more effectively exploring the kinase polypharmacology landscape.

Pan-cancer analysis of the tumorigenic role of Fanconi anemia complementation group D2 (FANCD2) in human tumors.

Monoubiquitination of FANCD2 is a central step in the activation of the Fanconi anemia (FA) pathway after DNA damage. Defects in the FA pathway centered around FANCD2 not only lead to genomic instability but also induce tumorigenesis. At present, few studies have investigated FANCD2 in tumors, and no pan-cancer research on FANCD2 has been conducted. We conducted a comprehensive analysis of the role of FANCD2 in cancer using public databases and other published studies. Moreover, we evaluated the role of FANCD2 in the proliferation, migration and invasion of lung adenocarcinoma cells through in vitro and in vivo experiments, and explored the role of FANCD2 in cisplatin chemoresistance. We investigated the regulatory effect of FANCD2 on the cell cycle of lung adenocarcinoma cells by flow cytometry, and verified this effect by western blotting. FANCD2 expression is elevated in most TCGA tumors and shows a strong positive correlation with poor prognosis in tumor patients. In addition, FANCD2 expression shows strong correlations with immune infiltration, immune checkpoints, the tumor mutation burden (TMB), and microsatellite instability (MSI), which are immune-related features, suggesting that it may be a potential target of tumor immunotherapy. We further found that FANCD2 significantly promotes the proliferation, invasion, and migration abilities of lung adenocarcinoma cells and that its ability to promote cancer cell proliferation may be achieved by modulating the cell cycle. The findings indicate that FANCD2 is a potential biomarker and therapeutic target in cancer treatment by analyzing the oncogenic role of FANCD2 in different tumors.

Nonlinear Photon Generations from Subwavelength Whispering-Gallery-Mode Resonator.

The strong light localization and long photon lifetimes in whispering gallery mode (WGM) microresonators, benefiting from a high-quality (Q) factor and a small mode volume (V), could significantly enhance light-matter interactions, enabling efficient nonlinear photon generation and paving the way for exploring novel on-chip optical functionalities. However, the leakage of energy from bending losses severely limits the improvement of the Q factor for subwavelength WGM microresonators. Here, we demonstrated an integrated self-suspended WGM microresonator that combines external rings and bridges with a microdisk on a platform of silicon on insulator, achieving about one-hundred-fold enhancement in the Q factor and an ultrasmall mode volume of 2.67(/λnSi)3 as predicted by numerical simulations. We experimentally confirmed the improved performance of the subwavelength WGM resonator with the dramatic enhancement of third-harmonic generation and second-harmonic generation on this device. Our work is anticipated to enhance light-matter interactions on small-footprint microresonators and boost the development of efficient integrated nonlinear and quantum photonics.

Room-Temperature Strong Coupling of Few-Exciton in a Monolayer WS2 with Plasmon and Dispersion Deviation.

Engineering room-temperature strong coupling of few-exciton in transition-metal dichalcogenides (TMDCs) with plasmons promises to construct compact and high-performance quantum optical devices. But it remains unimplemented due to their in-plane excitons. Here, we demonstrate the strong coupling of few-exciton within 10 in monolayer WS2 with the plasmonic mode with a large tangential component of the electric field tightly trapped around the sharp corners of an Au@Ag nanocuboid, the fewest number of excitons observed in the TMDC family so far. Furthermore, we for the first time report a significant deviation with a relative difference of up to 100.6% between the spectrum and eigenlevel splitting dispersions, which increases with decreasing coupling strength. It is also shown that the coupling strength obtained by the conventional concept of both being equal to the measured spectrum splitting is markedly overestimated. Our work enriches the understanding of strong light-matter interactions at room temperature.

A Fully Metaoptical Zoom Lens with a Wide Range.

Metalenses are typically designed for a fixed focal length, restricting their functionality to static scenarios. Various methods have been introduced to achieve the zoom function in metalenses. These methods, however, have a very limited zoom range, or they require additional lenses to achieve direct imaging. Here, we demonstrate a zoom metalens based on axial movement that performs both the imaging and the zoom function. The key innovation is the use of a polynomial phase profile that mimics an aspheric lens, which allows an extended depth of focus, enabling a large zoom range. Experimental results show that this focal length variation, combined with the extended depth of focus, translates into an impressive zoom range of 11.9× while maintaining good imaging quality. We see applications for such a zoom metalens in surveillance cameras of drones or microrobots to reduce their weight and volume, thus enabling more flexible application scenarios.

