Amphiphilic Janus Graphene Oxide Acts as a Corrosion Inhibitor to Mitigate the Corrosion Caused by a 1 M HCl Solution on Mild Steel.

Great aqueous dispersibility, a large specific surface area, and high impermeability make graphene oxide (GO) the ideal candidate for a high-performance corrosion inhibitor. Numerous symmetrical modification methods have been reported to enhance the adsorption of GO on metal surfaces in various corrosive media. This work aims to investigate the enhancement and mechanism of unilateral hydrophobic modification on the corrosion inhibition performance of GO. In this study, amphiphilic Janus GO (JGO) was prepared by grafting hydrophobic alkyl chains on one side of GO, and its anticorrosion performance was evaluated via weight loss experiments and electrochemical tests. The results revealed that the corrosion inhibition efficiency for Q235 mild steel (MS) in a 1 M HCl aqueous solution of 25 ppm JGO (81.08%) was much higher than that of GO at the same concentration (22.12%). Furthermore, the Langmuir adsorption isotherm and computational study demonstrated that the synergistic effect of physical adsorption and chemical adsorption promoted the hydrophilic side of JGO close to the surface of the metal, and the dense protective layer was formed by the hydrophobic chains toward the corrosive medium, which effectively hindered the corrosion of MS by the acidic liquid. This study emphasizes the significant role of asymmetrically modified hydrophobic alkyl chains in improving the corrosion prevention performance of GO and provides a perspective for the structural design of GO-based corrosion inhibitors.

Molecular Insights into the Dual Promotion-Inhibition Effects of NaCl at Various Concentrations on the CO2 Hydrate Growth: A Molecular Simulation Study.

Hydrate-based CO2 storage in the ocean is considered a potential method for mitigating the greenhouse effect. Numerous studies demonstrated that NaCl exhibited the dual effects of promotion and inhibition in the nucleation and growth processes of CO2 hydrate, whose mechanisms remain unclear. In this study, the effects of NaCl at various concentrations on the CO2 hydrate growth and crystal are investigated. The independent gradient model based on Hirshfeld partition, electrostatic potential, and binding energy is conducted to study the interaction between ions and water molecules. The motion trajectories of ions are observed at the molecular level to reflect the impact of ion motion on hydrate growth. The results show that the influence of NaCl on hydrate growth depends on a delicate balance of dual promotion-inhibition effects. NaCl can combine more water molecules and provide a transport channel of CO2 to promote hydrate growth at low concentrations. Meanwhile, the promoting effects shift toward inhibition with increasing NaCl concentrations. In a word, this paper proposes a novel mechanism for the dual promotion-inhibition effects of NaCl on hydrate growth, which is significant for further research on hydrate-based CO2 storage in the ocean.

Nanomanipulation of Ligand Nanogeometry Modulates Integrin/Clathrin-Mediated Adhesion and Endocytosis of Stem Cells.

Nanosubstrate engineering can be a biomechanical approach for modulating stem cell differentiation in tissue engineering. However, the study of the effect of clathrin-mediated processes on manipulating this behavior is unexplored. Herein, we develop integrin-binding nanosubstrates with confined nanogeometries that regulate clathrin-mediated adhesion- or endocytosis-active signaling pathways for modulating stem fates. Isotropically presenting ligands on the nanoscale enhances the expression of clathrin in cells, thereby facilitating uptake of dexamethasone-loaded nanoparticles (NPs) to boost osteogenesis of stem cells. In contrast, anisotropic ligand nanogeometry suppresses this clathrin-mediated NP entry by strengthening the association between clathrin and adhesion spots to reinforce mechanotransduced signaling, which can be abrogated by the pharmacological inhibition of clathrin. Meanwhile, inhibiting focal adhesion formation hinders cell spreading and enables a higher endocytosis efficiency. Our findings reveal the crucial roles of clathrin in both endocytosis and mechanotransduction of stem cells and provide the parameter of ligand nanogeometry for the rational design of biomaterials for tissue engineering.

Fast adaptation of multi-task meta-learning for optical performance monitoring.

