24 December 2022, Volume 34 Issue 12
-
Select all|
-
Medical Journal of Kiang Wu. 2022, 34(12): 2561-2572. https://doi.org/10.13390/j.issn.1672-1861Abstract ([an error occurred while processing this directive]) Download PDF ([an error occurred while processing this directive]) HTML ([an error occurred while processing this directive])
Knowledge map
Save
Deep learning is a modeling method based on neural network, which is constructed of multiple different functional perception layers and optimized by learning the inherent regularity of a large amount of data to achieve end-to-end modeling. The growth of data and the improvement of computing power promoted the applications of deep learning in spectral and medical image analysis. The lack of interpretability of the constructed models, however, constitutes an obstacle to their further development and applications. To overcome this obstacle of deep learning, various interpretability methods are proposed. According to different principles of explanation, interpretability methods are divided into three categories: visualization methods, model distillation, and interpretable models. Visualization methods and model distillation belong to external algorithms, which interpret a model without changing its structure, while interpretable models aim to make the model structure interpretable. In this review, the principles of deep learning and three interpretability methods are introduced from the perspective of algorithms. Moreover, the applications of the interpretability methods in spectral and medical image analysis in the past three years are summarized. In most studies, external algorithms were developed to make the models explainable, and these methods were found to be able to provide reasonable explanation for the abilities of the deep learning models. However, few studies attempt to construct interpretable algorithms within networks. Furthermore, most studies try to train the model through collecting large amounts of labeled data, which leads to huge costs in both labor and expenses. Therefore, training strategies with small data sets, approaches to enhance the interpretability of models, and the construction of interpretable deep learning architectures are still required in future work.
Contents1 Introduction
2 Principle and algorithm
3 Interpretability method
3.1 Visualization method
3.2 Model distillation
3.3 Interpretable model
4 Spectral analysis
5 Medical image analysis
5.1 Segmentation
5.2 Disease diagnosis
6 Summary and outlook
-
Pacific Journal of Optimization. 2022, 34(12): 2573-2587. https://doi.org/10.7536/PC220602Abstract ([an error occurred while processing this directive]) Download PDF ([an error occurred while processing this directive]) HTML ([an error occurred while processing this directive])
Knowledge map
Save
Cardiovascular disease (CVD) is the leading cause of death worldwide, while acute myocardial infarction (AMI) is the main cause of cardiovascular death. Early and rapid diagnosis of AMI is essential to reduce mortality in patients with CVD. Due to the lack of sufficient sensitivity of common detection methods such as electrocardiogram (ECG), screening for AMI-related biomarkers and conducting sensitive detection has become an important tool for early and accurate detection of AMI. Currently, cardiac troponin I (cTnI), creatine kinase-MB isoenzymes (CK-MB) and myoglobin (Myo) are identified as important biomarkers of myocardial injury. In the past few decades, many biosensors have been developed to detect biomarkers of myocardial injury, among which surface-enhanced Raman spectroscopy (SERS)-based detections have developed rapidly and showed unique advantages and broad application prospects. In this paper, several biomarkers of myocardial injury and their associations with AMI are introduced firstly, and the principles, advantages and limitations of the conventional detection methods for AMI-related biomarkers detection are outlined. Based on this, the research progress of SERS on detection of biomarkers of myocardial injury in recent years is reviewed, and the application and prospect of SERS in AMI diagnosis and the problems and direction of further study are discussed.