Structure Sensitivity of Metal Catalysts Revealed by Interpretable Machine Learning and First-Principles Calculations.

The nature of the active sites and their structure sensitivity are the keys to rational design of efficient catalysts but have been debated for almost one century in heterogeneous catalysis. Though the Brønsted-Evans-Polanyi (BEP) relationship along with linear scaling relation has long been used to study the reactivity, explicit geometry, and composition properties are absent in this relationship, a fact that prevents its exploration in structure sensitivity of supported catalysts. In this work, based on interpretable multitask symbolic regression and a comprehensive first-principles data set, we discovered a structure descriptor, the topological under-coordinated number mediated by number of valence electrons and the lattice constant, to successfully address the structure sensitivity of metal catalysts. The database used for training, testing, and transferability investigation includes bond-breaking barriers of 20 distinct chemical bonds over 10 transition metals, two metal crystallographic phases, and 17 different facets. The resulting 2D descriptor composing the structure term and the reaction energy term shows great accuracy to predict the reaction barriers and generalizability over the data set with diverse chemical bonds in symmetry, bond order, and steric hindrance. The theory is physical and concise, providing a constructive strategy not only to understand the structure sensitivity but also to decipher the entangled geometric and electronic effects of metal catalysts. The insights revealed are valuable for the rational design of the site-specific metal catalysts.

Alterations of resting-state network dynamics in Alzheimer's disease based on leading eigenvector dynamics analysis.

Alzheimer's disease (AD) is a neurodegenerative disease, and mild cognitive impairment (MCI) is considered a transitional stage between healthy aging and dementia. Early detection of MCI can help slow down the progression of AD. At present, there are few studies exploring the characteristics of abnormal dynamic brain activity in AD. This article uses a method called Leading Eigenvector Dynamics Analysis (LEiDA) to study resting-state functional magnetic resonance imaging (rs-fMRI) data of AD, MCI, and cognitively normal (CN) participants. By identifying repetitive states of phase coherence, inter group differences in brain dynamic activity indicators are examined. And the neurobehavioral scales were used to assess the relationship between abnormal dynamic activities and cognitive function. The results showed that in the indicators of occurrence probability and lifetime, the globally synchronized state of the patient group decreased. The activity state of the limbic regions significantly detected the difference between AD and the other two groups. Compared to CN, AD and MCI have varying degrees of increase in default and visual regions activity states. In addition, in the analysis related to the cognitive scales, it was found that individuals with poorer cognitive abilities were less active in the globally synchronized state, and more active in limbic regions activity state and visual regions activity state. Taken together, these findings reveal abnormal dynamic activity of resting-state networks in patients with AD and MCI, provide new insights into the dynamic analysis of brain networks, and contribute to a deeper understanding of abnormal spatial dynamic patterns in AD patients.

Test-retest reliability of the attention network test from the perspective of intrinsic network organization.

The attention network test (ANT), developed based on the triple-network taxonomy by Posner and colleagues, has been widely used to examine the efficacy of alerting, orienting and executive control in clinical and developmental neuroscience studies. Recent research suggests the imperfect reliability of the behavioural ANT and its variants. However, the classical ANT fMRI task's test-retest reliability has received little attention. Moreover, it remains ambiguous whether the attention-related intrinsic network components, especially the dorsal attention, ventral attention and frontoparietal network, manifest acceptable reliability. The present study approaches these issues by utilizing an openly available ANT fMRI dataset for participants with Parkinson's disease and healthy elderly. The reproducibility of group-level activations across sessions and participant groups and the test-retest reliability at the individual level were examined at the voxel, region and network levels. The intrinsic network was defined using the Yeo-Schaefer atlas. Our results reveal three critical facets: (1) the overlapping of the group-level contrast map between sessions and between participant groups was unsatisfactory; (2) the reliability of alerting, orienting and executive, defined as a contrast between conditions, was worse than estimates of specific conditions. (3) Dorsal attention, ventral attention, visual and somatomotor networks showed acceptable reliability for the congruent and incongruent conditions. Our results suggest that specific condition estimates might be used instead of the contrast map for individual or group-difference studies.

A multimodal atlas of hepatocellular carcinoma reveals convergent evolutionary paths and 'bad apple' effect on clinical trajectory.

