Great Application Value Research Articles

Biomass N/P co‐doped porous carbon plates for electrocatalytic oxygen reduction

AbstractNitrogen and phosphorus co‐doped porous carbon plate (NPCP) was prepared by simple pyrolysis carbonization and alkali activation of biomass duckweed NPCP displays the largest specific surface area (SSA) of 2023 m2 g−1, and is rich in pyridine N and P doping, which makes the metal‐free carbon have excellent electrocatalytic oxygen reduction performance. The best NPCP2 shows a half‐wave potential of 0.19 V vs. Ag/AgCl in alkaline electrolyte, and its activity is equivalent to that of commercial platinum carbon (Pt/C). In addition, it shows excellent methanol toxicity resistance and continuous catalytic stability. The experimental results show that the heteroatom doping method obtained by biomass hydroponics has great advantages and great application value in the catalytic field of porous carbon materials.

Photocatalytic Hydrosilylation over Pt@UiO-66-NH2: Enhanced Activity and Polymerization Kinetics.

Metal-organic frameworks (MOFs) have shown great research and application value in various types of hydrosilylation reactions. However, studies on photocatalysis-induced hydrosilylation using MOFs are extremely rare. Metal nanoparticles (MNPs)@MOFs are extensively studied for their excellent structural tunability and photocatalytic activity, but there are few reports on their application in photocatalytic hydrosilylation. Here, a novel photocatalyst consisting of platinum (Pt) nanoparticles immobilized in a MOF framework is synthesized and used for photocatalytic hydrosilylation. The effects of various factors on hydrosilylation conversion are investigated, including catalyst concentration, substrate ratio, and irradiation intensity. Furthermore, the photoreactivity of the synthesized Pt catalyst is evaluated in the presence of different concentrations of 2-chlorothixanthone as a photosensitizer. It is noteworthy that the conversion of the reaction increases with increasing catalyst concentration or photosensitizer concentration, whereas increasing the polymethylhydrosiloxane content does not lead to a significant increase in conversion. This study demonstrates the potential of MNPs@MOFs as efficient photocatalysts for photoinduced hydrosilylation reactions and paves the way for future applications in this area.

Mometasone furoate inhibits tumor progression and promotes apoptosis through activation of the AMPK/mTOR signaling pathway in osteosarcoma

Osteosarcoma is the most common primary malignant bone tumor in adolescents. While treatments for osteosarcoma have improved, the overall survival has not changed for three decades, and thus, new targets for therapeutic development are needed. Recently, glucocorticoids have been reported to have antitumor effects. Mometasone furoate (MF), a synthetic glucocorticoid, is of great value in clinical application, but there are few reports on its antitumor effect. Here, we verified the effect of MF on osteosarcoma in vitro and in vivo. In vitro, cell proliferation, cell cycle progression, apoptosis and cell metastasis were detected using Cell Counting Kit-8 (CCK-8), colony formation, flow cytometry, wound-healing and transwell assays, respectively. In vivo, we generated a xenograft mouse model. To examine the potential role of the AMPK pathway, an AMPK-specific inhibitor (dorsomorphin) was used. The expression levels of factors related to the cell cycle, apoptosis and activation of the AMPK/mTOR pathway were assessed by immunohistochemistry and Western blotting. MF inhibited proliferation and metastasis and induced S phase arrest and apoptosis in osteosarcoma cells in a dose-dependent manner. In vivo, MF effectively inhibited osteosarcoma cell growth and pulmonary metastasis; however, it had no negative effect on the internal organs. Additionally, MF could activate the AMPK/mTOR pathway in osteosarcoma. Dorsomorphin significantly attenuated MF-induced antitumor activities. In summary, MF can inhibit osteosarcoma proliferation and metastasis and promote osteosarcoma cell apoptosis through the AMPK/mTOR signaling pathway in vitro and in vivo, which can provide a new rationale for subsequent academic and clinical research on osteosarcoma treatment.