Contents1 Introduction
2 Cardiac biomarkers
2.1 Cardiac troponin
2.2 Creatine kinase-MB isoenzymes
2.3 Myoglobin
3 Conventional detection methods
3.1 ELISA
3.2 Electrochemical immunoassay
3.3 Chemiluminescence immunoassay
3.4 Fluorescence immunoassay
4 SERS detection of biomarkers of myocardial injury
4.1 SERS and SERS-based biosensing
4.2 SERS detection of myocardial injury-related biomarkers
5 Conclusion and outlook
-
Journal of The Chinese Society of Education. 2022, 34(12): 2588-2603. https://doi.org/10.7536/PC220528Abstract ([an error occurred while processing this directive]) Download PDF ([an error occurred while processing this directive]) HTML ([an error occurred while processing this directive])
Knowledge map
Save
Intelligent materials with stimulus responsiveness become the research hotspots in recent years. Liquid crystal (LC) materials with supramolecular self-assembly nanostructure and stimulus responsive properties exhibit inherent advantages in the development of the novel intelligent functional materials. Blue phases (BPs), as the LC phases with exotic fluid self-assembled 3D periodic supernanostructures and the characteristic of selective reflection of circularly polarized light in visible light range, have been regarded as one kind of the most promising candidates for smart photonic crystals. The crystallographic parameters or phase states of BPs are susceptible to various external stimulation such as temperature, light irradiation, electric field or humidity. This causes a change in the photonic band gap of BPs, which exhibits visually reflection color variance. Therefore, BPs have recently drawn vast and increasing attentions due to their external-field responsive properties and great potential applications in intelligent materials. Herein, we provide the frontier research advancements in the stimuli-responsive blue phase liquid crystalline photonic crystals. The important research results obtained on the optical, magnetic, electrical, mechanical and humidity responsiveness of BPLC photonic crystals were introduced in detail. At the end of this review, the challenges and possible development direction of this novel soft matter intelligent material are prospected briefly.
Contents1 Introduction
2 Supramolecular self-assembled structures and three-dimensional photonic band gap of blue phases
3 External-field responsiveness of blue phase liquid crystalline photonic crystals
3.1 Light responsiveness
3.2 Magnetic responsiveness
3.3 Electric field responsiveness
3.4 Mechanical responsiveness
3.5 Humidity responsiveness
4 Conclusion and outlook
-
Journal of Dalian Maritime University. 2022, 34(12): 2604-2618. https://doi.org/10.7536/PC220504Abstract ([an error occurred while processing this directive]) Download PDF ([an error occurred while processing this directive]) HTML ([an error occurred while processing this directive])
Knowledge map
Save
With the development of social economy, people pay more and more attention to physical health and demand more and more intelligence, portability and accuracy of medical devices, in this context, the market demand for wearable biosensors is rising. Smart fibers and textiles can meet the requirements of air permeability and wearability, and can be used in wearable biosensors to monitor people’s physical condition in real time. For vital signs monitoring, they can be used to reflect physiological status such as heartbeat, pulse, respiration and body movements in real time. For body fluid detection, the components in sweat, tears and saliva can be analyzed in real time. They can also analyze the exhaled products. Using the characteristics of wearing the sensor on the body, people can monitor their health while living and working normally. Compared with traditional biosensors, wearable biosensors based on smart fibers and textiles can be used for on-site real-time monitoring, and play an important role in preventing diseases, improving clinical outcomes and quality of life, increasing productivity, reducing medical burden and reducing medical costs. Here, this review mainly introduces the application of smart fibers and textiles in wearable biosensors in recent years, and according to three aspects (vital signs monitoring, body fluids detection and exhaled products detection), we introduce their sensing strategies such as colorimetric sensing, fluorescence sensing, piezoelectric sensing, etc. Finally, we summarize the application status and problems of smart fibers and textiles in wearable biosensors, and look forward to their future development.
