Technological Bottlenecks Research Articles

Advanced materials for ancient architecture protection: a mini review

Ancient architectures are not only valuable for their historical significance but also represent the scientific and technological advancements of human civilization over thousands years. Protecting these cultural relics is crucial for preserving human history, culture, and scientific values. However, ancient architecture is vulnerable to natural aggressions such as fires, lightning strikes, earthquakes, floods, biological threats, and ultraviolet rays over time. Additionally, while the electrification and information technology bring great convenience to daily life, the accompanying electromagnetic pollution, continuous vibration, and noise pollution also pose significant threats to the lifespan of ancient architecture. This review describes the progress made in the field of materials for ancient architecture protection to combat these threats. The protection research includes flame retardant materials, anti-biological attack materials, anti-aging materials, and vibration and noise reduction materials. The review also analyzes the mechanism of the role of materials for ancient architecture protection and the mechanism of performance enhancement. Finally, the review points out the key scientific and technological bottlenecks that need to be urgently addressed. This paper may be an important guide for subsequent research on materials for the conservation of ancient architectures.

Concentrated Solar-Driven Catalytic CO2 Reduction: From Fundamental Research to Practical Applications.

Concentrated solar-driven CO2 reduction is a breakthrough approach to combat climate crisis. Harnessing the in-situ coupling of high photon flux density and high thermal energy flow initiates multiple energy conversion pathways, such as photothermal, photoelectric, and thermoelectric processes, thereby enhancing the efficient activation of CO2. This review systematically presents the fundamental principles of concentrated solar systems, the design and classification of solar-concentrating devices, and industrial application case studies. Meanwhile, key technological advances-from theoretical foundations to practical applications-are also discussed. At the microscopic level, a comprehensive analysis of multiscale reaction kinetics within the domain of photothermal synergistic catalysis has been conducted. This analysis elucidates the significance of catalyst design, further detailing the intricate regulatory mechanisms governing reaction pathways and active sites through nanostructured catalysts, single-atom catalysts, and metal-support interactions. However, the transition from laboratory research to industrial-scale application still faces challenges, including the complexity of system integration, energy density optimization, and economic feasibility. This review provides a theoretical framework and practical guidance through a complete investigation of current technological bottlenecks and future development directions, with the aim of driving key advances in concentrated solar-driven CO2 reduction catalysis.

Research on Small-Target Detection of Flax Pests and Diseases in Natural Environment by Integrating Similarity-Aware Activation Module and Bidirectional Feature Pyramid Network Module Features

In the detection of the pests and diseases of flax, early wilt disease is elusive, yellow leaf disease symptoms are easily confusing, and pest detection is hampered by issues such as diversity in species, difficulty in detection, and technological bottlenecks, posing significant challenges to detection efforts. To address these issues, this paper proposes a flax pest and disease detection method based on an improved YOLOv8n model. To enhance the detection accuracy and generalization capability of the model, this paper first employs the Albumentations library for data augmentation, which strengthens the model’s adaptability to complex environments by enriching the diversity of training samples. Secondly, in terms of model architecture, a Bidirectional Feature Pyramid Network (BiFPN) module is introduced to replace the original feature extraction network. Through bidirectional multi-scale feature fusion, the model’s ability to distinguish pests and diseases with similar features and large scale differences is effectively improved. Meanwhile, the integration of the SimAM attention mechanism enables the model to learn information from three-dimensional channels, enhancing its perception of pest and disease features. Additionally, this paper adopts the EIOU loss function to further optimize the model’s bounding box regression, reducing the distortion of bounding boxes caused by high sample variability. The experimental results demonstrate that the improved model achieves a significant detection performance on the flax pest and disease dataset, with notable improvements in the detection accuracy and mean average precision compared to the original YOLOv8n model. Finally, this paper proposes a YOLOv8n model with a four-headed detection design, which significantly enhances the detection capability for small targets such as pests and diseases with a size of 4 × 4 pixels or larger by introducing new detection heads and optimizing feature extraction. This method not only improves the detection accuracy for flax pests and diseases but also maintains a high computational efficiency, providing effective technical support for the rapid and precise detection of flax pests and diseases and possessing an important practical application value.

