Efficient 2D MOFs nanozyme combining with magnetic SERS substrate for ultrasensitive detection of Hg2.

Biomimetic inorganic nanoenzyme is a kind of nanomaterial with long-term stability, easy preparation and low cost, which could instead of natural biological enzyme. Metal-organic framework (MOFs) as effectively nanoenzyme was attracted more attention for the adjustability and large specific surface area. This design is based on the catalase-like catalytic activity of 2D metal-organic frameworks (MOFs) and the high sensitivity of surface enhanced Raman spectroscopy (SERS) biosensors to construct a novel SERS biosensor capable of efficiently detecting mercury (Hg2+). In this study, 2D MOFs nanozyme was instead of 3D structure with more effecient catalytic site, which can catalyze o-Phenylenediamine (OPD) to OPDox with the assistance of H2O2. Besides, a magnetic composite nanomaterial Fe3O4@Ag@OPD was prepared as a signal carrier. In the presence of Hg2+, T-Hg2+-T base pairs were used to connect the two materials to realize Raman signal change. Based on this principle, the SERS sensor can realize the sensitive detection of Hg2+, the detection range is 1.0 × 10-12 âˆ¼ 1.0 × 10-2 mol‧L-1, and the detection limit is 1.36 × 10-13 mol‧L-1. This method greatly improves the reliability of SERS sensor for detecting the target, and provides a new idea for detecting metal ions in the environment.

SERS Immunoassay Based on an Enzyme-Catalyzed Cascade Reaction and Metal-Organic Framework/Alkaline Phosphatase for Ultrasensitive Detection of Adenosine Triphosphate.

Herein, an adenosine triphosphate (ATP)-induced enzyme-catalyzed cascade reaction system based on metal-organic framework/alkaline phosphatase (MOF/ALP) nanocomposites was designed to establish a surface-enhanced Raman spectroscopy (SERS) biosensor for use in rapid, sensitive ATP detection. Numerous ALP molecules were first encapsulated using ZIF-90 to temporarily deactivate the enzyme activity, similar to a lock. Au nanostars (AuNSs), as SERS-enhancing substrates, were combined with o-phenylenediamine (OPD) to form AuNSs@OPD, which could significantly improve the Raman signal of OPD. When the target ATP interacted with the MOF/ALP nanocomposites, ATP could act as a key to open the MOF structure, releasing ALP, which should further catalyze the conversion of OPD to oxOPD with the aid of ascorbic acid 2-phosphate. Therefore, with the increasing concentrations of ATP, more ALP was released to catalyze the conversion of OPD, resulting in the reduced intensity of the Raman peak at 1262 cm-1, corresponding to the level of OPD. Based on this principle, the ATP-induced enzyme-catalyzed cascade reaction SERS biosensor enabled the ultrasensitive detection of ATP, with a low detection limit of 0.075 pM. Consequently, this study provides a novel strategy for use in the ultrasensitive, rapid detection of ATP, which displays considerable potential for application in the fields of biomedicine and disease diagnosis.

Performance Prediction of Cement Stabilized Soil Incorporating Solid Waste and Propylene Fiber.

Cement stabilized soil (CSS) yields wide application as a routine cementitious material due to cost-effectiveness. However, the mechanical strength of CSS impedes development. This research assesses the feasible combined enhancement of unconfined compressive strength (UCS) and flexural strength (FS) of construction and demolition (C&D) waste, polypropylene fiber, and sodium sulfate. Moreover, machine learning (ML) techniques including Back Propagation Neural Network (BPNN) and Random Forest (FR) were applied to estimate UCS and FS based on the comprehensive dataset. The laboratory tests were conducted at 7-, 14-, and 28-day curing age, indicating the positive effect of cement, C&D waste, and sodium sulfate. The improvement caused by polypropylene fiber on FS was also evaluated from the 81 experimental results. In addition, the beetle antennae search (BAS) approach and 10-fold cross-validation were employed to automatically tune the hyperparameters, avoiding tedious effort. The consequent correlation coefficients (R) ranged from 0.9295 to 0.9717 for BPNN, and 0.9262 to 0.9877 for RF, respectively, indicating the accuracy and reliability of the prediction. K-Nearest Neighbor (KNN), logistic regression (LR), and multiple linear regression (MLR) were conducted to validate the BPNN and RF algorithms. Furthermore, box and Taylor diagrams proved the BAS-BPNN and BAS-RF as the best-performed model for UCS and FS prediction, respectively. The optimal mixture design was proposed as 30% cement, 20% C&D waste, 4% fiber, and 0.8% sodium sulfate based on the importance score for each variable.

Locating Hazardous Chemical Leakage Source Based on Cooperative Moving and Fixing Sensors.

In dealing with sudden hazardous chemical leakage accidents, the key to solving the evacuation and transfer of personnel and important property is to determine the location of the leakage source and the information of the source strength to gauge the scope of the impact of leakage. The particle swarm optimization algorithm with an adaptive mutation factor is applied to the inverse calculation of leakage source strength to obtain the leakage source information, and the leakage source location problem is transformed into an optimization problem. The mobile sensor is then introduced into the fixed sensor network. The mobile sensor moving strategy based on an extended Kalman filter is proposed. The estimated value of the previous moment and the current time are used to update the estimation of the state variable, and then the mobile strategy is planned. The interference of the random error of the optimization algorithm on the path planning of the mobile sensor is reduced by introducing the optimized result memory and, thus, location efficiency is improved. Simulation results showed that the proposed method, which combines mobile with fixed sensors, greatly expanded the monitoring function of the network, reduced the number of fixed sensors, and enhanced the positioning accuracy.