Ammonia is gaining attention as an efficient hydrogen energy carrier and a decarbonized fuel. In Japan, the development of technologies to use ammonia as a fuel began in the 2010s, focusing on power generation and industrial heating systems. Today, its application is being explored across various sectors worldwide. For gas turbine power generation, IHI has developed a 2 MW gas turbine capable of operating on 100% liquid ammonia and is conducting long-term durability tests within a power generation package system. The application of this technology to large gas turbines is also underway. In the field of boiler thermal power generation, ammonia co-firing technology with a 20% ammonia fuel ratio has been developed and successfully demonstrated in a 1000 MW commercial power plant. This paper introduces the current status and prospects of ammonia firing power generation technologies.
Cement production accounts for 5%-8% of global CO2 emissions, prompting industry interest in carbonation—the natural reabsorption of atmospheric CO2 by concrete—as a climate mitigation strategy. Recent studies suggest carbonation could offset approximately 50% of process emissions, positioning concrete infrastructure as vast carbon reservoirs. However, systematic analysis reveals fundamental limitations challenging this assumption. Cement production generates concentrated CO2 pulses during manufacturing while carbonation proceeds slowly through diffusion-limited processes spanning decades, creating critical temporal asymmetry. When properly accounted for through time-adjusted climate assessments, this mismatch reduces claimed benefits by 30%-60% compared to conventional global warming potential calculations. Moreover, synthesis of published experimental data across 99 scenarios demonstrates that 52% exhibit less than 50% probability of achieving net emission reductions, with compressive strength penalties often requiring additional binder use that erodes nominal carbon gains. Critically, this perspective exposes three systematic failures in current climate accounting: ① temporal frameworks treating decades-delayed absorption as equivalent to immediate emission avoidance, ② selective reporting obscuring widespread performance failures, and ③ policy prioritization allocating resources to slow, uncertain processes while proven alternatives remain underutilized. By integrating sector-scale projections, lifecycle timing analyses, and comprehensive performance distributions under consistent boundaries, this cross-study synthesis reveals patterns invisible when research remains fragmented—establishing evidence-based hierarchies for near-term decarbonization. In contrast, proven alternatives demonstrate superior performance: supplementary cementitious materials offer 11%-34% emission reductions through direct clinker substitution, structural design optimization achieves 18.5% reductions without compromising safety, and service life extension strategies enable 75% total reduction potential by 2100—far exceeding carbonation-dependent pathways. Consequently, while carbonation remains chemically viable, its slow kinetics, performance uncertainty, and temporal misalignment with climate targets necessitate policy recalibration prioritizing transparent temporal accounting and proven alternatives over uncertain future absorption processes.
With the rapid expansion of renewable energy systems, particularly wind and solar energy, coal-fired power plants (CFPPs) are expected to serve as flexible and dispatchable backup resources. This evolving role imposes new demands on their operational adaptability, efficiency, and intelligence. In this context, the intelligent transformation of CFPPs has become a key enabler for achieving both flexible operations and long-term sustainability. This paper provides a comprehensive review of the latest developments in intelligent coal-fired power technologies, focusing on three critical pillars: intelligent perception, intelligent control, and intelligent operation. Key enabling technologies, such as ubiquitous sensing systems, advanced control algorithms, and automated operation platforms, are examined in detail. Additionally, two representative engineering cases are introduced to demonstrate practical applications and benefits: the intelligent control of coal-fired units coupled with novel energy-storage systems and the implementation of unmanned operation in smart power plants. These projects highlight the transformative potential of intelligent technologies in enhancing the flexibility, efficiency, and autonomy of coal-fired power units. Finally, future perspectives on intelligent technologies are presented. The findings of this study offer valuable insights into the pathway toward clean, flexible, and intelligent coal-based power generation in an evolving energy landscape.