Egg Characteristics Assessment as an Enabler for In-ovo Sexing Technology: a review

The culling of day-old male chicks remains a significant challenge for the egg-laying industry. While in ovo sexing technology has made strides, it has yet to achieve an optimal balance between early identification of sex at hatching, high sensitivity, and non-invasive operation. This paper aims to provide a comprehensive review of the research progress in in ovo sexing from various perspectives and to explore potential solutions in light of industrial practices and key technological bottlenecks. This study describes an efficient detection method for low-concentration samples and provides recommendations for the rapid detection of sex markers in chicken embryo allantoic fluid. Given the complexity of optical signals and the significant individual differences among eggs, the paper underscores the importance of evaluating egg characteristics in advancing in ovo sex determination technology. Based on this core concept, the use of multimodal data fusion strategies are advocated for in ovo sexing. This approach aims to deepen researchers' understanding of the complex relationship between sex labels and the high-dimensional data of breeding eggs, thereby injecting new momentum and direction into the development of this field.

Hydrogen: Production, Applications, and Challenges in Sustainable Energy Systems

Hydrogen has long been recognized for its potential as a clean, versatile energy carrier. In the context of a global push towards sustainable systems and reduced carbon emissions, hydrogen stands out due to its high energy density, flammability, and environmental benefits. Despite these advantages, the production, storage, transport, and utilization of hydrogen present significant technical and economic challenges that must be addressed to realize its full potential. This paper delves into the comprehensive characteristics of hydrogen, focusing on its physical and chemical properties. It examines the various methods employed in hydrogen production, highlighting both traditional and innovative approaches, from steam methane reforming and coal gasification to biomass-derived hydrogen and water splitting, along with their respective advantages and drawbacks. Further, the paper explores key areas of hydrogen application, emphasizing its potential to revolutionize the industrial and energy sectors through hydrogen fuel cells, engines, and industrial feedstock roles. Finally, the paper addresses the current challenges in adopting hydrogen energy on a large scale, particularly the high costs and technological bottlenecks in storage and transportation. Understanding these facets is crucial for advancing hydrogen as a cornerstone of sustainable energy systems.

An analysis and reliability-based optimization design method of trajectory accuracy for industrial robots considering parametric uncertainties

To address the challenges of poor trajectory accuracy in industrial robots, which has emerged as a technological bottleneck hindering further robots’ applications in high-precision manufacturing industries, this paper proposes a method for the analysis and reliability-based optimization design for industrial robots’ trajectory accuracy considering parametric uncertainties. Firstly, the dynamic equation of an articulated industrial robot with six degrees of freedom is derived, incorporating the Stribeck joint friction model, followed by the uncertain parameter identification of this dynamic model. Subsequently, an uncertainty simulation system for the robot is established based on the constructed dynamic model and the sensitivity of system uncertain parameters to the robot trajectory accuracy is analyzed, where 10 key parameters are obtained among 54 uncertain parameters. Finally, a reliability-based multi-objective optimization design methodology is proposed synthesizing the robot trajectory accuracy, manufacturing cost, and quality loss, to achieve tolerance design of the robot's parameters, and enables minimizing costs and quality losses while ensuring the robot's trajectory accuracy reliability. The performance and practicality of the proposed method were validated using a six-degree-of-freedom rotary joint serial industrial robot as an example.

Progress in Zero-gravity Unloading Test and System Research of Solar Wing

Aim: The solar wing undertakes the important task of providing energy for spacecraft. The deployment test of the solar wing in a zero gravity environment using a zero gravity system on the ground is an important link to ensure the safe and stable deployment of the solar wing in space. This article briefly describes the development process and research progress of solar wings, introduces the ground zero gravity test methods and test platforms for solar wings, including some patents for improving the ground zero gravity test platform for solar wings, and points out some urgent problems that need to be solved in the zero gravity unloading test and system at present. Background: The solar wing needs to be deployed in the weightlessness environment of space before working. Objective: The objective of this study is to summarize the zero gravity experimental methods and systems of solar wings, introduce their categories, characteristics, and development. Methods: This article summarizes various scientific research achievements in the zero gravity test of solar wings, and introduces the advantages and disadvantages of the zero gravity test method of solar wings. Conclusion: Although research on zero-gravity unloading test systems for solar wings is continuously advancing, there are still several technological bottlenecks that need to be overcome in terms of system precision, payload capacity, dynamic simulation, method optimization, diversifying zero-gravity test methods and system universality for different types of solar panel unloading.