An improved infrared simulation method based on generative adversarial networks

Infrared simulation technology has great application value in the fields of UAV target simulation and infrared signal processing. However, traditional infrared simulation technology has the disadvantages of poor scene adaptability, low fidelity of infrared simulation images and long training time. This paper proposes an infrared image simulation algorithm based on improved Generative Adversarial Networks(GAN) to generate multi-scene infrared images, which could provide a reliable expansion of infrared database. First, we construct a GAN model by introducing a multi-resolution hierarchical structure and attention mechanism, which can effectively enhance the model’s adaptability to different scenes during the simulation of generating infrared images. Secondly, the SSIM loss term and L1 loss term are introduced during the training process to form a composite loss function, which prompts the proposed GAN to generate high-fidelity infrared simulation images. Finally, while maintaining the quality of simulated infrared images, a lightweight model is designed through model compression to significantly reduce the number of model parameters.The traditional discriminator based on the UNet-self attention is replaced by a discriminator based on attention mechanisms and adaptive max-pooling structure. The proposed GAN is trained and tested on three public datasets. Experimental results demonstrate that the proposed method improves structural similarity by 11% compared to Pix2Pix and achieves a Peak Signal-to-Noise Ratio (PSNR) of 25.8, which is 3.9 higher than InfraGAN. At the same time, compared with InfraGAN, the number of model parameters is reduced by 68% , which makes the method efficiently obtain simulated infrared images corresponding to given visible images.

Stability of Buriti Oil Microencapsulated in Mixtures of Azuki and Lima Bean Flours with Maltodextrin.

Buriti oil (Mauritia flexuosa L.) is rich in carotenoids, mainly β-carotene, and has great value for application as a food, pharmaceutical, or cosmetic ingredient, as well as a natural pigment. Microencapsulation is a promising technique to protect compounds sensitive to degradation such as β-carotene. Materials composed of carbohydrates and proteins, such as azuki bean (Vigna angularis L.) and lima bean (Phaseolus lunatus L.) flours, are alternative matrices for microencapsulation, which additionally provide good amounts of nutrients. In combination with maltodextrin, the flours represent a protective barrier in stabilizing lipophilic compounds such as buriti oil for subsequent spray drying. In this work, the performance of mixtures of maltodextrin with whole azuki and lima bean flours was evaluated in the microencapsulation of buriti oil. The microcapsules showed good results for solubility (>80%), hygroscopicity (~7%), encapsulation efficiency (43.52 to 51.94%), and carotenoid retention (64.13 to 77.49%.) After 77 days of storage, the microcapsules produced maintained 87.79% and 90.16% of carotenoids, indicating that the powders have high potential for application as encapsulants in the food and pharmaceutical industries.

Multi-task learning for hand heat trace time estimation and identity recognition

The thermal imager can capture the invisible thermal trace, and the potential information contained in the thermal trace can be extracted through the identification and analysis of the infrared thermal trace, such as the identity information and time information of the trace. This technology has great application value in criminal investigation, military and other fields. However, with the passage of time, the heat traces will gradually fade, and the intra-class distances will gradually increase. For hand heat trace, the trace may exhibit significant variations at different time points or in different states. This will pose challenges for extracting potential information and present significant challenges for model recognition. Therefore, we propose a multi task framework using deep convolutional neural networks (MTLHand) to jointly handle the two tasks of heat trace identification and heat trace departure time estimation. It can learn invariant representations of thermal trace identities, better capturing the dynamic changes in palmprint data, to enhance the accuracy of identity recognition. Specifically, soft threshold is inserted as nonlinear transformation layers into an improved ResNet to extract deep mixed hand trace features. The mixed features are decomposed into identity related features and time related features through spatial attention and channel attention. Use the multi task training method to carry out the corresponding learning tasks. In addition, we collected and constructed an infrared hand heat trace dataset containing labels such as departure time, gender, hand posture, identity category, etc., to promote the development of heat trace research. Experiments on this dataset show that our method is superior to other deep learning methods for the specific task of infrared heat trace recognition, and the identification accuracy can reach 83.48 %. In the trace leaving time estimation task, the recognition error rate is 18 % if the residual value between the estimated time and the real time is less than 60 s, and 3.55 % if the residual value is less than 120 s.