BACKGROUND & AIMS: Hepatocellular carcinoma (HCC) is a highly fatal cancer characterized by high intra-tumor heterogeneity (ITH). A panoramic understanding of its tumor evolution, in relation to its clinical trajectory, may provide novel prognostic and treatment strategies. METHODS: Through the Asia-Pacific Hepatocellular Carcinoma trials group (NCT03267641), we recruited one of the largest prospective cohorts of patients with HCC, with over 600 whole genome and transcriptome samples from 123 treatment-naïve patients. RESULTS: Using a multi-region sampling approach, we revealed seven convergent genetic evolutionary paths governed by the early driver mutations, late copy number variations and viral integrations, which stratify patient clinical trajectories after surgical resection. Furthermore, such evolutionary paths shaped the molecular profiles, leading to distinct transcriptomic subtypes. Most significantly, although we found the coexistence of multiple transcriptomic subtypes within certain tumors, patient prognosis was best predicted by the most aggressive cell fraction of the tumor, rather than by overall degree of transcriptomic ITH level - a phenomenon we termed the 'bad apple' effect. Finally, we found that characteristics throughout early and late tumor evolution provide significant and complementary prognostic power in predicting patient survival. CONCLUSIONS: Taken together, our study generated a comprehensive landscape of evolutionary history for HCC and provides a rich multi-omics resource for understanding tumor heterogeneity and clinical trajectories. IMPACT AND IMPLICATIONS: This prospective study, utilizing comprehensive multi-sector, multi-omics sequencing and clinical data from surgically resected hepatocellular carcinoma (HCC), reveals critical insights into the role of tumor evolution and intra-tumor heterogeneity (ITH) in determining the prognosis of HCC. These findings are invaluable for oncology researchers and clinicians, as they underscore the influence of distinct evolutionary paths and the 'bad apple' effect, where the most aggressive tumor fraction dictates disease progression. These insights not only enhance prognostic accuracy post-surgical resection but also pave the way for personalized treatment strategies tailored to specific tumor evolutionary and transcriptomic profiles. The coexistence of multiple subtypes within the same tumor prompts a re-appraisal of the utilities of depending on single samples to represent the entire tumor and suggests the need for clinical molecular imaging. This research thus marks a significant step forward in the clinical understanding and management of HCC, underscoring the importance of integrating tumor evolutionary dynamics and multi-omics biomarkers into therapeutic decision-making. CLINICAL TRIAL NUMBER: NCT03267641 (Observational cohort).

An open-label, prospective trial to evaluate the efficacy and safety of ixazomib in combination with cyclophosphamide and dexamethasone in patients with newly diagnosed POEMS syndrome.

This open-label, prospective trial evaluated the combination of ixazomib, cyclophosphamide and dexamethasone (ICD) in 12 newly diagnosed POEMS syndrome patients. The study is registered with the Chinese Clinical Trials Registry (ChiCTR2000030072). The treatment protocol consisted of 12 cycles of the ICD regimen compromising ixazomib (4 mg on Days 1, 8 and 15), oral cyclophosphamide (300 mg on Days 1, 8 and 15) and dexamethasone (20 mg weekly). A total of 12 patients received a median of 10 (range: 3-23) cycles of the ICD regimen. The haematological response could be evaluated in 10 patients. The overall haematological response rate was 80% (8/10), with 30% (3/10) achieving complete haematological response, and the overall serum VEGF response rate and neurological response were 100% and 83.3% respectively. Two patients experienced grade 3/4 AEs, including diarrhoea (n = 1) and leukopenia (n = 1). The combination of ixazomib, cyclophosphamide and dexamethasone demonstrated both efficacy and safety in newly diagnosed POEMS syndrome, making it a viable treatment option.

Trace Amount of Bi-Doped Core-Shell Pd@Pt Mesoporous Nanospheres with Specifically Enhanced Peroxidase-Like Activity Enable Sensitive and Accurate Detection of Acetylcholinesterase and Organophosphorus Nerve Agents.