Mini-LED Backlight: Advances and Future Perspectives

Miniaturized-light-emitting diode (mini-LED) backlights have emerged as the state-of-the-art technology for liquid crystal display (LCD), facilitating the improvement in a high dynamic range (HDR) and power saving. The local dimming technology divides the backlight into several dimming zones. Employing mini-LEDs, whose size ranges from 100 to 200 μm, as the light sources can enlarge the number of zones in the local dimming backlight, fulfilling the requirement for HDR. However, the halo effect still acts as one of the primary technological bottlenecks for mini-LED backlights. In this review, packaging technology of LEDs, color conversion, and the driving scheme of mini-LED backlights have been discussed. The strategies to reduce optical crosstalk in adjacent areas by various improved optical structures or to suppress the halo effect of LCDs by mini-LED backlights are summarized. The development trends of mini-LED backlights are also discussed.

Work together, walk forward: Unveiling collaborative innovation networks and mapping paths to breakthrough innovation in electric vehicles

The electric vehicle (EV) industry has made significant progress in practice, yet persistent technology bottlenecks impede its continuous progress. Collaborative innovation has emerged as a promising strategy to tackle these bottlenecks. To explore collaborative innovation within the EV sector, this study constructed EV collaborative innovation networks using patent data from 2009 to 2023 and applied the quadratic assignment procedure (QAP) to explore the internal mechanism of EV collaborative innovation networks. Furthermore, fuzzy-set qualitative comparative analysis (fsQCA) was employed to investigate the configurations for the realization of breakthrough innovation. The results show that (1) state-owned enterprises, along with universities and research institutions are crucial within the EV collaborative innovation network. Among them, state-owned enterprises play the most critical role. (2) Geographical proximity and institutional proximity positively impact the formation of EV collaborative innovation networks, while the effects of organizational proximity are insignificant. (3) Two pathways are identified for the realization of breakthrough innovation of innovation actors, with all proximity variables acting as core conditions in these pathways. This research engages in a dialogue between collaborative innovation networks and breakthrough innovation and enriches relevant theories and literature on this topic. Besides, this study provides practical implications for innovation actors to select collaborative partners and take tailored pathways for the realization of breakthrough innovations in the EV industry.

The Impact of Industrial Robots on Green Total Factor Energy Efficiency: Empirical Evidence from Chinese Cities

Improving energy utilization efficiency is a crucial means to achieve energy conservation, emission reduction, and green development. At present, to establish a high-quality development framework and satisfy the growing need for a better life among all its people, China must steadfastly pursue the path of green development. Although China’s substantial economic scale and achievements in ecological civilization construction provide favorable conditions for green transformation, there remains a significant gap compared to developed countries in the application of green and clean technologies. Confronted with technological bottlenecks, leveraging emerging technologies such as industrial robots from the new round of scientific and technological revolutions to improve the green total factor energy efficiency (GTFEE) is of critical importance to China’s green development. This study explores the potential impact of industrial robots on enhancing China’s GTFEE. It begins by reviewing the current research landscape in this field, highlighting its shortcomings, and theorizing potential impact pathways of industrial robots. Subsequently, the paper analyzes data from 2010 to 2019 on the usage of industrial robots and GTFEE across 276 cities at the prefectural level or above in China. Through empirical regression models that incorporate control variables and interaction terms, the study investigates the specific impacts of industrial robots on energy efficiency and their mechanisms of action. The results indicate that industrial robots significantly enhance the GTFEE of Chinese cities, especially in the Northeastern region. Industrial robots notably improve the GTFEE in resource-based cities, old industrial bases, and low-carbon pilot cities. Additionally, robots indirectly boost GTFEE by increasing labor productivity. Enhanced levels of green innovation and environmental regulations also positively moderate the effectiveness of industrial robots in improving energy efficiency. The findings of this research can assist local government agencies in coordinating and implementing policies that are conducive to green development, making better use of industrial robots to serve the people, and are of significant importance for promoting the transformation of China’s economy and society towards high-quality development.