Contents1 Introduction
2 Vital signs monitoring
2.1 Breathing and heartbeat
2.2 Pulse and blood pressure
2.3 Temperature
2.4 Humidity
2.5 Body movements
3 Body fluid detection
3.1 Sweat
3.2 Saliva
3.3 Urine
3.4 Tear
4 Exhaled products detection
4.1 Ammonia
4.2 Acetone
4.3 COVID-19 detection
5 Conclusion and outlook
-
Journal of Dalian Maritime University. 2022, 34(12): 2619-2637. https://doi.org/10.7536/PC220524Abstract ([an error occurred while processing this directive]) Download PDF ([an error occurred while processing this directive]) HTML ([an error occurred while processing this directive])
Knowledge map
Save
Metal-organic frameworks (MOFs) with ultrahigh surface area, large pore volume and tunable pore environment are regarded as promising adsorbents in gas adsorption and separation. With the exploding number of possible MOFs, it is an imperative challenge to discover high-performing MOFs for a specific application. High-throughput computational screening (HTCS) has been frequently adopted to identify the suitable adsorbents with remarkably reduced computational time and cost in the past decades. Recently, machine learning (ML) is used to accelerate the HTCS process and explore the structure-property relationship. In this review, we summarize the ML algorithms for gas storage, separation and catalysis by MOFs. Four categories of descriptors widely used in ML are reviewed, including geometrical descriptor (i.e., pore size, surface area), topological descriptor (i.e., pore connectivity, cavity size), chemical descriptor (i.e., atom type, degree of unsaturation), and energy-based descriptor (i.e., cohesive energies, Voronoi energies). Unsupervised learning and supervised learning, such as linear regression (LR), artificial neural network (ANN), support vector machine (SVM), and random forest (RF) are introduced and a complete overview of how these ML algorithms are effectively utilized to assist MOF discovery is provided. Recent research progress on ML algorithms in various applications, such as H2, CH4 storage, carbon capture, noble gas, Cx/Cy separation and catalysis are presented in this work. It is anticipated that ML will play a more vital role in identifying top candidates with increasing number of MOFs. Thus, this review aims to outline fundamental knowledge of ML algorithms for MOF discovery in the fields of gas storage, separation and catalysis.
Contents1 Introduction
2 Methodology
2.1 High-throughput computational screening
2.2 Machine learning
3 Machine learning accelerated MOFs screening
3.1 Gas storage
3.2 Gas separation
3.3 MOFs physicochemical property
4 Conclusion and outlook
-
Journal of Dalian Maritime University. 2022, 34(12): 2638-2650. https://doi.org/10.7536/PC220422Abstract ([an error occurred while processing this directive]) Download PDF ([an error occurred while processing this directive]) HTML ([an error occurred while processing this directive])
Knowledge map
Save
Selective catalytic reduction (SCR) technology is the most widely used industrial denitration technology at present, the development of catalyst system with excellent activity and anti-poisoning performance is the focus of researchers. Transition metal oxides and metal organic framework (MOF) materials have been widely studied and applied in the field of denitration catalysts because of their excellent redox performance, and researchers found that the combination of transition metal oxides and MOF materials can further improve the denitration activity of the catalyst. This paper summarizes the research progress of a series of single transition metal based MOF denitration catalysts and composite transition metal based MOF denitration catalysts mainly used in NH3-SCR reaction in recent years, and expounds the strengthening methods of water resistance, sulfur poisoning resistance and thermal stability of transition metal based MOF denitration catalysts, The main research directions of transition metal based MOF denitration catalysts in the future are prospected: new transition metal based MOF denitration catalysts with excellent denitration activity, water and sulfur resistance and thermal stability can be prepared by comprehensively utilizing the characteristics of different transition metal oxides and combined with the strong interaction between metal oxides. Further, transition metal based MOF denitration catalyst can be prepared by combining experiment and simulation to selectively catalyze the reduction of nitrogen oxides to meet the needs of industrialization.
Contents1 Introduction
2 Transition metal based MOF denitration catalyst
2.1 Mono-transition metal based MOF denitration catalysts
2.2 Double-transition metal based MOF denitration catalysts
3 Enhancement of antitoxicity of transition metal based MOF denitration catalysts
3.1 Enhancing the water resistance of transition metal based MOF denitration catalysts
3.2 Enhancing the sulfur resistance of transition metal based MOF denitration catalysts
4 Method for enhancing thermal stability of transition metal based MOF denitration catalysts
4.1 Adding metal clusters
4.2 Optimizing the molar ratio of metal ions to organic ligands
5 Conclusion and Prospect
-
Journal of Dalian Maritime University. 2022, 34(12): 2651-2666. https://doi.org/10.7536/PC220413Abstract ([an error occurred while processing this directive]) Download PDF ([an error occurred while processing this directive]) HTML ([an error occurred while processing this directive])
Knowledge map
Save
MFI zeolite nanosheets have tremendous application potential in adsorption, separation and catalysis fields, which has become one of the hot topics in control synthesis and application of MFI zeolite due to its open framework structure, large external surface, optimized surface acidity, highly accessible acid sites and excellent molecular mass transfer properties. This review focuses on the synthesis mechanism and template types by in-situ hydrothermal synthesis and post-synthesis, as well as the influencing factors of thickness, lamellar spacing and orderliness in depth on the manufacturing and utilization of MFI zeolite nanosheets. The development of MFI zeolite nanosheets with low economic cost and suitable for mass production, as well as its application in the preparation of ultrathin zeolite membranes, catalysis of organic macromolecular reactions, and the preparation of metal catalysts supported by MFI zeolite nanosheets, are the main future research directions.