The urgent need for sensitive and accurate assays to monitor acetylcholinesterase (AChE) activity and organophosphorus pesticides (OPs) arises from the imperative to safeguard human health and protect the ecosystem. Due to its cost-effectiveness, ease of operation, and rapid response, nanozyme-based colorimetry has been widely utilized in the determination of AChE activity and OPs. However, the rational design of nanozymes with high activity and specificity remains a great challenge. Herein, trace amount of Bi-doped core-shell Pd@Pt mesoporous nanospheres (Pd@PtBi2) have been successfully synthesized, exhibiting good peroxidase-like activity and specificity. With the incorporation of trace bismuth, there is a more than 4-fold enhancement in the peroxidase-like performance of Pd@PtBi2 compared to that of Pd@Pt. Besides, no significant improvement of oxidase-like and catalase-like activities of Pd@PtBi2 was found, which prevents interference from O2 and undesirable consumption of substrate H2O2. Based on the blocking impact of thiocholine, a colorimetric detection platform utilizing Pd@PtBi2 was constructed to monitor AChE activity with sensitivity and selectivity. Given the inhibition of OPs on AChE activity, a biosensor was further developed by integrating Pd@PtBi2 with AChE to detect OPs, capitalizing on the cascade amplification strategy. The OP biosensor achieved a detection limit as low as 0.06 ng mL-1, exhibiting high sensitivity and anti-interference ability. This work is promising for the construction of nanozymes with high activity and specificity, as well as the development of nanozyme-based colorimetric biosensors.

Chiral Assembly of Perovskite Nanocrystals: Sensitive Discrimination of Amino Acid Enantiomers.

Chirality is a widespread phenomenon in nature and in living organisms and plays an important role in living systems. The sensitive discrimination of chiral molecular enantiomers remains a challenge in the fields of chemistry and biology. Establishing a simple, fast, and efficient strategy to discriminate the spatial configuration of chiral molecular enantiomers is of great significance. Chiral perovskite nanocrystals (PNCs) have attracted much attention because of their excellent optical activity. However, it is a challenge to prepare perovskites with both chiral and fluorescence properties for chiral sensing. In this work, we synthesized two chiral fluorescent perovskite nanocrystal assembly (PNA) enantiomers by using l- or d-phenylalanine (Phe) as chiral ligands. PNA exhibited good fluorescence recognition for l- and d-proline (Pro). Homochiral interaction led to fluorescence enhancement, while heterochiral interaction led to fluorescence quenching, and there is a good linear relationship between the fluorescence changing rate and l- or d-Pro concentration. Mechanism studies show that homochiral interaction-induced fluorescence enhancement is attributed to the disassembly of chiral PNA, while no disassembly of chiral PNA was found in heterochiral interaction-induced fluorescence quenching, which is attributed to the substitution of Phe on the surface of chiral PNA by heterochiral Pro. This work suggests that chiral perovskite can be used for chiral fluorescence sensing; it will inspire the development of chiral nanomaterials and chiral optical sensors.

Hydrogen-Bonded Organic Cocrystal-Encapsulated Perovskite Nanocrystals as Coreactant-Free Electrochemiluminescent Luminophore for the Detection of Uranium.

Lead halide perovskite nanocrystals with excellent photophysical properties are promising electrochemiluminescence (ECL) candidates, but their poor stability greatly restricts ECL applications. Herein, hydrogen-bonded cocrystal-encapsulated CsPbBr3 perovskite nanocrystals (PeNCs@NHS-M) were synthesized by using PeNCs as nuclei for inducing the crystallization of melamine (M) and N-hydroxysuccinimide (NHS). The as-synthesized composite exhibits multiplicative ECL efficiencies (up to 24-fold that of PeNCs) without exogenous coreactants and with excellent stability in the aqueous phase. The enhanced stability can be attributed to the well-designed heterostructure of the PeNCs@NHS-M composite, which benefits from both moiety passivation and protection of the peripheral cocrystal matrix. Moreover, the heterostructure with covalent linkage facilitates charge transfer between PeNCs and NHS-M cocrystals, realizing effective ECL emission. Meanwhile, the NHS and M components act as coreactants for PeNCs, shortening the electron-transport distance and resulting in a significant increase in the ECL signal. Furthermore, by taking advantage of the specific binding effect between NHS-M and uranyl (UO22+), an ECL system with both a low detection limit (1 nM) and high selectivity for monitoring UO22+ in mining wastewater is established. The presence of UO22+ disrupted the charge-transfer effect within PeNCs@NHS-M, weakening the ECL signals. This work provides an efficient design strategy for obtaining stable and efficient ECLs from perovskite nanocrystals, offering a new perspective for the discovery and application of perovskite-based ECL systems.