Smart City Aquaculture: AI-Driven Fry Sorting and Identification Model

The development of smart agriculture has become a critical issue for the future of smart cities, with large-scale management of aquaculture posing numerous challenges. Particularly in the fish farming industry, producing single-sex fingerlings (especially male fingerlings) is crucial for enhancing rearing efficiency and could even provide key support in addressing future global food demands. However, traditional methods of manually selecting the gender of broodfish rely heavily on experienced technicians, are labor-intensive and time-consuming, and present significant bottlenecks in improving production efficiency, thus limiting the capacity and sustainable development potential of fish farms. In response to this situation, this study has developed an intelligent identification system based on the You Only Look Once (YOLO) artificial intelligence (AI) model, specifically designed for analyzing secondary sexual characteristics and gender screening in farmed fish. Through this system, farmers can quickly photograph the fish’s cloaca using a mobile phone, and AI technology is then used to perform real-time gender identification. The study involved two phases of training with different sample sets: in the first phase, the AI model was trained on a single batch of images with varying parameter conditions. In the second phase, additional sample data were introduced to improve generalization. The results of the study show that the system achieved an identification accuracy of over 95% even in complex farming environments, significantly reducing the labor costs and physical strain associated with traditional screening operations and greatly improving the production efficiency of breeding facilities. This research not only has the potential to overcome existing technological bottlenecks but also may become an essential tool for smart aquaculture. As the system continues to be refined, it is expected to be applicable across the entire life cycle management of fish, including gender screening during the growth phase, thereby enabling a more efficient production and management model. This not only provides an opportunity for technological upgrades in the aquaculture industry but also promotes the sustainable development of aquaculture. The smart aquaculture solution proposed in this study demonstrates the immense potential of applying AI technology to the aquaculture industry and offers strong support for global food security and the construction of smart cities.

QEYSSat 2.0 - White Paper on Satellite-based Quantum Communication Missions in Canada

We present the white paper developed during the QEYSSat 2.0 study, which was undertaken between June 2021 and March 2022. The study objective was to establish a technology road-map for a Canada-wide quantum network enabled by satellites. We survey the state-of-art in quantum communication technologies, identify the main applications and architectures, review the technical readiness levels and technology bottlenecks and identify a future mission scenario. We report the findings of a dedicated one-day workshop that included Canadian stakeholders from government, industry and academia to gather inputs and insights for the applications and technical road-map. We also provide an overview of the Quantum EncrYption and Science Satellite (QEYSSat) mission expected to launch in 2024-2025 and its anticipated outcomes. One of the main outcomes of this study is that developing the main elements for a Canada-wide quantum internet will have the highest level of impact, which includes Canada-wide entanglement distribution and teleportation. We present and analyze a possible future mission ('QEYSSat 2.0') that would enable a long range quantum teleportation across Canada as an important step towards this vision.

A review on recent progress of refractory high entropy alloys: From fundamental research to engineering applications

Refractory high-entropy alloys (RHEAs) have emerged as frontier materials for high-temperature structural applications due to their exceptional mechanical properties and thermal stability. This review aims to provide a comprehensive and in-depth summary of the latest research progress in the field of RHEAs. By systematically analyzing 253 publications since 2022, this review presents a panoramic overview of the current research status in the field of RHEAs, covering aspects from alloy design, microstructure, processing techniques, mechanical behavior, to physicochemical properties. Key strengthening and toughening mechanisms, such as solid solution strengthening, precipitation strengthening, and grain boundary strengthening, are extensively analyzed. The high-temperature mechanical performance, oxidation resistance, and adaptability of RHEAs in extreme environments including corrosion and irradiation, are critically reviewed, and the potential application value of these research findings in aerospace, nuclear energy, biomedical, and other fields is discussed. Simultaneously, the multidisciplinary characteristics of RHEAs research has revealed the trend of the field evolving from fundamental research to practical applications. Furthermore, with the aid of advanced characterization techniques and computational methods, the review elucidates the controlling effects of chemical short-range order, defect structures, and other factors on the performance evolution of RHEAs, highlighting the importance of multi-principal element synergistic effects. Based on summarizing the key scientific issues and technological bottlenecks faced by RHEAs, the article provides a forward-looking perspective on future research directions, emphasizing development strategies that integrate computation and experiments, and accelerate the transformation of fundamental research into engineering applications, thus providing insights and guidance for developing a new generation of high-performance RHEAs.