A rapid motion forecast strategy for ships in waves using seakeeping and maneuvering modules

This paper develops a new navigation assisted system for ships with time-varying wave disturbances. A new framework is presented using seakeeping and maneuvering modules to achieve the fast forecast of ship motions. S175 containership and KVLCC2 tanker are adopted to verify the seakeeping module. Based on the validated module, the heave, roll and pitch motions of a cruise ship in different conditions are calculated by using three-dimensional Rankine panel method. The horizontal surge, sway and yaw motions of ships can be rapidly evaluated by using MMG model considering the wave drift forces. Finally, the motion amplitudes of 6°-of-freedom can be forecasted rapidly by using seakeeping and maneuvering modules. The study provides the insight into the motions of ships in waves. It is of great engineering application value to evaluate the total ship motion amplitudes and to improve the safety of navigation of ships in waves.

Design and Implementation of SaaS Online Education Management System for Expanding LMS System

The development of the education industry benefits from the continuous progress and updates of internet technology, and the organic integration of the two can further promote the emergence of the LMS (Learning Management System) system. Based on the application characteristics of the LMS system and the characteristics of the Software as a Service online education management system, a Software as a Service online education management system based on the extended LMS system was constructed. The service mode of the system was analysed, and a solution to the problem of paper formation and an analysis of the automatic paper formation algorithm in the system were provided. In addition, further research was conducted on the scores of the credibility evaluation of the system’s services under different attribute parameters. Through the correlation analysis between different attributes and the credibility evaluation scores of the Software as a Service system, it can be seen that among the selected attribute parameters, Response Time and Latency show a negative correlation with the credibility evaluation, while other parameters show a positive correlation. The Software as a Service system constructed in this article provides relevant technical solutions, which have great practical application value and have reference significance for other similar applications. It provides more support for the application and promotion of the Software as a Service (SaaS) online education management system.

Detecting anomalies with granular-ball fuzzy rough sets

Most of the existing anomaly detection methods are based on a single and fine granularity input pattern, which is susceptible to noisy data and inefficient for detecting anomalies. Granular-ball computing, as a novel multi-granularity representation and computation method, can effectively compensate for these shortcomings. We utilize the fuzzy rough sets to mine the potential uncertainty information in the data efficiently. The combination of granular-ball computing and fuzzy rough sets takes into account the benefits of both methods, providing great application and research value. However, this novel combination still needs to be explored, especially for unsupervised anomaly detection. In this study, we first propose the granular-ball fuzzy rough set model, and the relevant definitions in the model are given. Subsequently, we pioneeringly present an unsupervised anomaly detection method based on granular-ball fuzzy rough sets called granular-ball fuzzy rough sets-based anomaly detection (GBFRD). Our method introduces the granular-ball fuzzy rough granules-based outlier factor to characterize the outlier degree of an object effectively. The experimental results demonstrate that GBFRD exhibits superior performance compared to the state-of-the-art methods. The code is publicly available at https://github.com/Mxeron/GBFRD.

Hawthorn with "homology of medicine and food": a review of anticancer effects and mechanisms.

