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Ischemic stroke is a highly prevalent cause of mortality and morbidity. The only FDA-approved treatment is thrombolysis using tissue plasminogen activator (tPA), which possesses a short effective time window. Real-time recanalization monitoring is crucial to establish successful reperfusion and serves as a strong predictor of clinical outcomes. First-line clinical imaging techniques, such as computed tomography and magnetic resonance imaging, are limited in their soft-tissue resolution, scanning speed, and accessibility. Transcranial doppler ultrasound, the current gold standard for recanalization monitoring, is a blind technique and highly operator dependent. Herein, a photothermal-activatable liposome carrying tPA for photoacoustic (PA) image-guided therapy for real-time and precise recanalization monitoring of ischemic stroke is designed. The liposome containing an organic molecule with propeller structures exhibits excellent photothermal properties and significantly amplified PA signals as compared with the widely used polymer nanoparticles. Near-infrared (NIR) laser triggers rapid release of tPA for quick blood clot lysis in vitro. Administration of the liposome in a rat photothrombotic ischemia model facilitates high-resolution PA imaging for precise real-time recanalization assessment. Precisely controlled tPA release at the ischemic region is achieved by PA image-guided NIR laser irradiation, which successfully dissolves the blood clot and restores perfusion, making this design promising for translational applications.

Artificial intelligence (AI) based self-learning or self-improving material discovery system will enable next-generation material discovery. Herein, we demonstrate how to combine accurate prediction of material performance via first-principle calculations and Bayesian optimization-based active learning to realize a self-improving discovery system for high-performance photosensitizers (PSs). Through self-improving cycles, such a system can improve the model prediction accuracy (best mean absolute error of 0.090 eV for singlet–triplet spitting) and high-performance PS search ability, realizing efficient discovery of PSs. From a molecular space with more than 7 million molecules, 5357 potential high-performance PSs were discovered. Four PSs were further synthesized to show performance comparable with or superior to commercial ones. This work highlights the potential of active learning in first-principle-based materials design, and the discovered structures could boost the development of photosensitization related applications.

For practical applications, molecules often exist in an aggregate state. Therefore, it is of great value if one can predict the performance of molecules when forming aggregates, for example, aggregation-induced emission (AIE) or aggregation-caused quenching (ACQ). Herein, a database containing AIE/ACQ molecules reported in the literature is first established. Through training, these machine learning (ML) models can build up the structure-property relationship and thus implement fast prediction of AIE/ACQ properties. To this end, a multi-modal approach is proposed, multiple prediction methods are compared and designed, and thus an ensemble strategy is developed. First, multiple molecular descriptors are considered at the same time, major features are extracted by dimensionality reduction, and multi-modal features are synthesized. Then, several state-of-the-art methods are designed and compared to analyze the advantages of the different methods. Finally, the ensemble strategy combines the advantages of the multiple methods to obtain the final prediction result. The reliability of this approach in an unknown molecular space is further verified by three newly designed molecules. Reasonable consistency between model predictions and experimental outcomes is obtained. The result indicates that ML can be a powerful tool to predict molecular properties in the aggregated state, thus accelerating the development of solid-state optical materials.

The electrochemical oxidation of DNA mediated by [Ru(bpy)2tatp]2+ (bpy = 2,2′-bipyridine, tatp = 1,4,8,9-tetra-aza-triphenylene) upon incorporation of [Co(phen)3]3+ (phen = 1,10-phenanthroline) has been investigated. The results from emission spectra and fluorescence microscope images suggest the formation of an aggregate containing [Ru(bpy)2tatp]2+, DNA and [Co(phen)3]3+ based on the strong interactions. The evidences from repetitive differential pulse voltammograms and cyclic voltammogams reveal that the presence of [Co(phen)3]3+ can promote the oxidation of DNA mediated by [Ru(bpy)2tatp]2+ on an indium tin oxide (ITO) electrode. The oxidative response of DNA shows a cumulative enhancement with increasing the voltammetric sweeping number, and a stable linear response to DNA is found between 0.001 mM and 0.1 mM. Furthermore, the oxidative mechanism of DNA mediated by [Ru(bpy)2tatp]2+ upon incorporation of [Co(phen)3]3+ becomes evident, depending on the concentration of DNA and mediators, the structure of mediators and scan rate. The results from this study provide a significant basis for better understanding the oxidative damage of DNA mediated by DNA-binding agents.

