Professor Cai Wen, a nationally renowned expert with outstanding contributions from Guangdong University of Technology, published a paper on Extension Set and non-compatible Problem in the Journal of Science Exploration in 1983, marking the birth of an original Chinese discipline called Extenics. Extenics is a kind of science that uses formal models to study the laws and methods of extension and transformation of things, and is used to achieve innovation and handle contradictory problems. The year 2023 marks the 40th anniversary of the establishment of Extenics. Through the joint efforts of numerous Extenics researchers, the theory and methodology of Extenics has been gradually improved, and has been widely applied in fields such as engineering technology, information science and intelligent science, management and economy, education and teaching, innovation and entrepreneurship, and has shown its important application value and effectiveness. This article summarizes the development process, research overview, internationalization and socialization of Extenics, and briefly introduces the scientific significance and academic evaluation of Extenics followed with the prospects for the future trends in the development of Extenics.

The advancement of high-speed and high-precision machining has led to a growing demand for cutting tools. Surface hard coatings substantially enhance the wear resistance and overall lifespan of cutting tools, thereby playing a pivotal role in the development of high-performance tools. This paper first introduces the significance of surface hard coatings in cutting operations, then reviews the research status of common hard coatings such as nitride, boride, and oxide, along with associated physical vapor deposition techniques. Finally, an analysis is conducted to identify the prevailing research and application challenges encountered in physical vapor deposition tool coatings.

Hard and brittle materials are extensively utilized in various fields, such as 5G communication, aerospace, and electronic power, owing to their exceptional physical and chemical properties. High-efficiency ultra-precision grinding technology plays a crucial role in their processing. Conducting systematic and thorough research on ultra-precision grinding technology is essential to enhance the processing quality of these materials. A detailed description is presented for three grinding methods: self-rotating grinding, double-sided grinding, and grinding-polishing integration, from the perspective of designing and optimizing ultra-precision grinding equipment. Furthermore, research progress is introduced on the preparation, wear detection, and dressing of super-hard abrasive grinding wheels. A thorough analysis is also provided of the grinding mechanism of hard and brittle materials, considering aspects such as indentation and scratch, nano-grinding, and simulation analysis. The control methods for surface roughness, surface morphology, subsurface damage, and surface shape accuracy of hard and brittle materials are also summarized.

Currently, straight lines or arcs are commonly used to approximate the profile of the formed grinding wheel for gears, resulting in discontinuity and fluctuations in the profile curve of the grinding wheel and even changing the concavity and convexity of the original profile curve, limiting the precision of the gears processed by the dressed formed grinding wheel. Besides, the dressing program is cumbersome and the amount of data is large. Aiming at this problem, a method of using B-spline curve to fit the profile of the formed grinding wheel is proposed, which is convenient for the numerical control system of the gear grinding machine to use the spline interpolation function to dress the formed grinding wheel. The method first calculates the profile of the involute helical gear formed grinding wheel. Feature points are then extracted from the data points of the grinding wheel profile and fitted with a B-spline curve. The fitting errors of the non-feature points are calculated using differential evolution algorithm. The data point with the maximum fitting error is added to the feature points. This process is iteratively repeated until the generated B-spline curve satisfies the fitting error requirement, fitting the profile of formed grinding wheel with fewer control points while meeting the specified error requirements. Simulation results show that the method can effectively fit the profile of the formed grinding wheel and the fitting error can meet the specified requirements.