Over the past few years, there has been a gradual increase in the incidence of cancer, affecting individuals at younger ages. With its refractory nature and substantial fatality rate, cancer presents a notable peril to human existence and wellbeing. Hawthorn, a medicinal food homology plant belonging to the Crataegus genus in the Rosaceae family, holds great value in various applications. Due to its long history of medicinal use, notable effects, and high safety profile, hawthorn has garnered considerable attention and plays a crucial role in cancer treatment. Through the integration of modern network pharmacology technology and traditional Chinese medicine (TCM), a range of anticancer active ingredients in hawthorn have been predicted, identified, and analyzed. Studies have shown that ingredients such as vitexin, isoorientin, ursolic acid, and maslinic acid, along with hawthorn extracts, can effectively modulate cancer-related signaling pathways and manifest anticancer properties via diverse mechanisms. This review employs network pharmacology to excavate the potential anticancer properties of hawthorn. By systematically integrating literature across databases such as PubMed and CNKI, the review explores the bioactive ingredients with anticancer effects, underlying mechanisms and pathways, the synergistic effects of drug combinations, advancements in novel drug delivery systems, and ongoing clinical trials concerning hawthorn's anticancer properties. Furthermore, the review highlights the preventive health benefits of hawthorn in cancer prevention, offering valuable insights for clinical cancer treatment and the development of TCM with anticancer properties that can be used for both medicinal and edible purposes.

ESMSec: Prediction of Secreted Proteins in Human Body Fluids Using Protein Language Models and Attention.

The secreted proteins of human body fluid have the potential to be used as biomarkers for diseases. These biomarkers can be used for early diagnosis and risk prediction of diseases, so the study of secreted proteins of human body fluid has great application value. In recent years, the deep-learning-based transformer language model has transferred from the field of natural language processing (NLP) to the field of proteomics, leading to the development of protein language models (PLMs) for protein sequence representation. Here, we propose a deep learning framework called ESM Predict Secreted Proteins (ESMSec) to predict three types of proteins secreted in human body fluid. The ESMSec is based on the ESM2 model and attention architecture. Specifically, the protein sequence data are firstly put into the ESM2 model to extract the feature information from the last hidden layer, and all the input proteins are encoded into a fixed 1000 × 480 matrix. Secondly, multi-head attention with a fully connected neural network is employed as the classifier to perform binary classification according to whether they are secreted into each body fluid. Our experiment utilized three human body fluids that are important and ubiquitous markers. Experimental results show that ESMSec achieved average accuracy of 0.8486, 0.8358, and 0.8325 on the testing datasets for plasma, cerebrospinal fluid (CSF), and seminal fluid, which on average outperform the state-of-the-art (SOTA) methods. The outstanding performance results of ESMSec demonstrate that the ESM can improve the prediction performance of the model and has great potential to screen the secretion information of human body fluid proteins.

FLSSnet: Few labeled samples segmentation network for coated fuel particle segmentation

Current image semantic segmentation networks based on convolutional neural networks require extensive training samples and pixel-level annotations to achieve satisfactory segmentation performance. This paper proposes a novel FLSSnet segmentation network based on an encoder-decoder structure. A modified MobileNetV3 backbone network, a contour-assisted training branch, and an improved semi-supervised training method with a cosine function to dynamically update the pseudo-label training weight are added to the network, which effectively reduces its dependency on labeled samples and improves its segmentation accuracy. Compared with other image segmentation networks, the proposed network does not need too many labeled samples for training to achieve the high segmentation accuracy required for industrial inspection. The effectiveness of the proposed network is validated on the coated fuel particle segmentation dataset and the MICCAI’s 2015 gland segmentation public dataset. It has great industrial application value in automating the thickness measurement of each layer of coated fuel particles and other fields.

Rapid quantitative analysis of multiple rare earth elements in NdFeB alloys based on laser-induced breakdown spectroscopy (LIBS) and random forest (RF)