An algorithm is proposed for few-shot-learning (FSL) jointing modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) estimation. The constellation diagrams of six widely-used modulation formats over a wide range of OSNR (10-40 dB) are obtained by a dual-polarization (DP) coherent detection system at 32 GBaud. We introduce auxiliary task to model-agnostic meta-learning (MAML) which makes the gradient of meta tasks decline faster in the direction of optimal target. Ablation experiments including multi-task model-agnostic meta-learning (MT-MAML), single-task model-agnostic meta-learning (ST-MAML) and adaptive multi-task learning (AMTL) are executed to train a data set with only 20 examples for each class. First, we discuss the impact from the number of shots and gradient descent steps for support set on the meta-learning based schemes to determine the best hyper parameters and conclude that the proposed method better captures the similarity between new and previous knowledge at 4 shot and 1 step. Withdrawn fine-tuning, the model achieves the lowest error ∼0.37 dB initially. Then, we simulate two other schemes (AMTL and ST-MAML), and the numerical results shows that mean square error (MSE) are ∼0.6 dB, ∼0.3 dB and ∼0.18 dB, respectively, proposed method has faster adaption to main task. For low order modulation formats, the proposed method almost reduces the error to 0. Meanwhile, we reveal the degree of deviation between the prediction and target and find that the deviation is mainly concentrated in the high OSNR range of 25-40 dB. Specifically, we investigate the variation curve of adaptive weights during pretraining and conclude that after 30 epoch, the model's attention was almost entirely focused on estimating OSNR. In addition, we study the generalization ability of the model by varying the transmission distance. Importantly, excellent generalization is also experimentally verified. In this paper, the method proposed will greatly reduce the cost for repetitively collecting data and the training resources required for fine-tuning models when OPM devices need to be deployed at massive nodes in dynamic optical networks.

Surface plasmon enhanced THz emission with nanoporous gold supported CdTe.

Terahertz emission by ultrafast excitation of semiconductor/metal interfaces was found strongly enhanced by plasmon resonance. Here, a three-dimensional nanoporous gold (NPG) was used to form semiconductor/metal compound with cadmium telluride (CdTe). We investigated the specific impact of surface plasmon from randomly nanoporous structure in the ultrafast optoelectronic response for THz generation, and observed a THz amplitude enhancement around an order of magnitude from CdTe on NPG compared to that from CdTe on silicon. Moreover, the plasmon enhancement for THz emission from NPG is stronger than that from gold film, indicating that randomly nanoporous structure is also effective for plasmonic enhancement in THz band.

Environmental impact and health risk assessment of volatile organic compound emissions during different seasons in Beijing.

Volatile organic compounds (VOCs) are major contributors to air pollution. Based on the emission characteristics of 99 VOCs that daily measured at 10 am in winter from 15 December 2015 to 17 January 2016 and in summer from 21 July to 25 August 2016 in Beijing, the environmental impact and health risk of VOC were assessed. In the winter polluted days, the secondary organic aerosol formation potential (SOAP) of VOC (199.70 ± 15.05 µg/m3) was significantly higher than that on other days. And aromatics were the primary contributor (98.03%) to the SOAP during the observation period. Additionally, the result of the ozone formation potential (OFP) showed that ethylene contributed the most to OFP in winter (26.00% and 27.64% on the normal and polluted days). In summer, however, acetaldehyde was the primary contributor to OFP (22.00% and 21.61% on the normal and polluted days). Simultaneously, study showed that hazard ratios and lifetime cancer risk values of acrolein, chloroform, benzene, 1,2-dichloroethane, acetaldehyde and 1,3-butadiene exceeded the thresholds established by USEPA, thereby presenting a health risk to the residents. Besides, the ratio of toluene-to-benzene indicated that vehicle exhausts were the main source of VOC pollution in Beijing. The ratio of m-/p-xylene-to-ethylbenzene demonstrated that there were more prominent atmospheric photochemical reactions in summer than that in winter. Finally, according to the potential source contribution function (PSCF) results, compared with local pollution sources, the spread of pollution from long-distance VOCs had a greater impact on Beijing.

Cross-Linked Reverse Vesicle as a General and Effective Vehicle for Hydrophobic Drugs.

It is well-known that vesicles serve as an excellent delivery platform for hydrophilic drugs. However, there is still a lack of a general and effective platform for hydrophobic drug loading. We herein disclose that water-soluble cross-linked reverse vesicles (cRVs) constructed from anionic surfactant 1, a counterpart of normal vesicles, would be excellent vehicles for hydrophobic drugs, the drug loading content (DLC) for which arrived up to 21.1%, 19.8%, and 25.8%, respectively, for three anticancer drugs, paclitaxel, camptothecin, and carmofur. This represents a general drug carrier with high drug loading content for various hydrophobic drugs without the assistance of other external forces. In addition to drug loading superiority, the cRVs were also characterized by robust stability, specific stimulus response, easy postfunctionalization, and good biocompatibility and thus are promising candidates for drug delivery systems.