Constant force control is significant for robot griding or polishing. Due to the influence of guide rail friction, the existing high precision force control employs air bearing and pneumatic control. Aiming at the problem that the precision of mechanical guideway force control system is affected by nonlinear friction, a design method is proposed that converts friction disturbance into elastic disturbance, which solves the problems of friction dead zone and static friction compensation. Specifically, the force control actuator is designed as a frame and working platform connected by a flexible hinge group. When the driving force is less than the friction force, the displacement is completely generated by the elastic deformation of the flexure hinge, eliminating the friction dead zone. The nonlinear friction disturbance is converted into the elastic deformation disturbance of the flexure hinge. Through the deformation amount and deformation rate of the flexure hinge, the elastic force and damping force are calculated for real-time compensation, which solves the problem of friction compensation. The experiment shows that the friction disturbance conversion design reduces the influence of friction, finally improves the force control accuracy to 0.08 N through measurement and compensation, which is 20~50 N times higher than the original, more than 10 times higher than the general air flotation accuracy, and maintains the advantages of fast response and low cost of the electronic control system.

Owing to advantages of high theoretical energy density, low cost and environmental friendliness, lithium-sulfur (Li-S) battery is considered as one of the most promising next-generation high-energy-density batteries. The "shuttle effect" of polysulfides is the key issue hindering the commercialization of Li-S batteries. Adoption of "catalytic" strategy to enhance the sulfur redox kinetics has been demonstrated to be an effective way to alleviate the "shuttle effect". Single-atom catalysts (SACs) have received much attention in the field of catalysis due to their uniform metal active centers, unique electronic properties, and theoretically 100% metal atom utilization. In recent years, SACs have been introduced into Li-S systems and studied to achieve fast sulfur conversion kinetics. In this research, the latest progress in the application of SACs in Li-S batteries was reviewed, with special emphasis on the discussion of key factors affecting the catalytic activity of SACs. The prospects of SACs for Li-S batteries were pointed out and highlighted. Important guidance is provided for future design and fabrication of high-performance SACs for Li-S battery application.

Lithium-sulfur batteries (LSBs) are considered to be one of the most promising new generation energy storage device owing to the high theoretical specific capacity, low cost, and environmental friendliness of sulfur. However, there are still some unsolved critical issues in LSBs, such as slow redox kinetics, shuttle effect caused by dissolution and diffusion of lithium polysulfides (LiPSs), as well as volume changes in electrodes during charge/discharge processes. which result in poor capacity and cycling stability, severely hinder the practical application of LSBs. Metal-organic frameworks (MOFs) have highly tunable pore microenvironment, and their chemical adsorption and catalytic abilities towards guest molecules, such as polysulfides, can be precisely controlled at the molecular level by regulating the metal centers/clusters and organic ligands. Therefore, applying MOFs to LSBs can effectively capture, block and accelerate the catalytic conversion of polysulfides, thus inhibiting the shuttle effect and improving the electrochemical performance of LSBs. This review summarizes various high performance interlayer materials, cathode materials and multifunctional separator materials based on MOFs developed by regulating the pore microenvironment of MOFs, and analyzes the mechanism of regulating MOFs’ microenvironment affecting the performance of LSBs. Finally, the problem and development direction of MOFs materials applicable to high performance LSBs are proposed.

Na metal batteries are considered to be one of the most promising large-scale energy storage batteries due to their high theoretical specific capacity and low cost. However, the high reactivity of sodium metal can easily lead to problems such as instability of the solid electrolyte interface (SEI) film, uneven deposition of sodium, and dendrite growth. Here, an Al₂O₃ in-situ modified Al foil current collector (Al@Al₂O₃) was fabricated by a facile one-step calcination method to promote uniform Na deposition/stripping. During the discharge process, Al₂O₃ is sodiumified to form a Na-Al-O film with high ion conductivity, which not only stabilizes the electrode/electrolyte interface, but also regulates the nucleation behavior on the current collector surface, reducing the formation of nuclear energy barrier, improving ion mass transfer kinetics, and achieving uniform deposition of dendrite-free sodium and long cycle life. The results show that Al@Al₂O₃ can stably deposit/strip sodium for 50 times with an average Coulombic efficiency of 99.6% under 3 mA·cm^-2/3 mAh·cm^-2; and that the Na-Al@Al₂O₃‖Na-Al@Al₂O₃ symmetric battery can be cycled stably for 1000 h at 1 mA·cm^-2 and 1 mAh·cm^-2. Even at a high current density of 10 C, the NVP‖Na-Al@Al₂O₃ full cell can be cycled stably for 250 cycles with a high capacity retention of 94%.