Infections caused by Gram-positive bacteria, especially those able to invade and survive in host cells, threaten human health severely. It is therefore highly desirable to develop therapeutics that can selectively target and kill intracellular Gram-positive pathogens with minimal toxicity to host cells. Herein, it is described that the aggregation-induced emission luminogen (AIEgen) TPEPy-Et, containing a positively charged pyridinium group and a hydrophobic tetraphenylethylene fragment, is effective for Gram-positive bacteria detection and elimination. The fluorescence of TPEPy-Et is greatly enhanced after incubation with Gram-positive bacteria, which can be used to detect and trace the bacteria in cells. TPEPy-Et also shows excellent killing effects against both extracellular and intracellular Gram-positive bacteria through a membrane depolarization mechanism. The luminescent antibacterial agent TPEPy-Et is thus promising for diagnosis and therapy against intracellular bacterial infection.

As a noninvasive treatment option, photodynamic therapy (PDT) offers light-controlled spatiotemporal precision. However, the current clinical practice requires the patients to avoid light exposure for days to weeks post PDT treatment due to the retention of photosensitizers (PSs) in the living body. In addition, traditional PSs suffer from quenched fluorescence and reduced 1O2 production in nanoparticles (NPs) or the aggregate state. Herein, an efficient metabolizable type-II photosensitizer FBD with aggregation-induced emission characteristics is developed and processed into NPs, which demonstrates good anti-tumor PDT efficiency in mice models. Importantly, FBD can be gradually oxidized after PDT by endogenous hypochlorite (ClO–) in the living body and subsequently excreted through urine within 24 h after PS administration. The designed metabolizable therapeutic agent addresses the key limitation of traditional PDT, showing great potential for clinical translation.

Ferroptosis regulates cell death through reactive oxygen species (ROS)-associated lipid peroxide accumulation, which is expected to affect the structure and polarity of lipid droplets (LDs), but with no clear evidence. Herein, we report the first example of an LD/nucleus dual-targeted ratiometric fluorescent probe, CQPP, for monitoring polarity changes in the cellular microenvironment. Due to the donor-acceptor structure of CQPP, it offers ratiometric fluorescence emission and fluorescence lifetime signals that reflect polarity variations. Using nucleus imaging as a reference, CQPP was applied to report the increase in LD polarity and the homogenization of polarity between LDs and cytoplasm in the ferroptosis model. This LD/nucleus dual-targeted fluorescent probe shows the great potential of using fluorescence imaging to study ferroptosis and ferroptosis-related diseases.

Photoluminescence quantum yield (PLQY) is one of the most important characteristics for organic luminescent materials. However, when carbazole prepared from the lab was compared to that from a commercial source, a remarkable change of PLQY was observed to vary from 37.1% to 69.8%. Taking 9-(4-bromobenzyl)-9H-carbazole (CzBBr) as an example of carbazole derivatives, the PLQYs of CzBBr synthesized from different origins of carbazole were varied from 16.0% to 91.1%. Our studies demonstrated that efficient radiative transition pathways were activated for commercial carbazole based luminescence, leading to a large enhancement in PLQY with fluorescence (from 16.0% to 54.4%) and phosphorescence (from <1% to 36.7%) for CzBBr. These results suggest that the origin of carbazole plays a very important role in determining the optical properties of carbazole derivatives, alerting us to carefully revisit carbazole and its derivatives, especially those with record-high efficiency.

Artificial intelligence (AI) based self-learning or self-improving material discovery system is the holy grail of next-generation material discovery and materials science. Herein, we demonstrate how to combine accurate prediction of material performance via quantum chemical calculations and Bayesian optimization-based active learning to realize a self-improving discovery system for high-performance photosensitizers (PS). Through self-improving cycles, such a system can improve the model prediction accuracy (best mean average error of 0.09 eV for singlet-triplet spitting) and high-performance PS search ability, realizing the efficient discovery of PS. From a molecular space with more than 7 million molecules, 5950 potential high-performance PSs were discovered.

Isomerization is an essential chemical process that often evokes dramatic change of chemical, physical, or biological properties. For a long time, isomerization has been known as a transformation that is induced by certain external energy such as light, heat, or mechanical force. Herein, a new isomerization phenomenon is described, which does not require external energy but simply occurs during molecular packing. The proposed isomerization is demonstrated by a series of symmetric donor-acceptor-donor (D-A-D) molecules, the donor of which may adopt two different stereoisomeric forms. Based on the evidence of the asymmetric isomers in crystals, the occurrence of isomerization during molecular packing is proved. Moreover, the unique asymmetric geometry in the solid state favors the restriction of intramolecular motion, resulting in highly efficient organic solids with quantum yields approaching unity.