Biofuels are promising alternatives to fossil fuels due to diminishing reserves and increasing environmental concerns. This review focuses on recent progress in understanding the combustion kinetics of oxygenated biofuels derived from biomass. The review begins with fundamental concepts and research methodologies in reaction kinetics, intended as a primer for engineering researchers. Subsequently, kinetic studies from the past decade on typical oxygenated biofuels are summarized, including alcohols, fatty acid methyl esters (FAMEs), ketones, ethers, and carbonates. Emphasis is placed on the influence of different oxygenated functionalities and their positions within the molecule on combustion characteristics and reaction pathways. Distinct reaction patterns for each class are highlighted. Alcohols exhibit a characteristic unimolecular dehydration reaction. FAME kinetics are similar to long-chain hydrocarbons, with unsaturation significantly impacting low-temperature oxidation. Ketone oxidation is influenced by the formation of resonance-stabilized radicals, while straight-chain ethers demonstrate a unique double negative temperature coefficient (NTC) behavior. Carbonates, relevant to lithium-ion battery safety, have gained research attention and can undergo a distinctive reaction pathway identified as CO2 elimination reaction. To advance predictive kinetic models for biomass-derived oxygenated fuels, several targeted research directions are essential. First, there is a critical need to expand experimental datasets that capture the combustion behavior of diverse oxygenated compounds, particularly under low-temperature conditions. This must be coupled with enhanced combustion diagnostics capable of resolving key reaction intermediates characteristic of oxygenated fuel oxidation. Second, detailed quantum chemical calculations and theoretical explorations of potential energy surfaces are required to accurately determine reaction rate parameters for oxygen-involved pathways, which are often determinant in fuel decomposition and pollutant formation. Finally, progress in model predictability will depend on the adoption of advanced computational methods, including automated mechanism generation for complex oxygenated structures, systematic optimization frameworks leveraging experimental data, and the incorporation of physics-informed artificial intelligence approaches tailored to oxygenated fuel chemistries.
This comprehensive review examines the application of liquid-ammonia injection and combustion in engine systems, highlighting the potential of liquid ammonia as a carbon-neutral fuel alternative. The study synthesizes recent advancements in liquid-ammonia injection and combustion technologies, addressing critical domains such as fundamental fuel properties, injection and spray dynamics, combustion behavior, and engine performance. Key challenges are identified, including ammonia’s high latent heat of vaporization, slow flame-propagation speed, narrow flammability range, and elevated NOx emissions, while emphasizing the need for optimized injection strategies and nozzle designs to enhance atomization and mixing. The research findings indicate that liquid-ammonia injection can significantly reduce greenhouse gas emissions, with dual-fuel modes (e.g., ammonia-diesel) proving effective in overcoming ammonia’s low reactivity. Studies show that both low-pressure and high-pressure dual fuel-injection modes can achieve substantial emission reductions, with high-pressure injections offering better thermal efficiency and lower NOx emissions. Innovative approaches, such as turbulent jet ignition, stratified fuel injection, and hydrogen co-injection, have been explored to improve ignition efficiency and combustion stability. Future research should prioritize the development of integrated solutions that combine advanced combustion technologies, optimized engine designs, and effective emission-control strategies. Collaboration between academia, industry, and policymakers will be crucial in driving the adoption of ammonia as a sustainable fuel alternative.
In the energy transition context, there is growing interest in thermochemical catalytic processes for producing synthetic renewable hydrocarbons. These include biomass gasification followed by syngas conversion, or CO2 capture from flue gases and subsequent hydrogenation—known as carbon capture and utilization (CCU). The latter uses excess renewable electricity to generate green hydrogen via water electrolysis, a concept called Power-to-Fuel. A recently proposed approach, sorption-enhanced hydrogenation, applies Le Chatelier’s principle to improve reaction efficiency by selectively removing steam with a suitable sorbent. By locally adsorbing water, the system shifts equilibrium toward desired products, enabling effective hydrogenation at relatively low pressures. The key challenge is developing materials that adsorb water under relevant operating conditions yet can be regenerated without degrading the catalyst or consuming excessive energy. Most research so far has focused on fixed-bed reactors, which are simple and compact but require intermittent operation for sorbent regeneration and face heat management challenges at larger scale. In contrast, chemical looping systems using coupled fluidized beds can offer continuous operation, easier heat control, and effective sorbent regeneration. This review summarizes both early and recent developments in sorption-enhanced catalytic hydrogenation of carbon oxides into products such as methane, methanol, dimethyl ether, and carbon monoxide (via the reverse water-gas shift reaction). It covers experimental and modeling studies, and highlights key challenges and research directions for scaling up this promising technology to commercial levels.