Oxygen evolution reaction (OER) is a core and rate-control process for the electrocatalytic water splitting. Due to the high energy barrier, the OER kinetics and overall water splitting efficiency are limited. Herein, bimetallic sulfide Co₉S₈-Ni₃S₂/SN-C catalyst was successfully prepared via oxidative ammonolysis of renewable lignosulfonates, and showed excellent electrocatalytic OER performance. The complex structure of sodium lignosulfonate modified with NHCO groups was investigated by comprehensive characterization, and the structural evolution mechanism of the precursor during pyrolysis and carbonization was revealed, with the formation of the active center Co₉S₈-Ni₃S₂ revealed as well. The strong binding of the modified lignin-carbon with metals enhanced the dispersion of active sites Co₉S₈ and Ni₃S₂. The close interaction between Co₉S₈-Ni₃S₂ and the defect doping of N onto the carbon carrier effectively regulated the surface electronic structure, and optimized the adsorption of the reaction intermediate, and thus improved the electrochemical OER reaction performance. The OER activity of Co₉S₈-Ni₃S₂/SN-C catalyst reached the highest level when m(H₂O₂)/m(LS) = 1.5 for oxidative ammonolysis, and showed a lower overpotential (350 mV) than that of commercial Ru/C catalyst (420 mV) at a current density of 50 mA·cm^?2. This work provides an insight into the directional regulation of lignin-based functional materials and its development for highly efficient and stable OER electrocatalysis.

The development of low-cost, highly stable, and catalytically active oxygen reduction reaction (ORR) non-precious metal catalysts is essential for fuel cells and metal-air batteries applications. In this study, the one-step synthesis of composite catalysts of ZnFe nanoparticles embedded with nitrogen and sulfur doped carbon nanotubes (ZnS-FeS-Fe₃C/S, NCNT) was reported using ZIF-8 as precursor. The sample of ZFF/S, NCNT-8 after the optimal calcination temperature was determined by electrochemical tests to be onset potential of 0.99 V vs. RHE and a half-wave potential of 0.84 V vs. RHE, as well as a good methanol tolerance and long-term durability comparable to commercial Pt/C. It is also proposed that S, N co-doped carbon nanotube (CNT) and additional ZnS, FeS and Fe₃C providing active sites can enhance the ORR catalytic performance. The in-situ growth CNT method used in the study can provide ideas for the preparation of cathode catalysts for fuel cells and metal-air batteries.

Ternary oxide, as a class of photoelectrode materials, are considered promising for solar energy conversion. Copper bismuthate (CuBi₂O₄) photocathode is one of the ideal photocathode materials with great potential and application value in photoelectrochemical water splitting, however, its development and application still face great challenges due to its inherent problem of slow carrier mobility. In this study, CuBi₂O₄ photocathode was prepared by spray pyrolysis and the breakthrough of photoelectrochemical performance was realized by morphology modulation and defect regulation. Firstly, by doping Na⁺ into the CuBi₂O₄ photocathode (Na-CuBi₂O₄), the Bi³⁺ sites is replaced by the low-valence Na⁺ and hole centers are formed, thus enhancing the carrier transmission capacity. Meanwhile, the introduction of Na⁺ led to a porous nanomorphology of the prepared CuBi₂O₄ photocathode, which effectively shortened the transmission distance of photogenerated carriers to the surface. Secondly, by annealing Na-CuBi₂O₄ in oxygen atmosphere (Na-CuBi₂O₄-O₂), the Na-CuBi₂O₄-O₂ photoelectrode forms multiple metal vacancies as electron acceptors, which reduces the oxygen vacancies brought by Na⁺ doping, thus increasing the hole density and further improving the charge separation efficiency. This strategy resulted in a high photocurrent density of –2.83 mA·cm^–2 at 0.6 V vs. RHE for the Na-CuBi₂O₄-O₂ photoelectrode, which is 15 times higher than that of the untreated CuBi₂O₄ photoelectrode (–0.18 mA cm^–2). By combining time-resolved fluorescence spectroscopy, Kelvin probe force microscopy and photoelectrochemical studies, it was revealed that the Na-CuBi₂O₄-O₂ photoelectrode exhibits longer carrier lifetime and higher surface photovoltage. In a word, this work utilizes elemental doping and metal vacancies to enhance the charge separation and transfer ability of CuBi₂O₄ photocathode, realizing a significant improvement in its photoelectrochemical performance, which is of guiding significance for the future development of high-performance photocathodes.