Technological Advancements and Prospects for Near-Zero-Discharge Treatment of Semi-Coking Wastewater

This review examines the technological bottlenecks, potential solutions, and future development directions in the treatment and resource utilization of semi-coking wastewater (SCOW) in China. By comprehensively investigating the semi-coking industry and analyzing wastewater treatment research hotspots and existing projects, this study systematically explores the current status and challenges of each treatment unit, emphasizing the necessity for innovative wastewater treatment technologies that offer high efficiency, engineering feasibility, environmental friendliness, and effective resource recovery. This review highlights prospects and recommendations, including the development of novel extractants for phenol and ammonia recovery, a deeper understanding of biological enhancement mechanisms, exogenous bio-enhancement materials, and the creation of cost-effective advanced oxidation process (AOP)-based combined processes. Additionally, it underscores the potential for repurposing SCOW as a valuable resource through appropriate treatment, whether recycling for production or other applications.

Has R&D contributed to productivity growth in China? The role of basic, applied and experimental R&D

Since the early 2000s, R&D expenditure in China has increased rapidly, with the country having the third highest R&D expenditure, next only to the OECD and USA, and having surpassed Japan since 2009. Furthermore, policies in China in recent years have emphasized the need to conduct more basic and applied R&D in order to overcome technological bottlenecks and risks in access to advanced technologies when faced with international geopolitical tension. However, in the meantime, the trend in measured total factor productivity (TFP) in China has been downward since 2010. A lack of TFP growth despite the significant investment in R&D raises the question of the impact of R&D expenditure on productivity growth in China. Therefore, this study aims to investigate this issue and estimate the effects of three types of R&D stocks (basic, applied and experimental R&D stock) on TFP in China, using newly constructed provincial panel data in China from 1998 to 2018. Various empirical models and control variables are adopted to take into account non-stationarity and spatial spill-over of the provincial R&D stock values over time. The analysis results are robust to various specifications and reveal a significant positive effect of overall R&D and experimental R&D on TFP in China, but basic R&D exerts no significant results, and the effects of applied R&D are mixed across specifications. Further analyses using the 1991–2018 national data demonstrated largely consistent results. These results suggest that experimental R&D has been crucial for enhancing TFP growth in China during the decades investigated, but evidence of basic and applied R&D driving TFP growth in China is lacking.

Sustainable Production of Ulva Oligosaccharides via Enzymatic Hydrolysis: A Review on Ulvan Lyase.

Ulvan is a water-soluble sulfated polysaccharide extracted from the green algae cell wall. Compared with polysaccharides, oligosaccharides have drawn increasing attention in various industries due to their enhanced biocompatibility and solubility. Ulvan lyase degrades polysaccharides into low molecular weight oligosaccharides through the β-elimination mechanism. The elucidation of the structure, catalytic mechanism, and molecular modification of ulvan lyase will be helpful to obtain high value-added products from marine biomass resources, as well as reduce environmental pollution caused by the eutrophication of green algae. This review summarizes the structure and bioactivity of ulvan, the microbial origin of ulvan lyase, as well as its sequence, three-dimensional structure, and enzymatic mechanism. In addition, the molecular modification of ulvan lyase, prospects and challenges in the application of enzymatic methods to prepare oligosaccharides are also discussed. It provides information for the preparation of bioactive Ulva oligosaccharides through enzymatic hydrolysis, the technological bottlenecks, and possible solutions to address these issues within the enzymatic process.

Nucleated synthetic cells with genetically driven intercompartment communication

Eukaryotic cells are characterized by multiple chemically distinct compartments, one of the most notable being the nucleus. Within these compartments, there is a continuous exchange of information, chemicals, and signaling molecules, essential for coordinating and regulating cellular activities. One of the main goals of bottom-up synthetic biology is to enhance the complexity of synthetic cells by establishing functional compartmentalization. There is a need to mimic autonomous signaling between compartments, which in living cells, is often regulated at the genetic level within the nucleus. This advancement is key to unlocking the potential of synthetic cells as cell models and as microdevices in biotechnology. However, a technological bottleneck exists preventing the creation of synthetic cells with a defined nucleus-like compartment capable of genetically programmed intercompartment signaling events. Here, we present an approach for creating synthetic cells with distinct nucleus-like compartments that can encapsulate different biochemical mixtures in discrete compartments. Our system enables in situ protein expression of membrane proteins, enabling autonomous chemical communication between nuclear and cytoplasmic compartments, leading to downstream activation of enzymatic pathways within the cell.