As a carbon-free carrier for renewable energies, hydrogen has the potential to contribute to the success of the energy transition. In addition to electrochemical applications, thermochemical applications will continue to play an important role in high-performance energy conversion such as advanced low-emission combustion systems. However, the combustion of hydrogen poses challenges due to its special thermophysical and reaction kinetic properties. Lean combustion is required to minimize primary nitrogen oxide (NOx) formation. This can lead to thermodiffusive instabilities that affect the internal structures of the reaction zone, the fuel consumption rate, the local equivalence ratios, and the local gas temperatures, thereby affecting primary NOx formation. The thermodiffusive instabilities have long been known and have been extensively described, primarily through theoretical studies and numerical simulations for simple combustion systems. However, their interaction with turbulence in practical combustion environments remains relatively unexplored, particularly in the context of complex, real-world technical applications. There are few experimental data quantifying the influence of thermodiffusive instabilities on the internal flame structure with respect to the turbulence level. Therefore, the aim of this review is to summarize recent experiments to quantitatively describe the interaction between thermodiffusive instabilities and turbulence. Combustion systems of increasing complexity are considered using laser-optical measurement techniques for elucidating local flame properties. While Raman/Rayleigh spectroscopy is used to quantitatively resolve internal flame structures for unconfined combustion systems, this is not easily possible for enclosed systems under pressure. Instead, the extent to which the reaction zone is affected by thermodiffusive instabilities in interaction with the turbulent flow field is quantitatively assessed using information from laser-induced fluorescence measurements. Consistent with all configurations presented here, the ratio of diffusive to convective time scales plays a critical role in the significance of thermodiffusive instabilities.
Recognizing the benefits of pressurization and fuel staging on the efficiency of oxy-combustion, the staged, pressurized oxy-combustion (SPOC) process was introduced in 2012. The combination of fuel staging and pressurized oxy-combustion results in a more compact plant, a higher plant efficiency and reduced costs for pollutant and greenhouse gas removal compared with plants equipped with conventional carbon capture. This approach to power generation enables a modular boiler design and optimizes the plant for flexible operation, which is essential to meet the demands of the modern grid when it contains intermittent power sources. Originally designed to burn coal, the SPOC process is well-suited for biomass because the combustion of biomass leads to a high moisture content in the flue gas and the SPOC process is able to recover the latent heat of this moisture, enhancing system performance over that of traditional biomass combustion at atmospheric pressure. The present work is focused on evaluating the potential for utilizing the SPOC process in retrofit applications wherein the boilers of an existing plant are replaced with the SPOC process, and woody biomass is used as the fuel to yield carbon-negative power. Two applications are considered: power generation and cogeneration (heat and power). Modeling these systems in Aspen Plus demonstrates that the SPOC process surpasses the performance of baseline plants with post-combustion capture (PCC) for both power generation and cogeneration. Specifically, compared to a PCC equipped plant, the SPOC power plant has 33% higher efficiency, and the SPOC cogeneration plant reaches 42% higher net energy. Experimentally, the existing SPOC facility was fired for the first time with 100% biomass and after minor improvements were made to the feeding system, the facility demonstrated excellent performance during startup, steady-state operation and turndown.
A 5-MWth chemical looping combustion (CLC) unit was designed, built, operated, and demonstrated in China as part of the Chinese-European Emission-Reducing Solutions (CHEERS) project, funded by China’s Ministry of Science and Technology (MOST) and the European Union (EU)’s Horizon 2020. In the configuration designed by the Chinese partners, the air reactor (AR) is a transport bed, while the fuel reactor (FR) is a bubbling/turbulent fluidized bed. The solid circulation between the FR and AR is regulated by the overflow method, and the oxygen carrier (OC) from the AR cyclone returns to the FR riser. From June to September 2024, the 5-MWth demonstration unit was operated and tested more or less continuously, with a thermal input ranging from 3.5 to 5.0 MWth. During the operation, all solid fuel was fed into the dense bed of the FR, while only air was introduced into the AR. No electric or other external heating was applied, meaning that the whole pilot unit was heated by the oxidation of the OC within the AR. Hence, auto-thermal CLC operation was successfully achieved. Heating the unit was completed in 48 h; furthermore, switching to CLC mode was straightforward and took less than 1 h. During the operation, the temperature of the entire loop was stable. The temperatures of the AR and FR were 1000-1040 °C and 940-980 °C, respectively. Based on the operational data, the maximum CO2 capture efficiency of the lignite-fed CLC unit was greater than 97%, and the minimum oxygen demand for unburnt gases from the FR was 2.45%. This work bridges the gap between lab-scale research and industrial applications in the field of CLC.