It is of great significance to develop an inexpensive and efficient catalyst to enhance the catalytic activity of oxidation coupling of alcohols with amines to imine. In this research, a series of manganese-iron bimetallic oxide catalysts have been prepared by a simple co-precipitation method, which were applied to the catalytic reaction of oxidation coupling of benzyl methanol with aniline to N-benzylideneaniline. The effects of different Fe/Mn feed ratios on the catalytic activity of the products have been explored. The Mn_0.5Fe_0.5O_x sample with a Fe/Mn feed ratio of 1:1 demonstrated the best catalytic activity, giving an aniline conversion rate of 74.7%, 99.9% selectivity and 74.6% yield of N-benzylidene, respectively. Through a variety of characterizations, Mn_0.5Fe_0.5O_x exhibited rich mesoporous structure and surface adsorbed oxygen species, as well as excellent oxidation ability. The high-efficiency catalyst synthesized in this work has great application potential in the preparation of imines by oxidation coupling of alcohols with amines.

Emerging contaminants (ECs) are characterized by stable structures and low concentrations, making them difficult to remove completely using traditional wastewater treatment processes. ECs are posing potential risks to aquatic ecosystems and human health. Advanced oxidation processes (AOPs) can rapidly and effectively degrade persistent pollutants. However, for trace refractory ECs in real water matrices, AOPs require excessive oxidants or consume more energy, resulting in low cost-effectiveness of water treatment and even secondary pollution. Therefore, developing efficient and low-energy selective oxidation processes for treating trace ECs in water has practical significance. Targeted adsorption-transformation technology can effectively enhance the utilization of free radicals and efficiently remove trace ECs. The concept of advanced water purification processes is elaborated based on selective oxidation, with a focus on the technical characteristics and recent development of selective electrochemical adsorption-transformation technology to remove per- and polyfluoroalkyl substances (PFAS) from water. Finally, an outlook is provided on the future research directions and trends.

As a key component of nuclear fuel, uranium resources are crucial to ensuring the construction and sustainable development of our country's nuclear industry. However, conventional terrestrial uranium resources are facing the challenges of relatively poor and low-grade mineable uranium ore. In contrast, seawater uranium resource reserves are nearly a thousand times that of land, and China owns vast sea areas and is rich in seawater resources. Therefore, how to efficiently extract uranium resources from seawater to meet the development needs of nuclear industry in China is an important scientific issue that needs to be explored and solved urgently. The development status of seawater uranium extraction technology is systematically introduced, the research progress of the uranium extraction materials especially the most promising adsorption materials summarized, taking material design and engineering application as the key points to clarify the main challenges and future development prospects of seawater uranium extraction technology.