Covalent capture of supramolecular species in an aqueous solution of water-miscible small organic molecules.

Since an early report in the 1970s, the mesoscale inhomogeneities formed in an aqueous solution of water-miscible small organic molecules have been debated for over forty years with a variety of explanations. Although it was recently established that these inhomogeneities are supramolecular species caused by trace impurities, the structure of the supramolecular species and the mechanism behind their formation are not yet clear. By means of covalent capture, we herein disclose that the formation mechanism of the supramolecular species consists of a two-step self-assembly process: the small molecules first assemble into primary micelles with a trace amount of impurity, and the formed dynamic ultra-small micelles aggregate further through hydrogen bonding to achieve a buildup of thermodynamic mesoscale inhomogeneities. Based on this finding, supramolecular species have been used as elements for pH-responsive size-changeable drug carriers, which respond to the acidic tumour extracellular milieu and decompose into small particles for deep tumour penetration and effective distribution.

Emission characteristics of 99 NMVOCs in different seasonal days and the relationship with air quality parameters in Beijing, China.

In recent years, the haze incidents have occurred frequently in China. Therefore, more attention should be taken in comprehensively determination and analysis of the extended-ranges of volatile organic compounds (VOCs). Here, up to 99 non-methane VOCs (NMVOCs), including not frequently reported partial halocarbons and oxygenated VOC (OVOC) species, were monitored in atmosphere of Beijing. The mean concentration of total NMVOC (TNMVOC) decreased in the order of winter polluted days (216.05 µg m-3) > summer polluted days (127.01 µg m-3) > summer normal days (95.63 µg m-3) > winter normal days (50.25 µg m-3). The ethane to n-butane, ethylene to 1-butene, BTEX, acetaldehyde, acetone, n-hexanal, dichloromethane, chloroform and 1,2-dichloroethane, were determined to be the main composition in their respective alkane, alkene, aromatic, OVOC and halocarbon classes. The minor propylbenzene, diethylbenzene, ethyltoluene, and trimethylbenzene isomer ratios were within the narrow range of 1.3-3.21. Generally, the most abundant NMVOCs were alkanes in winter but OVOCs in summer. TNMVOC significantly positively correlated with PM10, PM2.5, CO, RH, SO2 (winter), NO2 (winter), but negatively with windspeed, SSD and PRS (winter). The opposite correlation was observed between TNMVOC and O3 in winter and summer. There was no meaningful correlation between TNMVOC and T, PRS (summer), SO2 (summer) and NO2 (summer). 3D surface graphs, built by MATLAB, were drawn to investigate the relationship between PM2.5 and NMVOC taking air quality parameters into account. The PM2.5 concentration increased non-linearly as TNMVOC concentration increased, with various surface graphs. Unlike other air quality parameters, O3 affected the relationship differently between winter and summer. The findings presented herein may provide a new train of thought for occurrence of haze.

Efficient Light-Harvesting Systems with Tunable Emission through Controlled Precipitation in Confined Nanospace.

Light harvesting is a key step in photosynthesis but creation of synthetic light-harvesting systems (LHSs) with high efficiencies has been challenging. When donor and acceptor dyes with aggregation-induced emission were trapped within the interior of cross-linked reverse vesicles, LHSs were obtained readily through spontaneous hydrophobically driven aggregation of the dyes in water. Aggregation in the confined nanospace was critical to the energy transfer and the light-harvesting efficiency. The efficiency of the excitation energy transfer (EET) reached 95 % at a donor/acceptor ratio of 100:1 and the energy transfer was clearly visible even at a donor/acceptor ratio of 10 000:1. Multicolor emission was achieved simply by tuning the donor/acceptor feed ratio in the preparation and the quantum yield of white light emission from the system was 0.38, the highest reported for organic materials in water to date.

Visible-Light-Driven Inactivation of Bacteria and H2 Generation Catalyzed by Oxygen-Vacancy-Rich One-Dimensional/Two-Dimensional W18O49/g-C3N4 Z-Scheme Heterostructures.