To meet the demand for clean and efficient coal utilization under low-load conditions and new power systems, an innovative coal purification-combustion technology is proposed in this study. The feasibility and fuel adaptability were verified using a 200 kW coal purification-combustion system. The high-temperature purification characteristics of three types of coal under a low load of 55% and the nitrogen migration and transformation mechanism during the purification-combustion process were studied. The results show that the medium-temperature activation process mainly involves the release and reduction of volatile nitrogen to N2, with a nitrogen conversion rate of 43.8%-53.1%. During this process, coal powder activation is achieved, which significantly increases the specific surface area of the char, develops a pore structure, and increases the number of active sites, which are beneficial for high-temperature gasification reactions under low loads. During high-temperature purification, 62%-85% of the inorganic components were separated, achieving the separation of carbon and inorganic components. Coal powder is converted into high-temperature gaseous fuel, mainly composed of CO and H2, and the pore structure of char is further developed, which is conducive to stable combustion under low loads. The high-temperature purification process mainly involves the release and reduction of char nitrogen to N2, with a nitrogen conversion rate of 93.6%-96.6%. The fuel, mainly composed of high-temperature CO and H2, achieved a moderate or intense low-oxygen dilution (MILD) combustion process. In the reduction zone of the combustion furnace, NH3 was completely converted to N2 and char nitrogen was gradually released and reduced to N2, with a nitrogen conversion rate of 99.6% in the reduction zone. The oxidation zone involves the burnout of char, which mainly releases char nitrogen and oxidizes it to NOx. Ultimately, only 0.2%-0.9% of the coal nitrogen is converted to NOx. The minimum original NOx emissions of the three types of coal at low loads were 28 mg·Nm−3 (@6% O2), and the combustion efficiency exceeded 99.6%.
In pursuit of a low-carbon energy transition, biomass and other carbon-neutral fuels are increasingly utilized in modern combustion facilities. However, controlling these systems remains challenging due to their complex geometries, dynamic interactions, and diverse operating conditions. Data-driven digital twins have emerged as powerful tools for optimizing performance and minimizing emissions in industrial combustion systems. Their core functions include reconstructing multi-physical combustion fields and predicting and optimizing key performance metrics, such as efficiency and pollutant emissions. Despite recent advancements, existing approaches typically treat reconstruction and optimization as separate tasks, limiting their efficiency and scalability. Furthermore, developing digital twins for real-world industrial applications requires extensive high-fidelity data, which is often impractical to obtain. To address these limitations, we propose the multi-field reconstruction net (MFRNet) framework, which integrates dimension expansion, variable extension, and feature fusion techniques to enhance data efficiency and predictive accuracy. Using an industrial-scale biomass grate furnace as a case study, we construct a comprehensive dataset, consisting of 288 low-fidelity 2D cases (covering eight physical fields) and 48 high-fidelity 3D cases (covering eleven physical fields). The MFRNet achieves high-precision multi-field reconstruction under complex conditions while significantly reducing the reliance on costly 3D simulations. By leveraging intermediate features pre-trained during reconstruction, the model enhances scalar predictions, notably improving CO and NO emission accuracy, even with limited high-fidelity data. The trained model is then directly applied for multi-objective optimization under varying operating conditions, demonstrating robust predictive accuracy and reliable optimization guidance. This scalable and data-efficient digital twin framework is easily adapted for other combustion systems, offering an intelligent paradigm for active control, real-time optimization, and enhanced operational efficiency in modern combustion facilities.