NdFeB material has excellent comprehensive magnetic properties, which plays a crucial role in the field of rare earth magnetic materials, and is one of the important basic materials to support modern high-tech industries. In the production of NdFeB alloys, the control of rare earth elements (REEs) is directly related to the quality and resource utilization efficiency of NdFeB materials. Therefore, the prediction of REEs content in NdFeB alloys is of great research significance and application value for the quality control of products, the enhancement of production efficiency and reduction of energy consumption by the rare earth material industry. In this study, a combination of laser-induced breakdown spectroscopy (LIBS) and random forest (RF) was used to investigate the quantitative analysis of four REEs (Nd, Pr, Tb and Dy) in NdFeB alloys. Firstly, the original LIBS spectra were screened by principal component analysis-mahalanobis distance (PCA-MD). Then, the effects of different data processing methods on the screened LIBS spectra were explored, and next, the feature variables were extracted from the preprocessed spectral data by the variable importance measurement (VIM). In order to further verify the prediction performance of the model, the prediction results of the RF models based on the different methods were compared. Finally, a PCA-VIM-RF calibration model was established on the basis of the optimized spectra, selected feature variables and parameters. Leave-one-out cross validation (LOOCV) was used to optimize the parameters of PCA-MD method, spectral preprocessing method and variable importance thresholds during the construction of the calibration model. The results show that the PCA-VIM-RF model has better prediction performance than the RF calibration model based on the raw spectra. For the PCA-VIM-RF calibration method of Nd, Pr, Tb and Dy elements, the values of R2CV are 0.9991, 0.9998, 0.9986, and 0.9984, respectively, the values of RMSECV are 0.06296%, 0.02788%, 0.04647% and 0.05252%, respectively. The values of R2P are 0.9508, 0.9975, 0.9691 and 0.9457, respectively, and the values of RMSEP are 0.6082%, 0.09205%, 0.5776% and 0.2631%, respectively.The above results indicate that LIBS combined with PCA-VIM-RF algorithm is a promising method for rapid quantitative analysis of REEs in NdFeB alloys without complicated sample preparation, which can provide some new ideas or strategies for the future research, development and quality control of rare earth materials.

Expression and prediction of pump and pumping station characteristics

Abstract According to the relevant literature of pumps and pumping stations and the expression and conversion method of pump efficiency and pump cavitation proposed by the author, through further careful mathematical deduction, the expression of pump and pump device characteristics is proposed. The proposed expressions for centrifugal pumps (and volute type mixed flow pumps) and axial flow pumps (and guide vane type mixed flow pumps) are applicable to both pumps and pump units; it is applicable not only to models, but also to similar prototype pumps and prototype pumping units of any diameter and parameters. The research and realization of performance expression of pumps and pump devices have great theoretical significance and practical application value for improving the design level of pumps and pumping stations and for accurately predicting the characteristics of pumps and pumping stations.

Exploring the Targets and Molecular Mechanisms of Curcumin for the Treatment of Bladder Cancer Based on Network Pharmacology, Molecular Docking and Molecular Dynamics.