Z-scheme heterostructure-based photocatalysts consist of a reduction photocatalyst and an oxidation photocatalyst, enabling them to possess a high capacity for both reduction and oxidation. However, the coupling reaction between photocatalytic H2 generation through water reduction and sterilization using Z-scheme systems has been rarely reported. Herein, 1D W18O49 nanowires embedded over 2D g-C3N4 nanosheets are well-constructed as an integrated Z-scheme heterojunction. Experimental results and density functional theory calculations not only demonstrate the achievement of efficient interfacial charge separation and transport, leading to prolonged lifetime of photogenerated charge carriers, but also directly confirm the mechanism of Z-scheme charge transfer. As expected, the optimized W18O49/g-C3N4 nanostructure exhibits superior photocatalytic sterilization activity against Staphylococcus aureus as well as excellent H2 generation performance under visible-light irradiation (λ ≥ 420 nm). Due to its nontoxic nature, W18O49/g-C3N4 holds great potential in eradicating bacterial infections in living organisms.

An Aggregation-Induced Emission-Based Dual Emitting Nanoprobe for Detecting Intracellular pH and Unravelling Metabolic Variations in Differentiating Lymphocytes.

Monitoring T lymphocyte differentiation is essential for understanding T cell fate regulation and advancing adoptive T cell immunotherapy. However, current biomarker analysis methods necessitate cell lysis, leading to source depletion. Intracellular pH (pHi) can be affected by the presence of lactic acid (LA), a metabolic mediator of T cell activity such as glycolysis during T cell activation; therefore, it is a potentially a good biomarker of T cell state. In this work, a dual emitting enhancement-based nanoprobe, namely, AIEgen@F127-AptCD8, was developed to accurately detect the pHi of T cells to "read" the T cell differentiation process. The nanocore of this probe comprises a pair of AIE dyes, TPE-AMC (pH-sensitive moiety) and TPE-TCF, that form a donor-acceptor pair for sensitive detection of pHi by dual emitting enhancement analysis. The nanoprobe exhibits a distinctly sensitive narrow range of pHi values (from 6.0 to 7.4) that can precisely distinguish the differentiated lymphocytes from naïve ones based on their distinct pHi profiles. Activated CD8+ T cells demonstrate lower pHi (6.49 ± 0.09) than the naïve cells (7.26 ± 0.11); Jurkat cells exhibit lower pHi (6.43 ± 0.06) compared to that of nonactivated ones (7.29 ± 0.09) on 7 days post-activation. The glycolytic product profiles in T cells strongly correlate with their pHi profiles, ascertaining the reliability of probing pHi for predicting T cell states. The specificity and dynamic detection capabilities of this nanoprobe make it a promising tool for indirectly and noninvasively monitoring T cell activation and differentiation states.

A biomimetic upconversion nanoreactors for near-infrared driven H2 release to inhibit tauopathy in Alzheimer's disease therapy.

Abnormal hyperphosphorylation of tau protein is a principal pathological hallmark in the onset of neurodegenerative disorders, such as Alzheimer's disease (AD), which can be induced by an excess of reactive oxygen species (ROS). As an antioxidant, hydrogen gas (H2) has the potential to mitigate AD by scavenging highly harmful ROS such as •OH. However, conventional administration methods of H2 face significant challenges in controlling H2 release on demand and fail to achieve effective accumulation at lesion sites. Herein, we report artificial nanoreactors that mimic natural photosynthesis to realize near-infrared (NIR) light-driven photocatalytic H2 evolution in situ. The nanoreactors are constructed by biocompatible crosslinked vesicles (CVs) encapsulating ascorbic acid and two photosensitizers, chlorophyll a (Chla) and indoline dye (Ind). In addition, platinum nanoparticles (Pt NPs) serve as photocatalysts and upconversion nanoparticles (UCNP) act as light-harvesting antennas in the nanoreacting system, and both attach to the surface of CVs. Under NIR irradiation, the nanoreactors release H2 in situ to scavenge local excess ROS and attenuate tau hyperphosphorylation in the AD mice model. Such NIR-triggered nanoreactors provide a proof-of-concept design for the great potential of hydrogen therapy against AD.

Magnetic-responsive upconversion luminescence resonance energy transfer (LRET) biosensor for ultrasensitive detection of SARS-CoV-2 spike protein.