As the steel industry is intended to be carbon-neutral, transitional solutions are required before full-scale hydrogen-based reduction becomes viable. One such strategy is the partial replacement of pulverized coal injection (PCI) with high-quality biocarbon in blast furnace (BF) operations. Raw biomass presents challenges, such as low grindability, high ash content, and low energy density, which can be mitigated through torrefaction and carbonization. This study evaluates the combustion behavior and injection limits of four biocarbon samples (mildly torrefied biomass (MTB), hard torrefied biomass (HTB), mildly carbonized biomass (MCB), and hard carbonized biomass (HCB)) using thermogravimetric analysis (TGA), drop tube furnace (DTF), and laminar flow reactor (LFR) experiments. Results show that as biomass is carbonized, its combustion kinetics increasingly resemble those of PCI coal. Co-firing tests confirmed improved performance at higher blending ratios, especially with highly treated samples, such as HCB, due to enhanced fragmentation and char reactivity. Injection limits were determined based on combustion performance, heating value (±5 wt% of PCI coal), and ash content (< 10 wt%). The MTB and HCB exceeded these limits at approximately 27-30 wt% blending, indicating the need for an adjusted fuel input. Overall, biocarbon shows strong potential as a PCI substitute, offering a feasible low-carbon pathway for existing BF systems.
To maintain power grid stability under the increasing integration of renewable energy, the operational flexibility of thermal power plants is assuming growing significance. Flame stability and responsiveness on the combustion side under the extreme conditions of ultra-low loads and rapid load-change processes are the key to increasing the flexibility of thermal power plants. In this paper, a burner based on pre-gasification combustion technology is developed. The flexibility of the pre-gasification burner on a 5-MW pilot platform is investigated through simulation and performance verification. The results indicate that a single pre-gasification burner can maintain flame stability under a 9% load when burning bituminous coal, and a fuel load variation rate of 10% min-1 can be supported. The pre-gasification combustion has a faster stabilization rate compared with traditional combustion under coal flow and air flow disturbances. The application of pre-gasification burners in different classes of boiler is simulated, and the results indicate that the pre-gasification burner has the potential to improve the flexibility of industrial to full-scale coal-fired boilers.
In recent years, China has witnessed a surge in both its total primary energy consumption and its installed renewable energy capacity, which has had a profound impact on the nation’s carbon emissions. The future trajectories of energy consumption and renewable energy development are fraught with uncertainties, and these will critically influence the realization of China’s climate objectives, especially the goal of reaching a carbon peak. This research employs maximum likelihood estimation (MLE), in conjunction with Monte Carlo simulation and random sampling techniques, to assess the likelihood of China attaining its carbon peak and other climate targets under various scenarios. Additionally, it offers strategic policy recommendations to ensure the fulfillment of these environmental goals. In the baseline scenario, China must either surpass 4000 GW of installed non-fossil energy capacity before 2030 or maintain a total energy consumption below 6500 million tons of coal equivalent (Mtce) to align with its climate commitments. However, should the rate of reduction in energy intensity falter, leading to a total energy consumption exceeding 8250 Mtce before 2030, China may find it challenging to achieve all its climate ambitions.
The large-scale utilization of renewable energy challenges the stability and safety of the grid; thus, the flexibility of coal-fired power plants should be increased to balance unstable renewable energies. To achieve this, a heat storage system (HSS) is integrated into a power plant. This is the first study utilizing furnace flue gas to drive a molten-salt-heat-exchanger (MSHE). Compared to steam-vapor-driven MSHE, flue gas-driven technology avoids the pinch temperature limitation (PTL) and simplifies the system configuration. In this study, we demonstrate the concept, design, fabrication, and experiments of the MSHE. The novelties include: ① finned tubes to balance the thermal resistances between the flue gas side and the molten salt side; ② a weak angle design to ensure gravity-driven recession of the molten salt; and ③ a modular design to ensure even temperature distribution at the outlet of the tube bundles. A heat transfer correlation is developed for molten salt, covering a wide range of Reynolds numbers. An experimental setup is constructed to collect data and verify the effectiveness of the MSHE. The measured overall heat transfer coefficients matched the predictions well, with deviations of less than 10%. The measured heat power reached 320 kW, exceeding the 300 kW design target. We demonstrate the heat transfer between the flue gas and molten salt to compensate for the heat release from the HSS to the environment, reducing electricity consumption in the standby stage of the system. The modular design of the MSHE ensures minimal temperature deviations of < 4 K among different tubes, avoiding local overheating-induced decomposition of the molten salt. Based on the 300 kW MSHE results, a 10 MW MSHE is designed, fabricated, and integrated into a 350 megawatt electric (MWe) coal-fired plant to achieve a higher load variation rate of 6% Pe·min−1 for a coal-fired power plant.