Contents1 Introduction
2 Synthesis method of MFI zeolite nanosheets
2.1 In-situ hydrothermal
2.2 Post-synthesis
3 Applications of MFI zeolite nanosheets
3.1 Synthesis and separation application of ultrathin membrane
3.2 Catalytic conversion of organic compounds
3.3 Confinement synthesis and application of metal catalysts within MFI zeolite nanosheets
4 Conclusion and outlook
-
Journal of Dalian Maritime University. 2022, 34(12): 2667-2685. https://doi.org/10.7536/PC220403Abstract ([an error occurred while processing this directive]) Download PDF ([an error occurred while processing this directive]) HTML ([an error occurred while processing this directive])
Knowledge map
Save
Most of per- and poly-halogenated organic pollutants (PHOPs)possess biodiversity and bioaccumulation, long persistency and global transport throughout the environment and pose adverse effects on humans. Due to the strong electron-withdrawing ability and a large number of halogen atoms, PHOPs usually possess low positive position for highest occupied molecular orbital level and consequently are resistant to the oxidative degradation. Reductive technologies can efficiently degrade PHOPs, but they suffer from a problem of the accumulation of highly toxic less halogenated products, because the lower-halogenated products tend to be more difficult to reduce. In contrast, these lowly-halogenated products can be easily oxidized. Thus, the consecutive reduction and oxidation method has been developed to degrade PHOPs, in which the pre-reduction of PHOPs and the consecutive oxidation of dehalogenated intermediates are combined to realize the complete dehalogation and mineralization. Herein, this review summarizes the latest efforts to develop consecutive reduction and oxidation processes that are categorized as five types: catalysis, electrochemistry, photocatalysis, photoelectrochemistry and mechanochemistry. Particular attention is focused on the chemical design principles of consecutive reduction and oxidation processes that can help develop treatment technologies to efficiently eliminate PHOPs.
Contents1 Introduction
2 Reductive or oxidative degradation of PHOPs
2.1 Perfluorinated compounds
2.2 Chlorophenols
2.3 Polybrominated diphenyl ethers
3 Consecutive Reductive and Oxidative degradation of PHOPs
3.1 Traditional chemical reduction and Fenton like oxidation
3.2 Photochemical reduction and oxidation
3.3 Electrochemical reduction and oxidation
3.4 Photo-electrocatalytic reduction and oxidation
3.5 Mechanochemical reduction and oxidation
3.6 Hydrated electron-mediated reduction and persulfate-based catalytic oxidation
4 Conclusion and outlook
-
Journal of Dalian Maritime University. 2022, 34(12): 2686-2699. https://doi.org/10.7536/PC220405Abstract ([an error occurred while processing this directive]) Download PDF ([an error occurred while processing this directive]) HTML ([an error occurred while processing this directive])
Knowledge map
Save
Oxygen evolution reaction (OER) is a key process for green and sustainable energy storage and transfer technologies like electrocatalytic water splitting, rechargeable metal-air batteries, regenerative fuel cells, etc. Nevertheless, the high potential barrier and sluggish kinetic process limit its overall performance. Thus, designing and exploiting high-efficient, robust and noble-metal-free catalysts is one of the challenges in the field of new energy. CoFe layered double hydroxide (CoFe LDH) possesses broad prospects in OER due to the extraordinary features such as unique 2D layered structures, multiple and flexible chemical compositions, high dispersive metal cations, excellent stability and low cost. However, the poor conductivity and insufficient reactive sites hamper its industrial application. Beginning with the introduction of the structure of CoFe LDH and the elaboration of the proposed mechanisms for OER, the preparation of CoFe LDH and the current modification strategies for CoFe LDH to thoroughly boost its reactivity are summarized including intercalation and exfoliation, vacancy creation, hybridization, ions substitution and their derivatives. At last, the current challenges and future directions for LDH-based nanostructures in energy conversion and utilization are discussed.