Upconversion nanoparticles (UCNPs) are ideal donors for luminescence resonance energy transfer (LRET)-based biosensors due to their excellent upconversion luminescence properties. However, the relatively large size of antibodies and proteins limits the application of UCNPs-based LRET biosensors in protein detection because the large steric hindrance of proteins leads to low energy transfer efficiency between UCNPs and receptors. Herein, we developed a magnetic responsive UCNPs-based LRET biosensor to control the coupling distance between antibody-functionalized UCNPs (Ab-UCNPs) as donors and antibody-PEG linker-magnetic gold nanoparticles (Ab-PEG-MGNs) as acceptors for ultrasensitive and highly selective detection of SARS-CoV-2 spike proteins. Our results showed that this platform reversibly shortened the coupling distance between UCNPs and MGNs and enhanced the LRET signal with a 10-fold increase in the limit of detection (LOD) from 20.6 pg/mL without magnetic modulation to 2.1 pg/mL with magnetic modulation within 1 h. The finite-difference time-domain (FDTD) simulation with cyclic distance change confirmed the distance-dependent LRET efficiency under magnetic modulation, which supported the experimental results. Moreover, the applications of this magnetic-responsive UCNP-based LRET biosensor could be extended to other large-size biomolecule detection.

Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model.

The combination of rice husk ash and common concrete both reduces carbon dioxide emission and solves the problem of agricultural waste disposal. However, the measurement of the compressive strength of rice husk ash concrete has become a new challenge. This paper proposes a novel hybrid artificial neural network model, optimized using a reptile search algorithm with circle mapping, to predict the compressive strength of RHA concrete. A total of 192 concrete data with 6 input parameters (age, cement, rice husk ash, super plasticizer, aggregate, and water) were utilized to train proposed model and compare its predictive performance with that of five other models. Four statistical indices were adopted to evaluate the predictive performance of all the developed models. The performance evaluation indicates that the proposed hybrid artificial neural network model achieved the most satisfactory prediction accuracy regarding R2 (0.9709), VAF (97.0911%), RMSE (3.4489), and MAE (2.6451). The proposed model also had better predictive accuracy than that of previously developed models on the same data. The sensitivity results show that age is the most important parameter for predicting the compressive strength of RHA concrete.

Metaheuristic Optimization of Random Forest for Predicting Punch Shear Strength of FRP-Reinforced Concrete Beams.

Predicting the punching shear strength (PSS) of fiber-reinforced polymer reinforced concrete (FRP-RC) beams is a critical task in the design and assessment of reinforced concrete structures. This study utilized three meta-heuristic optimization algorithms, namely ant lion optimizer (ALO), moth flame optimizer (MFO), and salp swarm algorithm (SSA), to select the optimal hyperparameters of the random forest (RF) model for predicting the punching shear strength (PSS) of FRP-RC beams. Seven features of FRP-RC beams were considered as inputs parameters, including types of column section (TCS), cross-sectional area of the column (CAC), slab's effective depth (SED), span-depth ratio (SDR), compressive strength of concrete (CSC), yield strength of reinforcement (YSR), and reinforcement ratio (RR). The results indicate that the ALO-RF model with a population size of 100 has the best prediction performance among all models, with MAE of 25.0525, MAPE of 6.5696, R2 of 0.9820, and RMSE of 59.9677 in the training phase, and MAE of 52.5601, MAPE of 15.5083, R2 of 0.941, and RMSE of 101.6494 in the testing phase. The slab's effective depth (SED) has the largest contribution to predicting the PSS, which means that adjusting SED can effectively control the PSS. Furthermore, the hybrid machine learning model optimized by metaheuristic algorithms outperforms traditional models in terms of prediction accuracy and error control.

Application of the Improved POA-RF Model in Predicting the Strength and Energy Absorption Property of a Novel Aseismic Rubber-Concrete Material.

The application of aseismic materials in foundation engineering structures is an inevitable trend and research hotspot of earthquake resistance, especially in tunnel engineering. In this study, the pelican optimization algorithm (POA) is improved using the Latin hypercube sampling (LHS) method and the Chaotic mapping (CM) method to optimize the random forest (RF) model for predicting the aseismic performance of a novel aseismic rubber-concrete material. Seventy uniaxial compression tests and seventy impact tests were conducted to quantify this aseismic material performance, i.e., strength and energy absorption properties and four other artificial intelligence models were generated to compare the predictive performance with the proposed hybrid RF models. The performance evaluation results showed that the LHSPOA-RF model has the best prediction performance among all the models for predicting the strength and energy absorption property of this novel aseismic concrete material in both the training and testing phases (R2: 0.9800 and 0.9108, VAF: 98.0005% and 91.0880%, RMSE: 0.7057 and 1.9128, MAE: 0.4461 and 0.7364; R2: 0.9857 and 0.9065, VAF: 98.5909% and 91.3652%, RMSE: 0.5781 and 1.8814, MAE: 0.4233 and 0.9913). In addition, the sensitive analysis results indicated that the rubber and cement are the most important parameters for predicting the strength and energy absorption properties, respectively. Accordingly, the improved POA-RF model not only is proven as an effective method to predict the strength and energy absorption properties of aseismic materials, but also this hybrid model provides a new idea for assessing other aseismic performances in the field of tunnel engineering.