Molten salt is widely adopted in diverse thermal energy storage systems owing to its exceptional thermodynamic properties and economical cost. As a critical component in molten salt energy storage systems, the exchangers often utilize U-tube configurations for enhanced compactness, such as shell-and-tube designs. However, the high viscosity and density of molten salt can cause non-uniform flow distribution in U-tubes, posing localized overheating risks. This study proposes a heat transfer enhancement strategy applying a twisted cloverleaf U-tube in combination with molten salt-based nanofluids (MSBNs). The effects of tube geometry, operating parameters, and nanofluid thermophysical properties on flow and thermal performance were analyzed through numerical simulations. Multi-objective optimization of operating conditions was conducted using a combination of response surface method (RSM) and the non-dominated sorting genetic algorithm II (NSGA-II). Results indicate the twisted structure and nanoparticles significantly enhance heat transfer and improve temperature uniformity, however increase pressure drop. The optimal combination achieved a peak performance evaluation criterion (PEC) value of 1.21. Inlet velocity and inlet temperature influence flow and heat transfer performance additional strongly than heat flux. Optimized operating conditions yield a maximum temperature difference of 40.15 K, pressure drop of 1979.97 Pa, and average convective heat transfer coefficient of 2781.31 W·(m2·K)−1. This work provides critical guidance for the design and operational optimization of novel MSBN heat exchange tubes.
In recent years, the replacement of retired coal-fired power plants with nuclear power plants (also known as coal-to-nuclear conversion, C2N) has been considered a particularly cost-effective solution for power system decarbonization amid global climate change mitigation goals. In this study, we improved a power system model of China equipped with provincial spatial resolution. Specifically, we expanded the classification of nuclear technologies from one to four types, based on generation and reactor design, and incorporated relevant C2N conversion constraints. This improvement allows quantification of C2N’s potential role in decarbonizing China’s power system, following the identification of its maximum conversion potential. The results indicate that by utilizing conventional site resources in both coastal and inland China, a major growth of nuclear capacity is possible under China’s carbon peaking and neutrality goals, reaching 422 GW by 2060, with 42% of this capacity being small modular reactors that offer greater operational flexibility. In 2060, nuclear power will become an important source of electricity generation in China, accounting for 18% of total supply. Site resource availability represents a major constraint to this development: Expanding site availability through C2N has the potential to further increase nuclear capacity in 2060 by 13%-23%, while raising nuclear’s share of total electricity supply that year by 2-4 percentage points. Expanding nuclear energy’s share in China’s decarbonization via C2N will yield cost savings of 0.22%-0.69% of system cost from 2030 to 2060.
Despite the advantages of high effluent quality and small footprint, membrane bioreactor (MBR) technology faces challenges in sustainable development due to energy consumption and membrane fouling. Weighing the advantages and disadvantages of MBRs requires a comprehensive assessment from techno-economic-environmental perspectives. In this paper, we reviewed the related research on MBRs from three aspects: economic cost analysis, environmental impact assessment, and comprehensive techno-economic-environmental assessment. The aim of this paper is to understand the sustainable development performance of MBRs, and to review the current status of the application of multiple techno-economic-environmental assessment methods in the field of wastewater treatment. The currently available results of the economic cost analysis of MBRs showed that the operating cost and energy consumption of MBRs are higher than those of other wastewater treatment processes if MBRs’ potential benefit of smaller footprint is not taken into account. The results of the environmental impact assessment showed that MBRs have a positive environmental impact due to high quality effluent, although global warming potential limits the sustainability of MBRs to some extent. Combined techno-economic-environmental assessment showed that MBRs are economically feasible and technically efficient, while their sustainability is controversial. Given the rapid development of MBR technology, these results may evolve as new advancements are made. In addition, there is room for improvement in the existing literature regarding the reliability and comparability of results, as well as the applicability of the methods, particularly in defining the accounting scope, clarifying model assumptions, and considering discounting.
As the most fundamental organic unit, the methyl group is ubiquitously present yet frequently overlooked in various insecticide architectures. Despite its simplicity, this moiety plays a pivotal role in insecticide discovery. This perspective highlights documented cases of popular insecticides in which methyl substitution increases target affinity and bioactivity, alongside an analysis of the underlying molecular mechanisms. We propose insights into currently unsolved issues and future directions for leveraging methyl incorporation to accelerate the discovery of new agrochemicals. To our knowledge, this constitutes the first comprehensive perspective on the functional significance of methyl groups in agricultural chemistry. We expect this work to inspire methyl-driven optimization strategies for next-generation insecticides, thereby contributing to sustainable pest management.