Contents1 Introduction
2 Fundamentals of LDHs for OER
2.1 Structure of LDHs
2.2 OER mechanisms of LDHs
3 Preparation of CoFe LDH
3.1 Precipitation and solvothermal methods
3.2 Electrodeposition
4 Modification strategies of CoFe LDH
4.1 Intercalation and exfoliation
4.2 Vacancy creation
4.3 Hybridization
4.4 Ions substitution
4.5 Derivatives
5 Conclusion and outlook
-
Journal of Dalian Maritime University. 2022, 34(12): 2700-2714. https://doi.org/10.7536/220501Abstract ([an error occurred while processing this directive]) Download PDF ([an error occurred while processing this directive]) HTML ([an error occurred while processing this directive])
Knowledge map
Save
The pathogenic bacteria have exerted great threat to the safety of human being. Conventional antibacterial agents like antibiotics suffer from some limitations against drug-resistant bacteria. Thus, it is imperative to design novel antibacterial agents for efficient bacteria removal. Due to the tunable pores, abundant surface charge, periodically dispersed metal clusters and rich active sites, metal-organic frameworks (MOFs) can efficiently remove bacteria not only via the slowly released metal ions, but also via the corresponding advanced oxidation processes (AOPs) including photocatalysis, persulfate activation, Fenton-like reaction and photo-electrocatalysis. This review summarizes recent progress of antibacterial performances over MOFs-based AOPs catalysts (pristine MOFs, MOFs composites, MOFs derivatives and MOFs monoliths), in which the design strategies, antibacterial properties and antibacterial mechanisms are discussed. Finally, the challenges and the potential applications of MOFs-based catalysts are proposed.
Contents1 Introduction
2 MOFs-based AOPs catalysts for bacteria removal
2.1 Pristine MOFs
2.2 Photosensitized MOFs
2.3 MOFs photo-electrodes
2.4 MOFs heterojunctions
2.5 MOFs derivatives
2.6 MOFs monoliths
3 Conclusions and outlooks
-
Journal of Dalian Maritime University. 2022, 34(12): 2715-2728. https://doi.org/10.7536/PC220511Abstract ([an error occurred while processing this directive]) Download PDF ([an error occurred while processing this directive]) HTML ([an error occurred while processing this directive])
Knowledge map
Save
Fused-ring electron acceptors (FREAs) based organic solar cells (OSCs) have achieved rapidly development in recent years. However, the complexity of molecular structure of fused-ring electron acceptors leads to high synthesis costs and low yields, limiting their further commercial applications. Non-fused-ring small molecule acceptors have attracted widespread attention due to their simple moleclar structure, structural diversity, and low synthesis cost due to their application of C—C single bonds. In this review, from the perspective of materials design, focusing on the development of non-fused-ring electron acceptors (NFREAs), this paper will briefly discuss the influence of structural regulation on the basic properties, aggregation structure, molecular packing, active layer morphology and the corresponding photovoltaic performances. Further elucidating the structure-property relationships of non-fused-ring acceptor materials. Finally, we will prospect the development and challenge of non-fused-ring acceptors based OSCs from the point view of material design, device optimization, device photovoltaic performance, and device stability.
Contents1 Introduction
2 Research progress of non-fused ring electron acceptors
2.1 Origin of intramolecular noncovalent interactions
2.2 Application of intramolecular noncovalent interactions in non-fused ring acceptors
2.3 Fully non-fused ring acceptors
2.4 Application of NFREAs with the large steric hindrance side chains
3 Conclusion and outlook
3.1 Material design
3.2 Device optimization
3.3 Morphology optimization
3.4 Device Stability
