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Hot stamping of high-strength steel plays an increasingly important role as the demand for automotive lightweighting increases. However, the existing hot stamping process faces challenges such as a lack of digital monitoring, limited monitoring scope, unintuitive production data, and untimely analysis of production events. This paper proposes a framework for a hot stamping production monitoring system based on digital twin technology. By exploring key technologies, including finite state machine (FSM) modeling of the production process, collection, and storage of multi-source data, a digital twin monitoring system for the hot stamping line was developed by combining the data with virtual 3D equipment for multi-dimensional information integration. The system can visualize production information from multiple dimensions and reproduce the production process to analyze production events. Finally, the feasibility and effectiveness of the digital twin system were verified through real-world scenarios, providing valuable insights for hot stamping production process monitoring. [ABSTRACT FROM AUTHOR]

The high-residual and bioaccumulation property of organophosphorus pesticides (OPs) creates enormous risks towards the ecological environment and human health, promoting the research for smart adsorbents and detection methods. Herein, 2D hemin-bridged MOF nanozyme (2D-ZHM) was fabricated and applied to the efficient removal and ultrasensitive dual-mode aptasensing of OPs. On the one hand, the prepared 2D-ZHM contained Zr-OH groups with high affinity for phosphate groups, endowing it with selective recognition and high adsorption capacity for OPs (285.7 mg g−1 for glyphosate). On the other hand, the enhanced peroxidase-mimicking biocatalytic property of 2D-ZHM allowed rapid H2O2-directed transformation of 3,3′,5,5′-tetramethylbenzidine to oxidic product, producing detectable colorimetric or photothermal signals. Using aptamers of specific recognition capacity, the rapid quantification of two typical OPs, glyphosate and omethoate, was realized with remarkable sensitivity and selectivity. The limit of detections (LODs) of glyphosate were 0.004 nM and 0.02 nM for colorimetric and photothermal methods, respectively, and the LODs of omethoate were 0.005 nM and 0.04 nM for colorimetric and photothermal methods, respectively. The constructed dual-mode aptasensing platform exhibited outstanding performance for monitoring OPs in water and fruit samples. This work provides a novel pathway to develop MOF-based artificial peroxidase and integrated platform for pollutant removal and multi-mode aptasensing. [ABSTRACT FROM AUTHOR]

Survival and prognosis of patients with acute myocardial infarction (AMI) are highly dependent on rapid and accurate diagnosis of myocardial damage. Troponin T is the primary diagnostic biomarker and is widely used in clinical practice. Amplified luminescent proximity homogeneous assay (AlphaLISA) may provide a solution to rapidly detect a small amount of analyte through molecular interactions between special luminescent donor beads and acceptor bead. Here, a double-antibody sandwich assay was introduced into AlphaLISA for rapid detection for early diagnosis of AMI and disease staging evaluation. The performance of the assay was evaluated. The study found that the cTnT assay has a linear range of 48.66 to 20,000 ng/L with a limit of detection of 48.66 ng/L. In addition, the assay showed no cross-reactivity with other classic biomarkers of myocardial infarction and was highly reproducible with intra- and inter-batch coefficients of variation of less than 10%, notably, only 3 min was taken, which is particularly suitable for clinical diagnosis. These results suggest that our method can be conveniently applied in the clinic to determine the severity of the patient’s condition. [ABSTRACT FROM AUTHOR]

Context: 2,4,6,8,10, 12-hexanitro-2,4,6,8,10, 12-hexazepane (CL-20) is a new energetic material with high performance and low sensitivity. In-depth study of the thermal decomposition mechanism of CL-20 is a necessary condition to improve its performance, ensure its safety, and optimize its application. On the basis of a large number of empirical force fields used in molecular dynamics simulation in the past, the machine learning augmented first-principles molecular dynamics method was used for the first time to simulate the thermal decomposition reaction of CL-20 at 2200 K, 2500 K, 2800 K, and 3000 K isothermal temperature. The main stable resulting compounds are N2, CO2, CO, H2O, andH2, where CO2 and H2O continue to decompose at higher temperatures. The initial decomposition pathways are denitration by N–N fracture, ring-opening by C–N bond fracture, and redox reaction involving NO2 and CL-20. After ring opening, two main compounds, fused tricyclic pyrazine and azadicyclic, were formed, which were decomposed continuously to form monocyclic pyrazine and pyrazole ring structures. The most common fragments formed during decomposition are those containing two, three, four, and six carbons. The formation rule and quantity of main small molecule intermediates and resulting stable products under different simulated temperatures were analyzed. Methods: Based on ab initio Bayesian active learning algorithm, efficient and accurate prediction of CL-20 is made using the dynamic machine learning function of Vienna Ab-Initio Simulation Package (VASP), which constructs the energy potential surface by learning a large number of data based on AIMD calculations. The result is a machine learning force field (MLFF). Then the molecular dynamics of CL-20 was simulated using the trained MLFF model. PAW pseudopotentials and generalized gradient approximation (GGA), namely, Perdew-Burke-Ernzerhof (PBE) functional, are used in the calculation. The plane wave truncation energy (ENCUT) is set to 550 eV, and using the Gaussian broadening, the thermal broadening size of the single-electron orbital is 0.05 eV. A van der Waals revision of the system with Grimme Version 3. The energy convergence accuracy (EDIFF) of electron self-consistent iteration is set to 1E-5 eV and 1E-6 eV, respectively. The two-step structure optimization is carried out using 1′1′1 k point grid and conjugate gradient method. The ENCUT was changed to 500 eV and EDIFF to 1E-5 eV, and NVT integration (ISIF = 2) of Langevin thermostat was used for machine learning force field training and AIMD simulation of the system. [ABSTRACT FROM AUTHOR]