Nonlinear analyses possess tremendous significance throughout the entire lifespans of civil structures. In recent years, the interest in leveraging deep learning (DL) to address the efficiency limitations of the traditional structural analysis methods has increased. However, full-range nonlinear analyses of different structures remain underresearched because of a lack of appropriate data representations and the failure to consider both internal structural information and external load conditions. A heterogeneous graph (HetG) representation scheme that can digitalize arbitrary structural systems with high fidelity is proposed in this study. Furthermore, a composite feature learning framework is developed to enable efficient full-range nonlinear analyses. This framework comprises two main components: ① a heterogeneous graph neural network (GNN)-based module that encodes static features into embeddings with full structural semantics and ② a sequence-to-sequence (Seq2Seq) module that predicts history-dependent responses using structural embeddings and external stimuli in an end-to-end manner. A computational model named structural analysis based on a graph neural network-nonlinear (StructGNN-N) is implemented based on the proposed methodology and is validated through numerical experiments involving real-world concrete structures. The results show that StructGNN-N successfully reproduces the full-range nonlinear responses of all nodes in the entire structure and exhibits excellent generalizability across structures with diverse topological designs and member configurations. Notably, the developed model achieves a computational efficiency level that is 1000 times greater than that of the traditional elastoplastic history analysis approach using the finite-element (FE) method. A parametric analysis and ablation studies demonstrate the effectiveness of the StructGNN-N architecture. Due to its superior accuracy and computational efficiency, the proposed method holds great potential for use in engineering applications, especially in the context of digital twins. This approach provides an inspiring path for simulating diverse engineering structures with accurate and comprehensive mechanical information in real time.
Autonomous driving depends on successful interactions among humans, vehicles, and roads. However, people often lack an understanding of autonomous vehicle (AV) behaviours and decisions. Moreover, AVs have difficulty aligning with human intentions in their interactions. To overcome the obstacles associated with the absence of interactive intelligence, especially in complex and uncertain environments, we introduce the concept of embodied interactive intelligence towards autonomous driving (EIIAD), which establishes representation and learning methods aligned with the physical world, enhancing human-machine integration. Building on this concept, we propose an end-to-end unified constrained vehicle environment interaction (UniCVE) model, which involves the construction of an end-to-end perception-cognition-behaviour closed-loop feedback paradigm and continuous learning through accumulated split driving scenarios. This model realizes interaction cognition through networks designed for pedestrians and vehicles, and it unifies the cognition as a value network of AVs to generate socially compatible behaviours. The UniCVE model is implemented on Dongfeng autonomous buses, which have successfully travelled 22 thousand kilometres and completed 45 thousand navigation tasks in Xiong’an New Area, China, demonstrating its general applicability in various driving scenarios. In addition, we highlight the high-level interactive intelligence of the UniCVE model in selected simulated complex interaction scenarios, demonstrating that it makes AVs more intelligent, more reliable, and more attuned to human relationships. Furthermore, the UniCVE model’s capacity for self-learning and self-growth allows it to infinitely approximate true intelligence, even with limited experience.
Terahertz communication technology is envisioned as a promising candidate for the pivotal spectrum technology in future wireless communication networks. However, the limited penetration ability of terahertz waves makes line-of-sight (LoS) transmission indispensable, hindering the extensive application of terahertz communications. In this work, a novel liquid-crystal programmable metasurface (LCPM) is proposed for the first time, which can effectively achieve dual-broadband beam manipulation to improve link stability and extend coverage for terahertz communications in non-line-of-sight (NLoS) scenarios. The LCPM is operated in both the W band that covers 94 GHz and the D band that covers 140 GHz, corresponding to x-polarized and y-polarized wave incidence, respectively. Based on the proposed LCPM, realistic NLoS terahertz communication links are established and showcased. Communication measurements substantiate that the LCPM is capable of realizing extensive dynamic channel regulations and long-distance communications across both bands in various modulation schemes, supporting real-time high-speed video transmission. The experimental results validate the feasibility of employing the LCPM for terahertz wireless communications, paving the way for developing and implementing ubiquitous terahertz communication networks even with LoS blockage.