Curcumin, a phenolic compound derived from turmeric, has demonstrated anti-tumor properties in preclinical models of various cancers. However, the exact mechanism of curcumin in treating bladder cancer remains unclear. This study aimed to elucidate the therapeutic targets and molecular mechanisms of curcumin in the treatment of BC through an integrated approach of network pharmacology, molecular docking, and molecular dynamics simulations. PharmMapper, SuperPred, TargetNet, and SwissTargetPrediction were utilized to acquire targets associated with curcumin, while GeneCards, CTD, DisGeNET, OMIM, and PharmGKB databases were utilized to obtain targets related to bladder cancer. The drug-disease interaction targets were obtained using Venny 2.1.0, and GO and KEGG enrichment analyses were then conducted with the DAVID tool. We constructed a protein-protein interaction (PPI) network and identified tenkey targets. In conclusion, AutoDock Tools 1.5.7 was utilized to conduct molecular docking simulations, followed by additional analysis of the central targets through the GEPIA, HPA, cBioPortal, and TIMER databases. A total of 305 potential anticancer targets of curcumin were obtained. The analysis of GO functional enrichment resulted in a total of 1105 terms, including 786 terms related to biological processes (BP), 105 terms related to cellular components (CC), and 214 terms related to molecular functions (MF). In addition, KEGG pathway enrichment analysis identified 170 relevant signaling pathways. Treating bladder cancer could potentially involve inhibiting pathways like the PI3K-Akt signaling pathway, MAPK signaling pathway, EGFR tyrosine kinase inhibitor resistance, and IL-17 signaling pathway. Activating TNF, ALB, CASP3, and ESR1 while inhibiting AKT1, EGFR, STAT3, BCL2, SRC, and HSP90AA1 can also hinder the proliferation of bladder tumor cells. According to the results of molecular docking, curcumin binds to these central targets in a spontaneous manner, exhibiting binding energies lower than -1.631kJ/mol. These findings were further validated at the transcriptional, translational and immune infiltration levels. By utilizing network pharmacology and molecular docking techniques, it was discovered that curcumin possesses diverse effects on multiple targets and pathways for treating bladder cancer. It has the potential to impede the growth of bladder tumor cells by suppressing various pathways including the PI3K-Akt and MAPK signaling pathways, as well as pathways associated with EGFR tyrosine kinase inhibitor resistance and the IL-17 signaling pathway. Curcumin could potentially disrupt the cell cycle advancement in bladder cancer cells by increasing the expression of TNF, ALB, CASP3, and ESR1 while decreasing AKT1, EGFR, STAT3, BCL2, SRC, HSP90AA1, and other targeted genes. These findings reveal the possible molecular pathways through which curcumin exerts its anticancer effects in bladder cancer, and this novel research strategy not only provides an important basis for an in-depth understanding of the anticancer mechanism of curcumin, but also offers new potential drugs and targets for the clinical treatment of bladder cancer. Therefore, this study is of great scientific significance and practical application value for promoting the development of bladder cancer therapeutic field. This finding provides strong support for the development of novel, safe and effective drugs for bladder cancer treatment.

A Multimodal Fusion Behaviors Estimation Method for Public Dangerous Monitoring

At the present stage, the identification of dangerous behaviors in public places mostly relies on manual work, which is subjective and has low identification efficiency. This paper proposes an automatic identification method for dangerous behaviors in public places, which analyzes group behavior and speech emotion through deep learning network and then performs multimodal information fusion. Based on the fusion results, people can judge the emotional atmosphere of the crowd, make early warning, and alarm for possible dangerous behaviors. Experiments show that the algorithm adopted in this paper can accurately identify dangerous behaviors and has great application value.

A clinical-radiomics nomogram based on spectral CT multi-parameter images for preoperative prediction of lymph node metastasis in colorectal cancer.

To develop a clinical-radiomics nomogram based on spectral CT multi-parameter images for predicting lymph node metastasis in colorectal cancer. A total of 76 patients with colorectal cancer and 156 lymph nodes were included. The clinical data of the patients were collected, including gender, age, tumor location and size, preoperative tumor markers, etc. Three sets of conventional images in the arterial, venous, and delayed phases were obtained, and six sets of spectral images were reconstructed using the arterial phase spectral data, including virtual monoenergetic images (40keV, 70keV, 100keV), iodine density maps, iodine no water maps, and virtual non-contrast images. Radiomics features of lymph nodes were extracted from the above images, respectively. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select features. A clinical model was constructed based on age and carcinoembryonic antigen (CEA) levels. The radiomics featuresselected were used to generate a composedradiomics signature (Com-RS). A nomogram was developed using age, CEA, and theCom-RS. The models' prediction efficiency, calibration, and clinical application value were evaluated by the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis, respectively. The nomogram outperforms the clinical model and the Com-RS (AUC = 0.879, 0.824). It is well calibrated and has great clinical application value. This study developed a clinical-radiomics nomogram based on spectral CT multi-parameter images, which can be used as an effective tool for preoperative personalized prediction of lymph node metastasis in colorectal cancer.