Climate change caused by greenhouse gas emissions and aquatic eutrophication formed by nitrogen and phosphorus enrichment are the key issues that need to be solved urgently in the field of ecological environment in China. The agricultural sector is one of the main sources of carbon, nitrogen, and phosphorus emissions, and analyzing their characteristics and driving forces is of great significance to the implementation of China's pollution reduction and carbon reduction strategy. Based on the cross-sectional data of the agricultural sector in Guangdong province, the carbon emission model, nitrogen and phosphorus runoff model, and the LMDI driver model of agricultural sources in Guangdong province are constructed to analyze the evolution characteristics and driving factors of carbon, nitrogen and phosphorus emissions in each agricultural source sector at the provincial and county levels. The results showed that: (1) the carbon, nitrogen and phosphorus emissions from county agricultural sources in Guangdong province had significant spatial differences, and had the characteristics of concentration, correlation and homology in space; (2) From 1990 to 2021, the carbon and nitrogen emissions from agricultural sources showed heterogeneous changes, carbon emissions increased, nitrogen and phosphorus emissions decreased, and the nitrogen to phosphorus ratio showed an upward trend; (3) Per capita primary industry added value and unit primary industry added value emissions were the largest driving forces affecting the rise and decrease of agricultural carbon, nitrogen and phosphorus emissions in Guangdong province, respectively, and the ranking was different among different elements, indicating that there were differences in the driving factors controlling the change of agricultural source carbon and nitrogen emissions. The results of this study will provide decision-making support for the identification and collaborative regulation of key source areas of agricultural source carbon, nitrogen and phosphorus emissions in Guangdong province.

Deepfake detection aims to authenticate facial images and videos, which can offer operational and technical support to safeguard personal portrait rights, prevent fake news, and curb online deceit. Early detection technologies are usually based on convolutional neural networks (CNNs) and have achieved promising detection performance. However, there exists a prevalent issue of mediocre generalisation performance. To enhance the overall generality of Deepfake detection, recent research has focused on a deep neural network Transformer by utilizing the self-attention mechanisms. The Transformer can better model long-distance dependency and global receptive fields to capture the image context association and video timing relationships, such that the representation ability of the detectors can be improved. This survey first provides an overview of the research background in this field, followed by an explanation of the common techniques used to generate Deepfake. Then, the existing Transformer-based detection methods are summarized and comparatively evaluated. Finally, the challenges and future research directions of Deepfake detection are discussed.

Cardiac multi-class segmentation is of great significance in medical imaging, which can provide accurate cardiac structure information and assist clinical diagnosis. However, in the training of multi-class semantic segmentation models with high-resolution cardiac images, the loss of deep features due to multiple downsampling operations leads to the problems oforgan discontinuity and incorrect edge segmentation in the segmented cardiac. To address this, this paper proposes a 3DCSNet based on self-attention and 3D convolution for cardiac multi-class segmentation. Specifically, our proposed network introduces the 3D feature fusion module and a 3D spatial perception module into the segmentation network. The former 3D feature fusion module integrates self-attention and 3D convolution for parallel feature extraction, which is able to efficiently allocate the attentions weights within and between channels under the same dimension of the feature map. The latter 3D spatial perception module captures the positional correlation information between different dimensions by integrating the self-attention mechanism, avoiding the loss of important information in downsampling and further retaining the deep key features. Experimental results show that the proposed 3DCSNet outperforms several existing models on a publicly available 3D computed tomography image dataset (ImageCHD).

The medical named entity recognition aims to automatically identify and classify medical entities in electronic medical records, which plays a very important role in downstream tasks such as information retrieval and knowledge graph. Existing methods usually ignore the dependencies between entities. To address this, this paper proposes a gated attention unit-based model for Chinese medical named entity recognition. First, the proposed model uses the pre-training model MC-BERT to capture contextual information. Then, it uses the cross-attention and gated attention unit to enhance the interaction between entity query and contextual semantics, and further extract the dependency and correlation between entities. Finally, the proposed model uses the matching algorithm of bipartite graph to calculate the loss. This paper conducted experiments on three datasets, including the CMeEE, CMQNN, and MSRA. The experimental results show that the F₁ values of the proposed model on the three datasets are 70.74%, 96.92%, and 95.53%, respectively, which outperforms other related models, demonstrating the effectiveness of the proposed model in Chinese medical named entity recognition task.