Source identification in river pollution incidents using a cellular automata model and Bayesian Markov chain Monte Carlo method.

Identification of contaminant sources in rivers is crucial for river protection and emergency response. This study presents an innovative approach for identifying river pollution sources by using Bayesian inference and cellular automata (CA) modeling. A general Bayesian framework is proposed that combines the CA model with observed data to identify unknown sources of river pollution. To reduce the computational burden of the Bayesian inference, a CA contaminant transport model is developed to efficiently simulate pollutant concentration values in the river. These simulated concentration values are then used to calculate the likelihood function of available measurements. The Markov chain Monte Carlo (MCMC) method is used to produce the posterior distribution of contaminant source parameters, which is a sampling-based method that enables the estimation of complex posterior distributions. The suggested methodology is applied to a real case study of the Fen River in Yuncheng City, Shanxi Province, Northern China, and it estimates the release time, release mass, and source location with relative errors below 19%. The research indicates that the proposed methodology is an effective and flexible way to identify the location and concentrations of river contaminant sources.

Defective Bi2S3 Anchored on CuS/C as an Ultrafast and Long-Life Anode for Sodium-Ion Storage.

Due to the high electrical conductivity and abundant redox active sites, bimetal sulfides are highly competitive anode materials for sodium storage with long-life and high-rate. Herein, a heterostructured metal sulfide (Bi2S3-CuS) with a carbon-based support is prepared by calcination and ion exchange methods. The synergistic effects of the heterostructure and defective structure provide facile diffusion channels, fast Na+ migration, and plentiful active sites for Na+, which reflect in the impressive electrochemical performance with a high reversible capacity of 592.2 mA h g-1 after 1000 cycles at 8 A g-1. Furthermore, the Na-ion full batteries exhibit an ultra-long cycling performance with a value of 216 mA h g-1 after 4000 cycles at 10 A g-1. Interestingly, the defective structure of Bi2S3 remains after cycling. Kinetic analyses and density functional theoretical calculations clarified that the heterointerfacial structure, especially on the interface containing sulfur defects in Bi2S3 of Bi2S3-CuS, could induce feasible ion adsorption and promote ion transfer, which lays the foundation for achieving ultrafast sodiation kinetics.

A dual "turn-on" biosensor based on AIE effect and FRET for in situ detection of miR-125b biomarker in early Alzheimer's disease.

MicroRNA-125b (miR-125b) is highly associated with synaptic dysfunction and tau hyperphosphorylation in the early pathogenesis of Alzheimer's disease (AD), making it a promising biomarker for early AD diagnosis. Hence, there is an urgent need for a reliable sensing platform to assist in situ miR-125b detection. In this work, we report a dual "turn-on" fluorescence biosensor based on the nanocomposite of aggregation-induced emission fluorogen (AIEgen)-labeled oligonucleotide (TPET-DNA) probes immobilized on the surface of cationic dextran modified molybdenum disulfide (TPET-DNA@Dex-MoS2). In the presence of the target, TEPT-DNA can hybridize with miR-125b to form a DNA/RNA duplex, causing TPET-DNA to detach from the surface of Dex-MoS2 that simultaneously activates the dual fluorescence enhancement processes: (1) recovery of TPET-DNA signal and (2) strong fluorescent emission from AIEgen triggered by restriction of the intramolecular rotation. The sensing performance of TPET-DNA@Dex-MoS2 was demonstrated by detecting miR-125b in vitro with good sensitivity at the picomolar level and rapid response (≤1 h) without amplification procedures. Furthermore, our nanoprobes exhibited excellent imaging capabilities to aid real-time monitoring of the endogenous miR-125b in PC12 cells and brain tissues of mice AD model induced by local administration of okadaic acid (OA). The fluorescence signals of the nanoprobes indicated miR-125b was spatially associated with phosphorylated tau protein (p-tau) in vitro and in vivo. Therefore, TPET-DNA@Dex-MoS2 could be a promising tool for in situ and real-time monitoring of the AD-related microRNAs and also provide mechanistic insight into the early prognosis of AD.