Evaluation of the efficacy of Wall Shear Stress in Carotid Artery Stenting

To characterize the value of carotid wall shear stress (WSS) following carotid artery stenting (CAS) in patients with carotid stenosis. Twenty-eight patients with carotid stenosis treated with CAS between March 2021 to May 2022 in the eighth medical center of the PLA General Hospital were selected for our study. Carotid ultrasound was performed before the operation, one week post-operation, and six months post-operation. Carotid artery WSS was detected by blood flow vector imaging, and the changes in WSS before and after the operation were collected. Genetic testing of drugs was detected for patients with restenosis. Pre-operative WSS of the proximal, narrowest region, and distal carotid arteries in patients with ischemic carotid artery stenosis was 7.88±3.18Pa, 14.36±6.66Pa, and 1.55±1.15Pa, respectively. Comparatively, pre-operative WSS of the proximal, narrowest region and distal carotid arteries in patients without ischemic symptoms was 5.02±1.99Pa, 9.68±4.23Pa, and 1.10±0.68Pa, respectively, with a significant difference between the two groups (p<0.001). Overall WSS of the proximal, narrowest region, and distal carotid arteries in patients before CAS was 6.68±3.0Pa, 12.47±5.98Pa, and 1.39±0. 96Pa. WSS of the proximal, narrowest region, and distal carotid was 4.15±1.42Pa, 6.71±2.64Pa, and1.86±1.13Pa one week after CAS, compared to 4.44±1.91Pa, 7.90±4.38Pa, and 2. 36±1.09Pa six months after CAS. WSS of the proximal and narrowest region of the carotid artery was reduced after carotid stenting, and the difference was statistically significant (p<0.001). There was no statistically significant difference in WSS between one week and six months after stenting (P>0.05). We employed early carotid WSS as a means of evaluating the efficacy of carotid artery stenting. Changes in carotid WSS are closely associated with carotid artery stenosis, providing valuable hemodynamic information for CAS treatment. This technique holds great application value in pre-operative evaluation and long-term follow-up.

Synthesis Strategies, Preparation Methods, and Applications of Chiral Metal-Organic Frameworks.

Chiral Metal-Organic Frameworks (CMOFs) is a kind of material with great application value in recent years. Formed by the coordination of metal ions or metal clusters with organic ligands. It has ordered and adjustable pores, multi-dimensional network structure, large specific surface area and excellent adsorption properties. This material structure combines the properties of metal-organic frameworks (MOFs) with the chiral properties of chiral molecules. It has great advantages in catalysis, adsorption, separation and other fields. Therefore, it has a wide range of applications in chemistry, biology, medicine and materials science. In this paper, various synthesis strategies and preparation methods of chiral metal-organic frameworks are reviewed from different perspectives, and the advantages of each method are analyzed. In addition, the applications of chiral metal-organic framework materials in enantiomer recognition and separation, circular polarization luminescence and asymmetric catalysis are systematically summarized, and the corresponding mechanisms are discussed. Finally, the challenges and prospects of the development of chiral metal-organic frame materials are analyzed in detail.

Sound absorber based on a sonic black hole and multi-layer micro-perforated panels

In order to achieve low-frequency and broadband sound absorption simultaneously, we propose a structure that combines a sonic black hole with multilayer micro-perforated panels. Firstly, we present finite element models for composite structures based on sonic black holes and micro-perforated panels and describe the sound absorption mechanism of the composite structure by comparing the sound absorption phenomena of micro-perforated panels with sonic black holes and micro-perforated panels with ordinary circular tubes. Secondly, the effects of the end coordinates of the sonic black hole, the number of panels and the parameters of the micro-perforated panels are discussed. Thirdly, the theoretical model of the proposed structure is developed using the transfer matrix method. Finally, the sound absorption test of the proposed structure is carried out using impedance tubes. The test results show that the sound absorption coefficient of the sample with a geometric length of 203 mm reaches 0.8 at 223 Hz and stabilizes above 0.9 at 398–1600 Hz. The sound absorber based on a sonic black hole and multi-layer micro-perforated panels has excellent sound absorption performance and has great research potential and application value.