Deep learning-based state of charge estimation for electric vehicle batteries: Overcoming technological bottlenecks

This study presents a novel deep learning-based approach for the State of Charge (SOC) estimation of electric vehicle (EV) batteries, addressing critical challenges in battery management and enhancing EV efficiency. Unlike conventional methods, our research leverages a diverse dataset encompassing environmental factors (e.g., temperature, altitude), vehicle parameters (e.g., speed, throttle), and battery attributes (e.g., voltage, current, temperature) to train a sophisticated deep learning model. The key novelty of our approach lies in its integration of real-world driving data from a BMW i3 EV, enabling the model to capture the intricate dynamics affecting SOC with remarkable accuracy. We conducted 72 tests using actual driving trip data, which included 25 types of environmental variables, to validate the feasibility and effectiveness of our proposed model. The deep learning network, designed specifically for SOC estimation, outperformed traditional models by demonstrating superior accuracy and reliability in predicting SOC values. Our findings indicate a significant advancement in SOC estimation techniques, offering actionable insights for both policymakers and industry practitioners aimed at fostering energy conservation, carbon reduction, and the development of more efficient EVs. The study's major contribution is its demonstrated capability to improve SOC estimation accuracy by understanding the complex interrelationships among various influencing factors, thereby addressing a pivotal challenge in EV battery management. By employing cutting-edge deep learning techniques, this research not only marks a significant leap forward from traditional SOC estimation methods but also contributes to the broader goals of sustainable transportation and environmental protection.

Model-based hybrid control of combined active–passive vertical zero-gravity system

Ground-based full-scale physical experiments on spacecraft have become an essential part of ground simulations. However, achieving a vertical translational degree of freedom for zero-gravity simulation has become a key technological bottleneck for six-degree-of-freedom ground simulations. The combined active–passive vertical zero-gravity system discussed in this paper can achieve zero-gravity simulation in the vertical direction; however, it exhibits deficiencies such as inaccurate modeling, low control precision, and significant control delays. Addressing the issue of inaccurate modeling, this study uses interpretable ODE neural networks to identify the dynamic model of the system. For resolving the problems of poor control precision and significant delays, this study proposes a hybrid control algorithm based on the ODE dynamic model. The controller includes nominal model feedforward, PID feedback, and learning-based feedforward components. Finally, comparative experiments are designed to validate the proposed model-based hybrid control algorithm. Compared to PID-Feedforward controller, the hybrid controller exhibits a higher response speed, while compared to model-based feedforward controller, it exhibits a better control accuracy. Therefore, the proposed hybrid control algorithm based on the ODE dynamic model effectively enhances the control response speed and improves the control precision of the system.

Carbon Dots in the Pathological Microenvironment: ROS Producers or Scavengers?

Reactive oxygen species (ROS), as metabolic byproducts, play pivotal role in physiological and pathological processes. Recently, studies on the regulation of ROS levels for disease treatments have attracted extensive attention, mainly involving the ROS-induced toxicity therapy mediated by ROS producers and antioxidant therapy by ROS scavengers. Nanotechnology advancements have led to the development of numerous nanomaterials with ROS-modulating capabilities, among which carbon dots (CDs) standing out as noteworthy ROS-modulating nanomedicines own their distinctive physicochemical properties, high stability, and excellent biocompatibility. Despite progress in treating ROS-related diseases based on CDs, critical issues such as rational design principles for their regulation remain underexplored. The primary cause of these issues may stem from the intricate amalgamation of core structure, defects, and surface states, inherent to CDs, which poses challenges in establishing a consistent generalization. This review succinctly summarizes the recently progress of ROS-modulated approaches using CDs in disease treatment. Specifically, it investigates established therapeutic strategies based on CDs-regulated ROS, emphasizing the interplay between intrinsic structure and ROS generation or scavenging ability. The conclusion raises several unresolved key scientific issues and prominent technological bottlenecks, and explores future perspectives for the comprehensive development of CDs-based ROS-modulating therapy.